Publications

The scientific goal of ExaWind Exascale Computing Project (ECP) is to advance our fundamental understanding of the flow physics governing whole wind plant performance, including wake formation, complex terrain impacts, and turbine-turbine-interaction effects. Current methods for modeling wind plant performance fall short due to insufficient model fidelity and inadequate treatment of key phenomena, combined with a lack of computational power necessary to address the wide range of relevant length scales associated with wind plants. Thus, our ten-year exascale challenge is the predictive simulation of a wind plant composed of O(100) multi-MW wind turbines sited within a 100 km2 area with complex terrain, involving simulations with O(100) billion grid points. The project plan builds progressively from predictive petascale simulations of a single turbine, where the detailed blade geometry is resolved, meshes rotate and deform with blade motions, and atmospheric turbulence is realistically modeled, to a multi turbine array in complex terrain. The ALCC allocation will be used continually throughout the allocation period. In the first half of the allocation period, small (e.g., for testing Kokkos algorithms) and medium (e.g., 10K cores for highly resolved ABL simulations) sized jobs will be typical. In the second half of the allocation period, we will also have a number of large submittals for our resolved-turbine simulations. A challenge in the latter period is that small time step sizes will require long wall-clock times for statistically meaningful solutions. As such, we expect our allocation-hour burn rate to increase as we move through the allocation period.

Photonic Doppler velocimetry tracks motion during high-speed, single-event experiments using telecommunication fiber components. The same technology can be applied in situations where there is no actual motion, but rather a change in the optical path length. Migration of plasma into vacuum alters the refractive index near a fiber probe, while intense radiation modifies the refractive index of the fiber itself. Lastly, these changes can diagnose extreme environments in a flexible, time-resolved manner.

The capability, speed, size, and widespread availability of small unmanned aerial systems (sUAS) makes them a serious security concern. The enabling technologies for sUAS are rapidly evolving and so too are the threats they pose to national security. Potential threat vehicles have a small cross-section, and are difficult to reliably detect using purely ground-based systems (e.g. radar or electro-optical) and challenging to target using conventional anti-aircraft defenses. Ground-based sensors are static and suffer from interference with the earth, vegetation and other man-made structures which obscure objects at low altitudes. Because of these challenges, sUAS pose a unique and rapidly evolving threat to national security.

Conventional particle image velocimetry (PIV) configurations require a minimum of two optical access ports, inherently restricting the technique to a limited class of flows. Here, the development and application of a novel method of backscattered time-gated PIV requiring a single-optical-access port is described along with preliminary results. The light backscattered from a seeded flow is imaged over a narrow optical depth selected by an optical Kerr effect (OKE) time gate. The picosecond duration of the OKE time gate essentially replicates the width of the laser sheet of conventional PIV by limiting detected photons to a narrow time-of-flight within the flow. Thus, scattering noise from outside the measurement volume is eliminated. This PIV via the optical time-of-flight sectioning technique can be useful in systems with limited optical access and in flows near walls or other scattering surfaces.

Prior studies on the high-cycle fatigue behavior of nanocrystalline metals have shown that fatigue fracture is associated with abnormal grain growth (AGG). However, those previous studies have been unable to determine if AGG precedes fatigue crack initiation, or vice-versa. The present study shows that AGG indeed occurs prior to crack formation in nanocrystalline Ni-Fe by using a recently developed synchrotron X-ray diffraction modality that has been adapted for in-situ analysis. The technique allows fatigue tests to be interrupted at the initial signs of the AGG process, and subsequent microscopy reveals the precursor damage state preceding crack initiation.

Microstructure and phase evolution in magnetron sputtered nanocrystalline tungsten and tungsten alloy thin films are explored through in situ TEM annealing experiments at temperatures up to 1000 °C. Grain growth in unalloyed nanocrystalline tungsten transpires through a discontinuous process at temperatures up to 550 °C, which is coupled to an allotropic phase transformation of metastable β-tungsten with the A-15 cubic structure to stable body centered cubic (BCC) α-tungsten. Complete transformation to the BCC α-phase is accompanied by the convergence to a unimodal nanocrystalline structure at 650 °C, signaling a transition to continuous grain growth. Alloy films synthesized with compositions of W-20 at.% Ti and W-15 at.% Cr exhibit only the BCC α-phase in the as-deposited state, which indicate the addition of solute stabilizes the films against the formation of metastable β-tungsten. Thermal stability of the alloy films is significantly improved over their unalloyed counterpart up to 1000 °C, and grain coarsening occurs solely through a continuous growth process. The contrasting thermal stability between W-Ti and W-Cr is attributed to different grain boundary segregation states, thus demonstrating the critical role of grain boundary chemistry in the design of solute-stabilized nanocrystalline alloys.

We demonstrate inverse Hall-Petch behavior (softening) in pure copper sliding contacts at cryogenic temperatures. By kinetically limiting grain growth, it is possible to generate a quasi-stable ultra-nanocrystalline surface layer with reduced strength. In situ electrical contact resistance measurements were used to determine grain size evolution at the interface, in agreement with reports of softening in highly nanotwinned copper. We also show evidence of a direct correlation between surface grain size and friction coefficient, validating a model linking friction in pure metals and the transition from dislocation mediated plasticity to grain boundary sliding.

The growth dynamics and evolution of intrinsic stacking faults, lamellar, and double positioning twin grain boundaries were explored using molecular dynamics simulations during the growth of CdTe homoepitaxy and CdTe/CdS heteroepitaxy. Initial substrate structures were created containing either stacking fault or one type of twin grain boundary, and films were subsequently deposited to study the evolution of the underlying defect. Results show that during homoepitaxy the film growth was epitaxial and the substrate's defects propagated into the epilayer, except for the stacking fault case where the defect disappeared after the film thickness increased. In contrast, films grown on heteroepitaxy conditions formed misfit dislocations and grew with a small angle tilt (within ∼5°) of the underlying substrate's orientation to alleviate the lattice mismatch. Grain boundary proliferation was observed in the lamellar and double positioning twin cases. Our study indicates that it is possible to influence the propagation of high symmetry planar defects by selecting a suitable substrate defect configuration, thereby controlling the film defect morphology.

There has been a widespread interest in the preparation of self-assembled porphyrin nanostructures and their ordered arrays, aiming to emulate natural light harvesting processes and energy storage and to develop new nanostructured materials for photocatalytic process. Here, we report controlled synthesis of one-dimensional porphyrin nanostructures such as nanorods and nanowires with well-defined self-assembled porphyrin networks that enable efficient energy transfer for enhanced photocatalytic activity in hydrogen generation. Preparation of these one-dimensional nanostructures is conducted through noncovalent self-assembly of porphyrins confined within surfactant micelles. X-ray diffraction and transmission electron microscopy results reveal that these one-dimensional nanostructures contain stable single crystalline structures with controlled interplanar separation distance. Optical absorption characterizations show that the self-assembly enables effective optical coupling of porphyrins, resulting in much more enhanced optical absorption in comparison with the original porphyrin monomers, and the absorption bands red shift to more extensive visible light spectrum. The self-assembled porphyrin network facilitates efficient energy transfer among porphyrin molecules and the delocalization of excited state electrons for enhanced photocatalytic hydrogen production under visible light.

The high density of the extracellular matrix in solid tumors is an important obstacle to nanocarriers for reaching deep tumor regions and has severely limited the efficacy of administrated nanotherapeutics. The use of proteolytic enzymes prior to nanoparticle administration or directly attached to the nanocarrier surface has been proposed to enhance their penetration, but the low in vivo stability of these macromolecules compromises their efficacy and strongly limits their application. Herein, we have designed a multifunctional nanocarrier able to transport cytotoxic drugs to deep areas of solid tumors and once there, to be engulfed by tumoral cells causing their destruction. This system is based on mesoporous silica nanocarriers encapsulated within supported lipid bilayers (SLBs). The SLB avoids premature release of the housed drug while providing high colloidal stability and an easy to functionalize surface. The tumor penetration property is provided by attachment of engineered polymeric nanocapsules that transport and controllably unveil and release the proteolytic enzymes that in turn digest the extracellular matrix, facilitating the nanocarrier diffusion through the matrix. Additionally, targeting properties were endowed by conjugating an antibody specific to the investigated tumoral cells to enhance binding, internalization, and drug delivery. This multifunctional design improves the therapeutic efficacy of the transported drug as a consequence of its more homogeneous distribution throughout the tumoral tissue.

The practical implementation of many quantum technologies relies on the development of robust and bright single photon sources that operate at room temperature. The negatively charged silicon-vacancy (SiV−) color center in diamond is a possible candidate for such a single photon source. However, due to the high refraction index mismatch to air, color centers in diamond typically exhibit low photon out-coupling. An additional shortcoming is due to the random localization of native defects in the diamond sample. Here we demonstrate deterministic implantation of Si ions with high conversion e ciency to single SiV− centers, targeted to fabricated nanowires. The co-localization of single SiV− centers with the nanostructures yields a ten times higher light coupling e ciency than for single SiV− centers in bulk diamond. This enhanced photon out-coupling, together with the intrinsic scalability of the SiV− creation method, enables a new class of devices for integrated photonics and quantum science.

Lasing on the D1 transition (62P1/2 → 62S1/2) of cesium can be reached in both diode and excimer pumped alkali lasers. The first uses D2 transition (62S1/2 → 62P3/2) for pumping, whereas the second is pumped by photoexcitation of ground state Cs-Ar collisional pairs and subsequent dissociation of diatomic, electronically-excited CsAr molecules (excimers). Despite lasing on the same D1 transition, di erences in pumping schemes enables chemical pathways and characteristic timescales unique for each system. We investigate unavoidable plasma formation during operation of both systems side by side in Ar/C2H6/Cs.

Fluid-structure interactions were studies on a 7° half-angle cone in the Sandia Hypersonic Wind Tunnel at Mach 5 and 8 and in the Purdue Boeing/AFOSR Mach 6 Quiet Tunnel. A thin composite panel was integrated into the cone and the response to boundary-layer disturbances was characterized by accelerometers on the backside of the panel. Here, under quiet-flow conditions at Mach 6, the cone boundary layer remained laminar. Artificially generated turbulent spots excited a directionally dependent panel response which would last much longer than the spot duration.

The inverse problem of Kohn–Sham density functional theory (DFT) is often solved in an effort to benchmark and design approximate exchange-correlation potentials. The forward and inverse problems of DFT rely on the same equations but the numerical methods for solving each problem are substantially different. We examine both problems in this tutorial with a special emphasis on the algorithms and error analysis needed for solving the inverse problem. Two inversion methods based on partial differential equation constrained optimization and constrained variational ideas are introduced. We compare and contrast several different inversion methods applied to one-dimensional finite and periodic model systems.

Double-stranded DNA (dsDNA) binding and cleavage by Cas9 is a hallmark of type II CRISPR-Cas bacterial adaptive immunity. All known Cas9 enzymes are thought to recognize DNA exclusively as a natural substrate, providing protection against DNA phage and plasmids. Here, we show that Cas9 enzymes from both subtypes II-A and II-C can recognize and cleave single-stranded RNA (ssRNA) by an RNA-guided mechanism that is independent of a protospacer-adjacent motif (PAM) sequence in the target RNA. RNA-guided RNA cleavage is programmable and site-specific, and we find that this activity can be exploited to reduce infection by single-stranded RNA phage in vivo. We also demonstrate that Cas9 can direct PAM-independent repression of gene expression in bacteria. These results indicate that a subset of Cas9 enzymes have the ability to act on both DNA and RNA target sequences, and suggest the potential for use in programmable RNA targeting applications.

Multiple-color-emissive carbon dots (CDots) have potential applications in various fields such as bioimaging, light-emitting devices, and photocatalysis. The majority of the current CDots to date exhibit excitation-wavelength-dependent emissions with their maximum emission limited at the blue-light region. Here, a synthesis of multiple-color-emission CDots by controlled graphitization and surface function is reported. The CDots are synthesized through controlled thermal pyrolysis of citric acid and urea. By regulating the thermal-pyrolysis temperature and ratio of reactants, the maximum emission of the resulting CDots gradually shifts from blue to red light, covering the entire light spectrum. Specifically, the emission position of the CDots can be tuned from 430 to 630 nm through controlling the extent of graphitization and the amount of surface functional groups, COOH. The relative photoluminescence quantum yields of the CDots with blue, green, and red emission reach up to 52.6%, 35.1%, and 12.9%, respectively. Furthermore, it is demonstrated that the CDots can be uniformly dispersed into epoxy resins and be fabricated as transparent CDots/epoxy composites for multiple-color- and white-light-emitting devices. This research opens a door for developing low-cost CDots as alternative phosphors for light-emitting devices.

Current practice for commercial spent nuclear fuel management in the United States of America (US) includes storage of spent fuel in both pools and dry storage cask systems at nuclear power plants. Most storage pools are filled to their operational capacity, and management of the approximately 2,200 metric tons of spent fuel newly discharged each year requires transferring older and cooler fuel from pools into dry storage. In the absence of a repository that can accept spent fuel for permanent disposal, projections indicate that the US will have approximately 134,000 metric tons of spent fuel in dry storage by mid-century when the last plants in the current reactor fleet are decommissioned. Current designs for storage systems rely on large dual-purpose (storage and transportation) canisters that are not optimized for disposal. Various options exist in the US for improving integration of management practices across the entire back end of the nuclear fuel cycle.

The manufacturing tolerances of a stencil-lithography variant, membrane projection lithography, were investigated. In the first part of this work, electron beam lithography was used to create stencils with a range of linewidths. These patterns were transferred into the stencil membrane and used to pattern metallic lines on vertical silicon faces. Only the largest lines, with a nominal width of 84 nm, were resolved, resulting in 45 ± 10 nm (average ± standard deviation) as deposited with 135-nm spacing. Although written in the e-beam write software file as 84-nm in width, the lines exhibited linewidth bias. This can largely be attributed to nonvertical sidewalls inherent to dry etching techniques that cause proportionally larger impact with decreasing feature size. The line edge roughness can be significantly attributed to the grain structure of the aluminum nitride stencil membrane. In the second part of this work, the spatial uniformity of optically defined (as opposed to e-beam written) metamaterial structures over large areas was assessed. A Fourier transform infrared spectrometer microscope was used to collect the reflection spectra of samples with optically defined vertical split ring from 25 spatially resolved 300 × 300 μm regions in a 1-cm2 area. The technique is shown to provide a qualitative measure of the uniformity of the inclusions.

A unique experimental capability was developed so combined mechanical and thermal loads could be imposed on specimens that are representative of laser welded structures. The apparatus, instrumentation and specimens were designed concurrently to yield the ability to apply a wide range of loading conditions that accurately replicate the multiaxial stress states produced in laser welded, sealed structures during pressurization at high temperatures up to 800 °C. Axial, radial and torsional loads can be applied individually or in combination, by direct or variable loading paths, to eventual failure of laser weld specimens. Several advantages exist for applying equivalent stress states by mechanical means rather than pressurization with gas, including: repeatability, controlled failure, safe experiments, assessment of loading path dependence, experimental efficiency and overall facility. The experimental design and development are described along with resulting measurements and findings from sample experiments.

The failure progression of a fiber-reinforced toughened-matrix composite (IM7/8552) was experimentally characterized at quasi-static (10−4 s−1) strain rate using crossply and quasi-isotropic laminate specimens. A progressive failure framework was proposed to benchmark the initiation and progression of damage within composite laminates based on the matrix-dominated failure modes. The Northwestern Failure Theory (NU Theory) was used to provide a set of physics-based failure criteria for predicting the matrix-dominated failure of embedded plies using the lamina-based transverse tension, transverse compression, and shear failure strengths. The NU Theory was used to predict the first-ply-failure (FPF) of embedded plies in [0/904]s and [02/452/−452/902]s laminates for the embedded 90° and 45° plies. The Northwestern Criteria were found to provide superior prediction of the matrix-dominated embedded ply failure for all evaluated cases compared to the classical approaches. The results indicate the potential to use the Northwestern Criteria to provide the predictive baseline for damage propagation in composite laminates based on experimentally identified damage response on a length scale-relevant basis.

The strain-rate-dependent matrix-dominated failure of multiple fiber-reinforced polymer matrix composite systems was evaluated over the range of quasi-static (10−4) to dynamic (103 s−1) strain rates using available experimental data from literature. The strain rate dependent parameter, m, was found to relate strain-rate dependent lamina behavior linearly to the logarithm of strain rate. The parameter was characterized for a class of laminates comprised of epoxy-based matrices and either carbon or glass fibers, and determined to be approximately 0.055 regardless of fiber type. The strain-rate-dependent Northwestern Failure Criteria were found to fit all data in superior agreement to classical approaches across all strain rates evaluated based on solely lamina-level properties. It was determined that using the determined m value with the Northwestern Failure Criteria provided an accurate prediction of material behavior regardless of fiber type for the identified material class, which significantly reduces the material characterization testing required for the typical building block approach used by industry for computational analysis validation.

We have designed, built, and recorded data with a custom infrasound logger (referred to as the Gem) that is inexpensive, portable, and easy to use. We describe its design process, qualities, and applications in this article. Field instrumentation is a key element of geophysical data collection, and the quantity and quality of data that can be recorded is determined largely by the characteristics of the instruments used. Geophysicists tend to rely on commercially available instruments, which suffice for many important types of fieldwork. However, commercial instrumentation can fall short in certain roles, which motivates the development of custom sensors and data loggers. In particular, we found existing data loggers to be expensive and inconvenient for infrasound campaigns, and developed the Gem infrasound logger in response. In this article, we discuss development of this infrasound logger and the various uses found for it, including projects on volcanoes, high-Altitude balloons, and rivers. Further, we demonstrate that when needed, scientists can feasibly design and build their own specialized instruments, and that doing so can enable them to record more and better data at a lower cost.

We formulate, and present a numerical method for solving, an inverse problem for inferring parameters of a deterministic model from stochastic observational data on quantities of interest. The solution, given as a probability measure, is derived using a Bayesian updating approach for measurable maps that finds a posterior probability measure that when propagated through the deterministic model produces a push-forward measure that exactly matches the observed probability measure on the data. Our approach for finding such posterior measures, which we call consistent Bayesian inference or push-forward based inference, is simple and only requires the computation of the push-forward probability measure induced by the combination of a prior probability measure and the deterministic model. We establish existence and uniqueness of observation-consistent posteriors and present both stability and error analyses. We also discuss the relationships between consistent Bayesian inference, classical/statistical Bayesian inference, and a recently developed measure-theoretic approach for inference. Finally, analytical and numerical results are presented to highlight certain properties of the consistent Bayesian approach and the differences between this approach and the two aforementioned alternatives for inference.

The measurement of photon dose in pure gamma-ray and mixed (neutron/ gamma) field environments relies heavily on calibration of thermoluminescent dosimeters (TLDs) in cobalt-60 (Co-60) gamma irradiation environments. One of the principal means of reducing the gamma dose measurement uncertainty in Sandia National Laboratories' reactor environments is careful calibration of the CaF2:Mn TLDs used in the test environment. One issue that arises is that Co-60 gamma fields used for calibration universally have a low energy photon component. The scattered photons that make up the low energy photon component are a principal source of measurement error for the TLD calibration. ASTM E1249, Standard Practice for Minimizing Dosimetry Errors in Radiation Hardness Testing of Silicon Electronic Devices Using Co-60 Sources, describes a method that utilizes photon spectrum filter boxes to enclose devices under test that can reduce the measurement error during TLD calibration as well as during normal radiation testing of electronic components in the gamma field. Using a silicon sensor representative of a CMOS-7 technology, a series of calculations was performed for single-layer, two-layer, and three-layer filters to identify a filter box that improves the silicon dose-to-kerma ratio (that is, the filter reduces the low energy photon component in the Co-60 radiation field) in the sensor over the current filter box design. The results of the parameter study in this paper will be used to plan experimental studies in the Co-60 gamma fields used for calibration.

The measurement of photon dose in pure gamma-ray and mixed (neutron/ gamma) field environments relies heavily on calibration of thermoluminescent dosimeters (TLDs) in cobalt-60 (Co-60) gamma irradiation environments. One of the principal means of reducing the gamma dose measurement uncertainty in Sandia National Laboratories' reactor environments is careful calibration of the CaF2:Mn TLDs used in the test environment. One issue that arises is that Co-60 gamma fields used for calibration universally have a low energy photon component. The scattered photons that make up the low energy photon component are a principal source of measurement error for the TLD calibration. ASTM E1249, Standard Practice for Minimizing Dosimetry Errors in Radiation Hardness Testing of Silicon Electronic Devices Using Co-60 Sources, describes a method that utilizes photon spectrum filter boxes to enclose devices under test that can reduce the measurement error during TLD calibration as well as during normal radiation testing of electronic components in the gamma field. Using a silicon sensor representative of a CMOS-7 technology, a series of calculations was performed for single-layer, two-layer, and three-layer filters to identify a filter box that improves the silicon dose-to-kerma ratio (that is, the filter reduces the low energy photon component in the Co-60 radiation field) in the sensor over the current filter box design. The results of the parameter study in this paper will be used to plan experimental studies in the Co-60 gamma fields used for calibration.

The reports of 35 J/cc energy density in thinned alkali-free glasses make it a top candidate for next generation high energy density capacitors. In this article, we demonstrate a scalable process to take currently available commercial glass and fabricate fully packaged capacitors. These prototypes have 0.086 J/cc energy density at 1000 V, making them competitive with some commercially available ceramic capacitors. This was achieved while focusing on developing a process for thinning and handling the glass and without minimization of the inactive volume of the capacitor. These results portend the achievement of significantly higher energy densities in devices made from alkali-free glass.

The Annular Core Research Reactor (ACRR) at Sandia National Laboratories provides experimenters with a unique platform for irradiations. Its central cavity is wide enough to accommodate spectrum-modifying materials, commonly referred to as buckets. The addition of hydrogenous moderators, such as polyethylene or water, can cause considerable thermalization of the free field neutron spectrum. Conversely, thick annular regions of strong, thermal absorbers, such as boron or cadmium, create a faster neutron spectrum inside. Similarly, the gamma-ray fluence can be attenuated by adding high-Z materials or enhanced through radiative capture in cadmium or gadolinium. Novel configurations of buckets allow simultaneous neutron energy spectrum modification and gamma-ray attenuation. As such, different radiation environments can exist at ACRR's core centerline. Recent efforts have produced detailed characterizations of several neutron- and gamma-ray spectrum-modifying buckets for the ACRR central cavity, including: the free field; the 44-in.-tall lead-boron carbide bucket (fast neutron, attenuated photon); the polyethylene-lead-graphite bucket (thermalized neutrons, attenuated photon); and the Cd-Poly bucket (cadmium polyethylene lined bucket used to enhance photon production). Dedicated opportunities to perform multiple characterizations occurred somewhat infrequently, which afforded the authors the ability to hone techniques for performing these tests. Each neutron spectrum characterization generally followed both ASTM E720, Standard Guide for Selection and Use of Neutron Sensors for Determining Neutron Spectra Employed in Radiation-Hardness Testing of Electronics, and ASTM E721, Standard Guide for Determining Neutron Energy Spectra from Neutron Sensors for Radiation-Hardness Testing of Electronics. This paper presents some practical lessons learned throughout these characterizations-both experimental and computational.

Silicone foams and rubber are used in a variety of applications to protect internal components from external shock impact. Understanding how these materials mitigate impact energy is a crucial step in designing more effective shock isolation systems for components. In this study, a Kolsky bar with pre-compression and passive radial confinement capabilities was used to investigate the response of silicone foams and rubber subjected to impact loading at different speeds. Using the preload capability, silicone foam samples were subjected to increasing levels of pre-strain. Frequency-based analyses were carried out on results from silicone foams and rubber to study the effect of both pre-strain and material processing conditions on the mechanism of energy dissipation in the frequency domain. Additionally, effects of impact speed on energy dissipation through silicone foams and rubber were investigated.

Independent meshing of subdomains separated by an interface can lead to spatially non-coincident discrete interfaces. We present an optimization-based coupling method for such problems, which does not require a common mesh refinement of the interface, has optimal H1 convergence rates, and passes a patch test. The method minimizes the mismatch of the state and normal stress extensions on discrete interfaces subject to the subdomain equations, while interface “fluxes” provide virtual Neumann controls.

Maintaining the performance of high-performance computing (HPC) applications with the expected increase in failures is a major challenge for next-generation extreme-scale systems. With increasing scale, resilience activities (e.g. checkpointing) are expected to become more diverse, less tightly synchronized, and more computationally intensive. Few existing studies, however, have examined how decisions about scheduling resilience activities impact application performance. In this work, we examine the relationship between the duration and frequency of resilience activities and application performance. Our study reveals several key findings: (i) the aggregate amount of time consumed by resilience activities is not an effective metric for predicting application performance; (ii) the duration of the interruptions due to resilience activities has the greatest influence on application performance; shorter, but more frequent, interruptions are correlated with better application performance; and (iii) the differential impact of resilience activities across applications is related to the applications’ inter-collective frequencies; the performance of applications that perform infrequent collective operations scales better in the presence of resilience activities than the performance of applications that perform more frequent collective operations. This initial study demonstrates the importance of considering how resilience activities are scheduled. We provide critical analysis and direct guidance on how the resilience challenges of future systems can be met while minimizing the impact on application performance.

Fluctuating boundary layer pressure fluctuations are an important loading component for high speed reentry vehicles. Characterization of the unsteady time series requires access to longitudinal and lateral coherence expressions as well spatial correlation and frequency power-spectral density models. Coherence, spatial correlation and frequency power spectral density are related as through their cross-spectral density definitions. However the frequency PSD and the spatial correlation are often based upon measurements or approximate models which may introduce bias in the associated derived coherence function. Here, we examine the effect of measurement and model form associated with frequency spectrum and correlation on the longitudinal and lateral coherence for supersonic pressure fluctuation flow fields. The widely utilized Corcos separable coherence model functional form has been employed in this study. The associated integral equations which relate coherence and correlation are solved using a simple iterative approach. To minimize distortion in results due to computational issues a high accuracy numerical integration procedure is utilized. Despite a more robust computational approach, solution accuracy is limited for some problems by the functional form of the longitudinal coherence model. These limitations are discussed in detail. This overall approach is applied to Mach 5 and Mach 8 seven degree sharp cone pressure fluctuation measurements. Estimates for the parameters associated with the Corcos coherence expressions are typically larger than more traditional values especially for the longitudinal coherence. These larger values suggest that fluctuations streamwise correlation length is small. Limited longitudinal correlation can be associated with shock influence and is explored as a possible cause.

Partial penetration laser welds join metal surfaces without additional filler material, providing hermetic seals for a variety of components. The crack-like geometry of a partial penetration weld is a local stress riser that may lead to failure of the component in the weld. Computational modeling of laser welds has shown that the model should include damage evolution to predict the large deformation and failure. We have performed interrupted tensile experiments both to characterize the damage evolution and failure in laser welds and to aid computational modeling of these welds. Several EDM-notched and laser-welded 304L stainless steel tensile coupons were pulled in tension, each one to a different load level, and then sectioned and imaged to show the evolution of damage in the laser weld and in the EDM-notched parent 304L material (having a similar geometry to the partial penetration laser-welded material). SEM imaging of these specimens revealed considerable cracking at the root of the laser welds and some visible micro-cracking in the root of the EDM notch even before peak load was achieved in these specimens. The images also showed deformation-induced damage in the root of the notch and laser weld prior to the appearance of the main crack, though the laser-welded specimens tended to have more extensive damage than the notched material. These experiments show that the local geometry alone is not the cause of the damage, but also microstructure of the laser weld, which requires additional investigation.

In this work we present a computational capability featuring a hierarchy of models with different fidelities for the solution of electrokinetics problems at the micro-/nano-scale. A multifidelity approach allows the selection of the most appropriate model, in terms of accuracy and computational cost, for the particular application at hand. We demonstrate the proposed multifidelity approach by studying the mobility of a colloid in a micro-channel as a function of the colloid charge and of the size of the ions dissolved in the fluid.

Fiber reinforced polymer composites are frequently used in hybrid structures where they are co-cured or co-bonded to dissimilar materials. For autoclave cured composites, this interface typically forms at an elevated temperature that can be quite different from the part’s service temperature. As a result, matrix shrinkage and CTE mismatch can produce significant residual stresses at this bi-material interface. This study shows that the measured critical strain energy release rate, Gc, can be quite sensitive to the residual stress state of this interface. If designers do not properly account for the effect of these process induced stresses, there is danger of a nonconservative design. Tests including double cantilever beam (DCB) and end notched flexure (ENF) were conducted on a co-cured GFRP-CFRP composite panel across a wide range of temperatures. These results are compared to tests performed on monolithic GFRP and CFRP panels.

This letter presents a new frequency control strategy that takes advantage of communications and fast responding resources such as photovoltaic generation, energy storage, wind generation, and demand response, termed collectively as converter interfaced generators (CIGs). The proposed approach uses an active monitoring of power imbalances to rapidly redispatch CIGs. This approach differs from previously proposed frequency control schemes in that it employs feed-forward control based on a measured power imbalance rather than relying on a frequency measurement. Time-domain simulations of the full Western Electricity Coordinating Council system are conducted to demonstrate the effectiveness of the proposed method, showing improved performance.

The collapse or merging of individual plumes of direct-injection gasoline injectors is of fundamental importance to engine performance because of its impact on fuel-air mixing. However, the mechanisms of spray collapse are not fully understood and are difficult to predict. The purpose of this work is to study the aerodynamics in the inter-spray region, which can potentially lead to plume collapse. High-speed (100 kHz) particle image velocimetry is applied along a plane between plumes to observe the full temporal evolution of plume interaction and potential collapse, resolved for individual injection events. Supporting information along a line of sight is obtained using simultaneous diffused back illumination and Mie-scatter techniques. Experiments are performed under simulated engine conditions using a symmetric eight-hole injector in a high-temperature, high-pressure vessel at the “Spray G” operating conditions of the engine combustion network. Indicators of plume interaction and collapse include changes in counter-flow recirculation of ambient gas toward the injector along the axis of the injector or in the inter-plume region between plumes. The effect of ambient temperature and gas density on the inter-plume aerodynamics and the subsequent plume collapse are assessed. Increasing ambient temperature or density, with enhanced vaporization and momentum exchange, accelerates the plume interaction. Plume direction progressively shifts toward the injector axis with time, demonstrating that the plume interaction and collapse are inherently transient.

Fiber reinforced polymer composites are frequently used in hybrid structures where they are co-cured or co-bonded to dissimilar materials. For autoclave cured composites, this interface typically forms at an elevated temperature that can be quite different from the part’s service temperature. As a result, matrix shrinkage and CTE mismatch can produce significant residual stresses at this bi-material interface. This study shows that the measured critical strain energy release rate, Gc, can be quite sensitive to the residual stress state of this interface. If designers do not properly account for the effect of these process induced stresses, there is danger of a nonconservative design. Tests including double cantilever beam (DCB) and end notched flexure (ENF) were conducted on a co-cured GFRP-CFRP composite panel across a wide range of temperatures. These results are compared to tests performed on monolithic GFRP and CFRP panels.

Radionuclides and heavy metals easily sorb onto colloids. This phenomenon can have a beneficial impact on environmental clean-up activities if one is trying to scavenge hazardous elements from soil for example. On the other hand, it can have a negative impact in cases where one is trying to immobilize these hazardous elements and keep them isolated from the public. Such is the case in the field of radioactive waste disposal. Colloids in the aqueous phase in a radioactive waste repository could facilitate transport of contaminants including radioactive nuclides. Salt formations have been recommended for nuclear waste isolation since the 1950's by the U.S. National Academy of Science. In this capacity, salt formations are ideal for isolation of radioactive waste. However, salt formations contain brine (the aqueous phase), and colloids could possibly be present. If present in the brines associated with salt formations, colloids are highly relevant to the isolation safety concept for radioactive waste. The Waste Isolation Pilot Plant (WIPP) in southeast New Mexico is a premier example where a salt formation is being used as the primary isolation barrier for radioactive waste. WIPP is a U.S. Department of Energy geological repository for the permanent disposal of defenserelated transuranic (TRU) waste. In addition to the geological barrier that the bedded salt formation provides, an engineered barrier of MgO added to the disposal rooms is used in WIPP. Industrial-grade MgO, consisting mainly of the mineral periclase, is in fact the only engineered barrier certified by the U.S. Environmental Protection Agency (EPA) for emplacement in the WIPP. Of interest, an Mg(OH)2-based engineered barrier consisting mainly of the mineral brucite is to be employed in the Asse repository in Germany. The Asse repository is located in a domal salt formation and is another example of using salt formations for disposal of radioactive waste. Should colloids be present in salt formations, they would facilitate transport of contaminants including actinides. In the case of colloids derived from emplaced MgO, it is the hydration and carbonation products that are of interest. These colloids could possibly form under conditions relevant in particular to the WIPP. In this chapter, we report a systematic experimental study performed at Sandia National Laboratories in Carlsbad, New Mexico, related to the WIPP engineered barrier, MgO. The aim of this work is to confirm the presence or absence of mineral fragment colloids related to MgO in high ionic strength solutions (brines). The results from such a study provides information about the stability of colloids in high ionic strength solutions in general, not just for the WIPP. We evaluated the possible formation of mineral fragment colloids using two approaches. The first approach is an analysis of long-term MgO hydration and carbonation experiments performed at Sandia National Laboratories (SNL) as a function of equivalent pore sizes. The MgO hydration products include Mg(OH)2 (brucite) and Mg3 Cl(OH)5•4H2O (phase 5), and the carbonation product includes Mg5(CO3)4(OH)2•4H2O (hydromagnesite). All these phases contain magnesium. Therefore, if mineral fragment colloids of these hydration and carbonation products were formed in the SNL experiments mentioned above, magnesium concentrations in the filtrate from the experiments would show a dependence on ultrafiltration. In other words, there would be a decrease in magnesium concentrations as a function of ultrafiltration with decreasing molecular weight (MW) cut-offs for the filtration. Therefore, we performed ultrafiltration on solution samples from the SNL hydration and carbonation experiments as a function of equivalent pore size. We filtered solutions using a series of MW cut-off filters at 100 kD, 50 kD, 30 kD and 10 kD. Our results demonstrate that the magnesium concentrations remain constant with decreasing MW cutoffs, implying the absence of mineral fragment colloids. The second approach uses spiked Cs+ to indicate the possible presence of mineral fragment colloids. Cs+ is easily absorbed by colloids. Therefore, we added Cs+ to a subset of SNL MgO hydration and carbonation experiments. Again, we filtered the solutions with a series of MW cut-off filters at 100 kD, 50 kD, 30 kD and 10 kD. This time we measured the concentrations of Cs. The concentrations of Cs do not change as a function of MW cut-offs, indicating the absence of colloids from MgO hydration and carbonation products. Therefore, both approaches demonstrate the absence of mineral fragment colloids from MgO hydration and carbonation products. Based on our experimental results, we acknowledge that mineral fragment colloids were not formed in the SNL MgO hydration and carbonation experiments, and we further conclude that high ionic strength solutions associated with salt formations prevent the formation of mineral fragment colloids. This is due to the fact that the high ionic strength solutions associated with salt formations have high concentrations of both monovalent and divalent metal ions that are orders of magnitude higher than the critical coagulation concentrations for mineral fragment colloids. The absence of mineral fragment colloids in high ionic strength solutions implies that contributions from mineral fragment colloids to the total mobile source term of radionuclides in a salt repository are minimal.

As device dimensions decrease, single displacement effects become more important. We measured the gain degradation in III-V heterojunction bipolar transistors due to single particles using a heavy ion microbeam. Two devices with different sizes were irradiated with various ion species ranging from oxygen to gold to study the effect of the irradiation ion mass on gain change. From the single steps in the inverse gain (which is proportional to the number of defects), we calculated cumulative distribution functions to help determine design margins. The displacement process was modeled using the MARLOWE binary collision approximation code. The entire structure of the device was modeled and the defects in the base-emitter junction were counted to be compared with the experimental results. While we found good agreement for the large device, we had to modify our model to reach reasonable agreement for the small device.

A bipolar-transistor-based sensor technique has been used to compare silicon displacement damage from known and unknown neutron energy spectra generated in nuclear reactor and high-energy-density physics environments. The technique has been shown to yield 1-MeV(Si) equivalent neutron fluence measurements comparable to traditional neutron activation dosimetry. This paper significantly extends previous results by evaluating three types of bipolar devices utilized as displacement damage sensors at a nuclear research reactor and at a Pelletron particle accelerator. Ionizing dose effects are compensated for via comparisons with 10-keV X-ray and/or cobalt-60 gamma ray irradiations. Nonionizing energy loss calculations adequately approximate the correlations between particle and device responses and provide evidence for the use of one particle type to screen the sensitivity of the other.

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertainparameters involvedandthe high computational costofflow simulations. These difficulties are addressedin this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying themin the current studyto large-eddy simulations ofajet incrossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the system's stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. These methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.

The crystal structure, lattice dynamics and themomechanical properties of bulk monoclinic zirconium tetrachloride (ZrCl4) have been investigated using zero-damping dispersion-corrected density functional theory [DFT-D3(zero)]. Phonon analysis reveals that ZrCl4(cr) undergoes negative thermal expansion (NTE) near T≈10 K, with a coefficient of thermal expansion of α=-1.2 ppm K−1 and a Grüneisen parameter of γ=-1.1. The bulk modulus is predicted to vary from K0=8.7 to 7.0 GPa in the temperature range 0–550 K. The isobaric molar heat capacity derived from phonon calculations within the quasi-harmonic approximation is in fair agreement with existing calorimetric data.

Abstract not provided.

This work presents two computational efficiency improvements for the hybridizable discontinuous Galerkin (HDG) fluid-structure interaction (FSI) model presented by Sheldon et al. A new formulation for the solid is presented that eliminates the global displacement, resulting in the velocity being the only global solid variable. This necessitates a change to the solid-mesh displacement coupling, which is accounted for by coupling the local solid displacement to the global mesh displacement. Additionally, the mesh basis and test functions are restricted to linear polynomials, rather than being equal-order with the fluid and solid. This change increases the computational efficiency dynamically, with greater benefit the higher order the computation, when compared to an equal-order formulation. These two improvements result in a 50% reduction in the number of global degrees of freedom for high-order simulations for both the fluid and solid domains, as well as an approximately 50% reduction in the number of local fluid domain degrees of freedom for high-order simulations. The new, more efficient formulation is compared against that from Sheldon et al. and negligible change of accuracy is found.

Abstract not provided.

Abstract not provided.

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress towards optimal engine designs requires accurate and computationally affordable flow simulations, as well as uncertainty quantification (UQ). While traditional UQ techniques can become prohibitive under expensive simulations and high-dimensional parameter spaces, polynomial chaos (PC) surrogate modeling is a useful tool for alleviating some of the computational burden. However, non-intrusive quadrature-based constructions of PC expansions relying on a single high-fidelity model can still be quite expensive. We thus introduce a two-stage numerical procedure for constructing PC surrogates while making use of multiple models of different fidelity. The first stage involves an initial dimension reduction through global sensitivity analysis using compressive sensing. The second stage utilizes adaptive sparse quadrature on a multifidelity expansion to compute PC surrogate coefficients in the reduced parameter space where quadrature methods can be more effective. The overall method is used to produce accurate surrogates and to propagate uncertainty induced by uncertain boundary conditions and turbulence model parameters, for performance quantities of interest from large eddy simulations of supersonic reactive flows inside a scramjet engine.

As we develop new materials to increase performance of lithium ion batteries for electric vehicles, the impact of potential safety and reliability issues become increasingly important. In addition to electrochemical performance increases (capacity, energy, cycle life, etc.), there are a variety of materials advancements that can be made to improve lithium-ion battery safety. Issues including energetic thermal runaway, electrolyte decomposition and flammability, anode SEI stability, and cell-level abuse tolerance behavior. Introduction of a next generation materials, such as silicon based anode, requires a full understanding of the abuse response and degradation mechanisms for these anodes. This work aims to understand the breakdown of these materials during abuse conditions in order to develop an inherently safe power source for our next generation electric vehicles. The effect of materials level changes (electrolytes, additives, silicon particle size, silicon loading, etc.) to cell level abuse response and runaway reactions will be determined using several techniques. Experimentation will start with base material evaluations in coin cells and overall runaway energy will be evaluated using techniques such as differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and accelerating rate calorimetry (ARC). The goal is to understand the effect of materials parameters on the runaway reactions, which can then be correlated to the response seen on larger cells (18650). Experiments conducted showed that there was significant response from these electrodes. Efforts to minimize risk during testing were taken by development of a smaller capacity cylindrical design in order to quantify materials decision and how they manifest during abuse response.

Within the SEQUOIA project, funded by the DARPA EQUiPS program, we pursue algorithmic approaches that enable comprehensive design under uncertainty, through inclusion of aleatory/parametric and epistemic/model form uncertainties within scalable forward/inverse UQ approaches. These statistical methods are embedded within design processes that manage computational expense through active subspace, multilevel-multifidelity, and reduced-order modeling approximations. To demonstrate these methods, we focus on the design of devices that involve multi-physics interactions in advanced aerospace vehicles. A particular problem of interest is the shape design of nozzles for advanced vehicles such as the Northrop Grumman UCAS X-47B, involving coupled aero-structural-thermal simulations for nozzle performance. In this paper, we explore a combination of multilevel and multifidelity forward and inverse UQ algorithms to reduce the overall computational cost of the analysis by leveraging hierarchies of model form (i.e., multifidelity hierarchies) and solution discretization (i.e., multilevel hierarchies) in order of exploit trade offs between solution accuracy and cost. In particular, we seek the most cost effective fusion of information across complex multi-dimensional modeling hierarchies. Results to date indicate the utility of multiple approaches, including methods that optimally allocate resources when estimator variance varies smoothly across levels, methods that allocate sufficient sampling density based on sparsity estimates, and methods that employ greedy multilevel refinement.

In this paper, we report the formulation to account for dielectrics in a first principles multipole-based cable braid electromagnetic penetration model. To validate our first principles model, we consider a one-dimensional array of wires, which can be modeled analytically with a multipole-conformal mapping expansion for the wire charges; however, the first principles model can be readily applied to realistic cable geometries. We compare the elastance (i.e., the inverse of the capacitance) results from the first principles cable braid electromagnetic penetration model to those obtained using the analytical model. The results are found in good agreement up to a radius to half spacing ratio of 0.5–0.6, depending on the permittivity of the dielectric used, within the characteristics of many commercial cables. We observe that for typical relative permittivities encountered in braided cables, the transfer elastance values are essentially the same as those of free space; the self-elastance values are also approximated by the free space solution as long as the dielectric discontinuity is taken into account for the planar mode.

Torque feedback control and series elastic actuators are widely used to enable compact, highly-geared electric motors to provide low and controllable mechanical impedance. While these approaches provide certain benefits for control, their impact on system energy consumption is not widely understood. This paper presents a model for examining the energy consumption of drivetrains implementing various target dynamic behaviors in the presence of gear reductions and torque feedback. Analysis of this model reveals that under cyclical motions for many conditions, increasing the gear ratio results in greater energy loss. A similar model is presented for series elastic actuators and used to determine the energy consequences of various spring stiffness values. Both models enable the computation and optimization of power based on specific hardware manifestations, and illustrate how energy consumption sometimes defies conventional best-practices. Results of evaluating these two topologies as part of a drivetrain design optimization for two energy-efficient electrically driven humanoids are summarized. The model presented enables robot designers to predict the energy consequences of gearing and series elasticity for future robot designs, helping to avoid substantial energy sinks that may be inadvertently introduced if these issues are not properly analyzed.

Amphiphilic Janus particles demonstrate unique assembly structures when dried on a hydrophilic substrate. Particle orientations are influenced by amphiphilicity and Janus balance. A three-stage model is developed to describe the process. Simulation further indicates the dominant force is capillary attraction due to the interface pinning at rough Janus boundaries.

A pair of thallium salen derivatives was synthesized and characterized for potential use as monitors (or taggants) or as models for Group 13 complexes for subterranean fluid flows. These precursors were isolated from the reaction of thallium ethoxide with N,N′-bis(3,5-di-tert-butylsalicylidene)-ethylenediamine (H2-salo-But), or N,N′-bis(3,5-di-tert-butylsalicylidene)-1,2-phenylenediamine (H2-saloPh-But). The products were identified by single crystal X-ray diffraction as: [((μ-O)2,κ1-(N)(N′)salo-But)Tl2] (1) and {[((μ-O)2saloPh-But)Tl2][((μ-O)2,κ1-(N)(N′)saloPh-But)Tl2]} (2). Both structures are similar, wherein each O atom of the salo moiety bridges the two Tl atoms, leading to a Tl⋯Tl interaction, which is further stabilized by an intramolecular π-bond with neighboring phenyl rings. For 1, an additional Tl⋯N interaction was solved for each metal center; whereas, for 2, one of the two molecules in the matrix has a weak Tl⋯N interaction but no bonding noted in the other molecule. Both Density Functional Theory (DFT) calculations and variable temperature solution 205Tl NMR studies of 1 and 2 further confirmed the Tl⋯Tl interaction. The UV-vis absorbance spectra of these compounds had an absorbance peak at 392 nm for 1 and a broad absorbance peak centered at 469 nm for 2, which were found to be in good agreement with the DFT calculated spectra that were dominated by the singlet state. Fluorescence emission and excitation studies reveal absorptions at 360 and 380 nm for 1 and 2, respectively, which are attributed to the Tl⋯Tl metal centers. To demonstrate practicality, fluorescence spectra of 1 and 2 were obtained using a handheld 405 nm cw laser pointer and portable spectrometer where compound 1 was found to glow 15 times brighter than compound 2. Only compound 1 was found to survive the simulated deep-well conditions explored, which was attributed to the Tl⋯N interaction noted for 1 but not for 2.

Diesel piston bowl geometry can affect turbulent mixing and therefore it impacts heat-release rates, thermal efficiency, and soot emissions. The focus of this work is on the effects of bowl geometry and injection timing on turbulent flow structure. This computational study compares engine behavior with two pistons representing competing approaches to combustion chamber design: a conventional, re-entrant piston bowl and a stepped-lip piston bowl. Three-dimensional computational fluid dynamics (CFD) simulations are performed for a part-load, conventional diesel combustion operating point with a pilot-main injection strategy under non-combusting conditions. Two injection timings are simulated based on experimental findings: an injection timing for which the stepped-lip piston enables significant efficiency and emissions benefits, and an injection timing with diminished benefits compared to the conventional, re-entrant piston. While the flow structure in the conventional, re-entrant combustion chamber is dominated by a single toroidal vortex, the turbulent flow evolution in the stepped-lip combustion chamber depends more strongly on main injection timing. For the injection timing at which faster mixing controlled heat release and reduced soot emissions have been observed experimentally, the simulation predicts the formation of two additional recirculation zones created by interactions with the stepped-lip. Analysis of the CFD results reveals the mechanisms responsible for these recirculating flow structures. Vertical convection of outward radial momentum drives the formation of the recirculation zone in the squish region, while adverse pressure gradients drive flow inward near the cylinder head, thereby contributing to the formation of the second recirculation zone above the step. Bulk gas density is higher for the near-TDC injection timing than for the later injection timing. This leads to increased air entrainment into the sprays and slower spray velocities, so the sprays take longer to interact with the step, and beneficial recirculating flow structures are not obseved.

This open access book examines how the social sciences can be integrated into the praxis of engineering and science, presenting unique perspectives on the interplay between engineering and social science. Motivated by the report by the Commission on Humanities and Social Sciences of the American Association of Arts and Sciences, which emphasizes the importance of social sciences and Humanities in technical fields, the essays and papers collected in this book were presented at the NSF-funded workshop ‘Engineering a Better Future: Interplay between Engineering, Social Sciences and Innovation’, which brought together a singular collection of people, topics and disciplines. The book is split into three parts: A. Meeting at the Middle: Challenges to educating at the boundaries covers experiments in combining engineering education and the social sciences; B. Engineers Shaping Human Affairs: Investigating the interaction between social sciences and engineering, including the cult of innovation, politics of engineering, engineering design and future of societies; and C. Engineering the Engineers: Investigates thinking about design with papers on the art and science of science and engineering practice.

Dynamic probabilistic risk assessment (DPRA) methodologies and the dynamic event tree (DET) methodology specifically allow traditional PRA to be complemented by insights into time-dependent behavior. The ADAPT DET driver has been enhanced recently to provide greater capability to generate DETs and analyze results. The functions that ADAPT uses to gather and present output data have been standardized and enhanced with the goal of automating as much of the process as feasible while remaining simulator and technology agnostic. These individual enhancements come together to reduce the burden on the analyst and allow insights to be discovered more quickly. A recent goal has been the use of ADAPT on high performance computing (HPC) platforms. The number and granularity of treatment of uncertain parameters in a DET may lead to a state space explosion unless DETs are truncated (e.g., using a probability threshold) which may make the complete DET to be infeasible to run on local machines or small computer clusters. Progress can be greatly accelerated by using the large capacity of HPCs. A scheme is presented by which ADAPT gains the capability to distribute jobs to an HPC and retrieve the results seamlessly alongside other types of computation hosts.

The use of exhaust gas recirculation (EGR) in spark ignition engines has been shown to have a number of beneficial effects under specific operating conditions. These include reducing pumping work under part load conditions, reducing NOx emissions and heat losses by lowering peak combustion temperatures, and by reducing the tendency for engine knock (caused by end-gas autoignition) under certain operating regimes. In this study, the effects of EGR addition on knocking combustion are investigated through a combined experimental and modeling approach. The problem is investigated by considering the effects of individual EGR constituents, such as CO2, N2, and H2O, on knock, both individually and combined, and with and without traces species, such as unburned hydrocarbons and NOx. The effects of engine compression ratio and fuel composition on the effectiveness of knock suppression with EGR addition were also investigated. A parametric, experimental matrix of diluents, compression ratio, and fuels was tested to measure knock-limited combustion phasing of each combination. The resulting knock limits were evaluated in the context of thermodynamic effects on the closed cycle, chemical interactions between the EGR constituents and the fuel-oxidizer mixture, and the effect of altered pressure-temperature trajectories on fuel-autoignition behavior. This paper provides an overview of the experimental results, and uses chemical-kinetic modeling to investigate the behavior of a particular fuel - diluent combination which had a strong sensitivity to compression ratio variation. The numerical results shed light on the complex interactions between fuel chemistry, the engine's thermodynamic cycle, and the effect of residence times on the autoignition chemistry which leads to knock. An important and fuel-dependent role of thermal stratification in the end-gas is also suggested by the chemical-kinetics modeling of the experimentally observed knock limits.

Gene therapy has long held promise to correct a variety of human diseases and defects. Discovery of the Clustered Regularly-Interspaced Short Palindromic Repeats (CRISPR), the mechanism of the CRISPRbased prokaryotic adaptive immune system (CRISPR-associated system, Cas), and its repurposing into a potent gene editing tool has revolutionized the field of molecular biology and generated excitement for new and improved gene therapies. Additionally, the simplicity and flexibility of the CRISPR/Cas9 site-specific nuclease system has led to its widespread use in many biological research areas including development of model cell lines, discovering mechanisms of disease, identifying disease targets, development of transgene animals and plants, and transcriptional modulation. In this review, we present the brief history and basic mechanisms of the CRISPR/Cas9 system and its predecessors (ZFNs and TALENs), lessons learned from past human gene therapy efforts, and recent modifications of CRISPR/ Cas9 to provide functions beyond gene editing. We introduce several factors that influence CRISPR/ Cas9 efficacy which must be addressed before effective in vivo human gene therapy can be realized. The focus then turns to the most difficult barrier to potential in vivo use of CRISPR/Cas9, delivery. We detail the various cargos and delivery vehicles reported for CRISPR/Cas9, including physical delivery methods (e.g. microinjection; electroporation), viral delivery methods (e.g. adeno-associated virus (AAV); full-sized adenovirus and lentivirus), and non-viral delivery methods (e.g. liposomes; polyplexes; gold particles), and discuss their relative merits. We also examine several technologies that, while not currently reported for CRISPR/Cas9 delivery, appear to have promise in this field. The therapeutic potential of CRISPR/Cas9 is vast and will only increase as the technology and its delivery improves.

In this study, we experimentally investigate the high-temperature oxidation kinetics of n-pentane, 1-pentene and 2-methyl-2-butene (2M2B) in a combustion environment using flame-sampling molecular beam mass spectrometry. The selected C5 fuels are prototypes for linear and branched, saturated and unsaturated fuel components, featuring different C-C and C-H bond structures. It is shown that the formation tendency of species, such as polycyclic aromatic hydrocarbons (PAHs), yielded through mass growth reactions increases drastically in the sequence n-pentane < 1-pentene < 2M2B. This comparative study enables valuable insights into fuel-dependent reaction sequences of the gas-phase combustion mechanism that provide explanations for the observed difference in the PAH formation tendency. First, we investigate the fuel-structure-dependent formation of small hydrocarbon species that are yielded as intermediate species during the fuel decomposition, because these species are at the origin of the subsequent mass growth reaction pathways. Second, we review typical PAH formation reactions inspecting repetitive growth sequences in dependence of the molecular fuel structure. Third, we discuss how differences in the intermediate species pool influence the formation reactions of key aromatic ring species that are important for the PAH growth process underlying soot formation. As a main result it was found that for the fuels featuring a CC double bond, the chemistry of their allylic fuel radicals and their decomposition products strongly influences the combination reactions to the initially formed aromatic ring species and as a consequence, the PAH formation tendency.

In an effort to utilize their unique photoactive properties, porphyrin monomers were assembled into tetragonal microparticles by a surfactant-assisted neutralization method through the cooperative interactions between the porphyrin building blocks including π-π stacking, J-aggregation and metal-ligand coordination. Electron microscopy characterization in combination with X-ray diffraction confirmed the three-dimensional ordered tetragonal microstructures with stable crystalline frameworks and well defined external surface morphology. Optical absorption and fluorescence spectroscopy revealed enhanced absorbance properties as compared with the raw porphyrin material, favourable for chromophore excitation and energy transport. With active and responsive optical properties, these new porphyrin microparticles look to serve as promising components for a wide range of applications including sensing, diagnostics, solar cells, and optoelectronic devices.

We recently developed a vacuum assisted micelle confinement synthesis for spherical microparticles of CL-20 with outstanding monodispersity. These microparticles are promising energetic material for explosive devices with enhanced and predictable performances. In this work, to facilitate further development and application of this synthesis, the particle growth process was monitored by in-situ dynamic light scattering measurements. The result was interpreted by a finite element model to obtain critical parameters. These parameters were then used to predict the behavior and product quality of batch synthesis under various operation conditions.

A new quantum dot synthesis method based on metallic-block copolymer precursors was developed. The synthesis produced CdS QDs assembled into chains. This method provides a new model for the study of 1D QD chains to determine its effect on charge transport and optoelectronic coupling. This synthesis method was readily extended to other semiconductor materials including PbS and perovskites producing QDs of various shapes. It evidenced further promise of this synthesis method to assist in the assembly, shape and size control of various nanomaterials.

Low dimensional lead halide perovskite particles are of tremendous interest due to their size-tunable band gaps, low exciton binding energy, high absorption coefficients, outstanding quantum and photovoltaic efficiencies. Herein we report a new solution-based synthesis of stabilized Cs4PbBr6 perovskite particles with high luminescence. This method requires only mild conditions and produces colloidal particles that are ideal for highly efficient solution-based device fabrications. The synthesized microstructures not only display outstanding luminescence quantum yield but also long term stability in atmospheric conditions. Partial halide substitutions were also demonstrated to extend photoluminescence spectra of the perovskite particles. This convenient synthesis and optical tunability of Cs4PbBr6 perovskite particles will be advantageous for future applications of optoelectronic advices.

Sodium Fast Reactors (SFRs) have an extensive operational history that can be leveraged to accelerate the licensing process for modern designs. Sandia National Laboratories (SNL) has recently reconstituted the United States SFR data from the Centralized Reliability Database Organization (CREDO) into a new modern database called the Sodium (Na) System Component Reliability Database (NaSCoRD). This new database is currently undergoing validation and usability testing to better understand the strengths and limitations of this historical data. The most common class of equipment found in the NaSCoRD database are valves. NaSCoRD contains a record of over 4,000 valves that have operated in EBR-II, FFTF, and test loops including those operated by Westinghouse and the Energy Technology Engineering Center. Valve failure events in NaSCoRD can be categorized by working fluid (e.g., sodium, water, gas), valve type (e.g., butterfly, check, throttle, block), failure mode (e.g., failure to open, failure to close, rupture), operating facility, operating temperature, or other user defined categories. Sodium valve reliability estimates will be presented in comparison to estimates provided in historical studies. The impacts of EG&G Idaho’s suggested corrections and various prior distributions on these reliability estimates will also be presented.

Containment bypass scenarios in nuclear power plants can lead to large early release of radionuclides. A residual heat removal (RHR) system interfacing system loss of coolant accident (ISLOCA) has the potential to cause a hazardous environment in the auxiliary building, a loss of coolant from the primary system, a pathway for early release of radionuclides, and the failure of a system important to safely shutting down the plant. Prevention of this accident sequence relies on active systems that may be vulnerable to cyber failures in new or retrofitted plants with digital instrumentation and control systems. RHR ISLOCA in a hypothetical pressurized water reactor is analyzed in a dynamic framework to evaluate the time-dependent effects of various uncertainties on the state of the nuclear fuel, the auxiliary building environment, and the release of radionuclides. The ADAPT dynamic event tree code is used to drive both the MELCOR severe accident analysis code and the RADTRAD dose calculation code to track the progression of the accident from the initiating event to its end states. The resulting data set is then mined for insights into key events and their impacts on the final state of the plant and radionuclide releases.

The recovery of approximately sparse or compressible coefficients in a polynomial chaos expansion is a common goal in many modern parametric uncertainty quantification (UQ) problems. However, relatively little effort in UQ has been directed toward theoretical and computational strategies for addressing the sparse corruptions problem, where a small number of measurements are highly corrupted. Such a situation has become pertinent today since modern computational frameworks are sufficiently complex with many interdependent components that may introduce hardware and software failures, some of which can be difficult to detect and result in a highly polluted simulation result. In this paper we present a novel compressive sampling-based theoretical analysis for a regularized \ell1 minimization algorithm that aims to recover sparse expansion coefficients in the presence of measurement corruptions. Our recovery results are uniform (the theoretical guarantees hold for all compressible signals and compressible corruptions vectors) and prescribe algorithmic regularization parameters in terms of a user-defined a priori estimate on the ratio of measurements that are believed to be corrupted. We also propose an iteratively reweighted optimization algorithm that automatically refines the value of the regularization parameter and empirically produces superior results. Our numerical results test our framework on several medium to high dimensional examples of solutions to parameterized differential equations and demonstrate the effectiveness of our approach.

The laser damage thresholds of optical coatings can degrade over time due to a variety of factors, including contamination and aging. Optical coatings deposited using electron beam evaporation are particularly susceptible to degradation due to their porous structure. In a previous study, the laser damage thresholds of optical coatings were reduced by roughly a factor of two from 2013 to 2017. The coatings in question were high reflectors for 1054 nm that contained SiO 2 and HfO 2 and/or TiO 2 layers, and they were stored in sealed PETG containers in a class 100 cleanroom with temperature control. At the time, it was not certain whether contamination or thin film aging effects were responsible for the reduced laser damage thresholds. Therefore, to better understand the role of contamination, the coatings were recleaned and the laser damage thresholds were measured again in 2018. The results indicate that contamination played the most dominant role in reducing the laser damage thresholds of these optical coatings, even though they were stored in an environment that was presumed to be clean.

Multi-material composite structures develop residual stresses during the curing process due to dissimilar material properties, which eventually may lead to failure in the form of fracture, delamination, or disbonding. Experimentally determining residual stresses can be both time and cost prohibitive, whereas accurate simulated predictions of residual stresses can be cheaper and provide equivalent information during the design process. Residual stresses can be simulated through several different approaches of varying complexity. The method employed in this study assumes the majority of residual stresses are developed due the mismatch of coefficients of thermal expansion and polymer shrinkage, which is indirectly accounted for by calibrating the simulation with an experimentally determined stress free temperature. This method has shown success in predicting the residual stress states across different material combinations and structures in previous studies. Simply using single, or nominal, inputs to the simulation may provide a reasonable prediction, but will be unable to provide any confidence when failure could occur. Therefore, one must consider the natural stochastic behavior of the materials and geometry through an uncertainty quantification study. However, a limitation in performing uncertainty quantification studies for more complex models exists due to the large number of material and geometry parameters that need to be considered. The results from a previously conducted survey of sensitivity analysis methods were leveraged to reduce the number of parameters considered during an uncertainty quantification study, as well as decrease the computational cost in determining the sensitive parameters. This allowed the application of uncertainty quantification methods to validate more complex multi-material structures against experimental results. The structure that will be considered is a multi-material split ring comprised of three layers: Aluminum, glass fiber composite, and carbon fiber composite.

The U.S. Nuclear Regulatory Commission initiated the state-of-the-art reactor consequence analyses (SOARCA) project to develop realistic estimates of the offsite radiological health consequences for potential severe reactor accidents. The SOARCA analysis of an ice condenser containment plant was performed because its relatively low design pressure and reliance on igniters makes it potentially susceptible to early containment failure from hydrogen combustion during a severe accident. The focus was on station blackout accident scenarios where all alternating current power is lost. Accident progression calculations used the MELCOR computer code and offsite consequence analyses were performed with MACCS. The analysis included more than 500 MELCOR and MACCS simulations to account for uncertainty in important accident progression and offsite consequence input parameters. Consequences from severe nuclear power plant accidents modeled in this and previous SOARCA analyses are smaller than calculated in earlier studies. The delayed releases calculated provide more time for emergency response actions. The results show that early containment failure is very unlikely, even without successful use of igniters. However, these results are dependent on the distributions assigned to safety valve failure-to-close parameters, and considerable uncertainty remains on the true distributions for these parameters due to very limited test data. Even for scenarios resulting in early containment failure, the calculated individual latent fatal cancer risks are very small. Early and latent-cancer fatality risks are one focus of this paper. Regression results showing the most influential parameters are also discussed.

An ENUN 32P cask supplied by Equipos Nucleares S.A. (ENSA) was transported 9,600 miles by road, sea, and rail in 2017 in order to collect shock and vibration data on the cask system and surrogate spent fuel assemblies within the cask. The task of examining 101,857 ASCII data files – 6.002 terabytes of data (this includes binary and ASCII files) – has begun. Some results of preliminary analyses are presented in this paper. A total of seventy-seven accelerometers and strain gauges were attached by Sandia National Laboratories (SNL) to three surrogate spent fuel assemblies, the cask basket, the cask body, the transport cradle, and the transport platforms. The assemblies were provided by SNL, Empresa Nacional de Residuos Radiactivos, S.A. (ENRESA), and a collaboration of Korean institutions. The cask system was first subjected to cask handling operations at the ENSA facility. The cask was then transported by heavy-haul truck in northern Spain and shipped from Spain to Belgium and subsequently to Baltimore on two roll-on/roll-off ships. From Baltimore, the cask was transported by rail using a 12- axle railcar to the American Association of Railroads’ Transportation Technology Center, Inc. (TTCI) near Pueblo, Colorado where a series of special rail tests were performed. Data were continuously collected during this entire sequence of multi-modal transportation events. (We did not collect data on the transfer between modes of transportation.) Of particular interest – indeed the original motivation for these tests – are the strains measured on the zirconium-alloy tubes in the assemblies. The strains for each of the transport modes are compared to the yield strength of irradiated Zircaloy to illustrate the margin against rod failure during normal conditions of transport. The accelerometer data provides essential comparisons of the accelerations on the different components of the cask system exhibiting both amplification and attenuation of the accelerations at the transport platforms through the cradle and cask and up to the interior of the cask. These data are essential for modeling cask systems. This paper concentrates on analyses of the testing of the cask on a 12-axle railcar at TTCI.

This work demonstrates that the ionic selectivity and ionic conductivity of nanoporous membranes can be controlled independently via layer-by-layer (LbL) deposition of polyelectrolytes and subsequent selective cross-linking of these polymer layers. LbL deposition offers a scalable, inexpensive method to tune the ion transport properties of nanoporous membranes by sequentially dip coating layers of cationic polyethyleneimine and anionic poly(acrylic acid) onto polycarbonate membranes. The cationic and anionic polymers are self-assembled through electrostatic and hydrogen bonding interactions and are chemically crosslinked to both change the charge distribution and improve the intermolecular integrity of the deposited films. Both the thickness of the deposited coating and the use of chemical cross-linking agents influence charge transport properties significantly. Increased polyelectrolyte thickness increases the selectivity for cationic transport through the membranes while adding polyelectrolyte films decreases the ionic conductivity compared to an uncoated membrane. Once the nanopores are filled, no additional decrease in conductivity is observed with increasing film thickness and, upon cross-linking, a portion of the lost conductivity is recovered. The cross-linking agent also influences the ionic selectivity of the resulting polyelectrolyte membranes. Increased selectivity for cationic transport occurs when using glutaraldehyde as the cross-linking agent, as expected due to the selective cross-linking of primary amines that decreases the net positive charge. Together, these results inform deposition of chemically robust, highly conductive, ion-selective membranes onto inexpensive porous supports for applications ranging from energy storage to water purification.

The corrosion behavior of selective laser melted (SLM) 304L was investigated and compared to conventional wrought 304L in aqueous chloride and acidic solutions. Through immersed electrochemical testing and exposure in acidic solutions, the SLM 304L exhibited superior pitting resistance in the polished state compared to wrought 304L. However, the surface condition of the SLM material had a great impact on its corrosion resistance, with the grit-blasted condition exhibiting severely diminished pitting resistance. Local scale, capillary micro-electrochemical and scanning electrochemical microscopy investigations, identified porosity as a contributing factor to decreased corrosion resistance. Preferential corrosion attack was not observed to be related to the characteristic underlying cellular microstructure produced through SLM processing. This study highlights the effects of SLM microstructural features on corrosion resistance, specifically the substantial influence of surface finish on SLM corrosion behavior and the need for development and optimization of processing techniques to improve surface finish.

In light- and medium-duty diesel engines, piston bowl shape influences thermal efficiency, either due to changes in wall heat loss or to changes in the heat release rate. The relative contributions of these two factors are not clearly described in the literature. In this work, two production piston bowls are adapted for use in a single cylinder research engine: a conventional, re-entrant piston, and a stepped-lip piston. An injection timing sweep is performed at constant load with each piston, and heat release analyses provide information about thermal efficiency, wall heat loss, and the degree of constant volume combustion. Zero-dimensional thermodynamic simulations provide further insight and support for the experimental results. The effect of bowl geometry on wall heat loss depends on injection timing, but changes in wall heat loss cannot explain changes in efficiency. Late cycle heat release is faster with the stepped-lip bowl than with the conventional re-entrant bowl, which leads to a higher degree of constant volume combustion and therefore higher thermal efficiency. This effect also depends on injection timing. In general, increasing the degree of constant volume combustion is significantly more effective at improving thermal efficiency than decreasing wall heat loss. Maximizing thermal efficiency will require a deeper understanding of how bowl geometry impacts flow structure, turbulent mixing, and mixing-controlled combustion.

Modeling of chemical and ionization reactions at the extreme conditions of upper-atmosphere hypersonic flow has been critical for spacecraft design from the Apollo era to the present because chemical activity in the flow reduces heat transfer. Nitrogen, which behaves as an inert gas in ambient flows, becomes chemically active under conditions of hypersonic reentry (-10,000 K). Atmospheric chemical reactions during hypersonic reentry are dominated by dissociation of diatomic nitrogen and oxygen molecules and exchange reactions involving diatomic molecules and single atoms. At higher temperatures, ionization also occurs.

Abstract not provided.

Abstract not provided.

The dissociative photoionization processes of methyl hydroperoxide (CH3OOH) have been studied by imaging Photoelectron Photoion Coincidence (iPEPICO) spectroscopy experiments as well as quantum-chemical and statistical rate calculations. Energy selected CH3OOH+ ions dissociate into CH2OOH+, HCO+, CH3 +, and H3O+ ions in the 11.4-14.0 eV photon energy range. The lowest-energy dissociation channel is the formation of the cation of the smallest "QOOH" radical, CH2OOH+. An extended RRKM model fitted to the experimental data yields a 0 K appearance energy of 11.647 ± 0.005 eV for the CH2OOH+ ion, and a 74.2 ± 2.6 kJ mol-1 mixed experimental-theoretical 0 K heat of formation for the CH2OOH radical. The proton affinity of the Criegee intermediate, CH2OO, was also obtained from the heat of formation of CH2OOH+ (792.8 ± 0.9 kJ mol-1) to be 847.7 ± 1.1 kJ mol-1, reducing the uncertainty of the previously available computational value by a factor of 4. RRKM modeling of the complex web of possible rearrangement-dissociation processes was used to model the higher-energy fragmentation. Supported by Born-Oppenheimer molecular dynamics simulations, we found that the HCO+ fragment ion is produced through a roaming transition state followed by a low barrier. H3O+ is formed in a consecutive process from the CH2OOH+ fragment ion, while direct C-O fission of the molecular ion leads to the methyl cation.

We present here a multi-length scale integration of compositionally tailored NaSICON-based Na+ conductors to create a high Na+ conductivity system resistant to chemical attack in strongly alkaline aqueous environments. Using the Pourbaix Atlas as a generalized guide to chemical stability, we identify NaHf2P3O12 (NHP) as a candidate NaSICON material for enhanced chemical stability at pH > 12, and demonstrate the stability of NHP powders under accelerated aging conditions of 80 °C and pH = 13-15 for a variety of alkali metal cations. To compensate for the relatively low ionic conductivity of NHP, we develop a new low temperature (775 °C) alkoxide-based solution deposition chemistry to apply dense NHP thin films onto both platinized silicon wafers and bulk, high Na+ conductivity Na3Zr2Si2PO12 (NZSP) pellets. These NHP films display Na+ conductivities of 1.35 × 10-5 S cm-1 at 200 °C and an activation energy of 0.53 eV, similar to literature reports for bulk NHP pellets. Under aggressive conditions of 10 M KOH at 80 °C, NHP thin films successfully served as an alkaline-resistant barrier, extending the lifetime of NZSP pellets from 4.26 to 36.0 h. This integration of compositionally distinct Na+ conductors across disparate length scales (nm, mm) and processing techniques (chemically-derived, traditional powder) represents a promising new avenue by which Na+ conducting systems may be utilized in alkaline environments previously thought incompatible with ceramic Na+ conductors.

Low-temperature gasoline combustion (LTGC) engines can deliver high efficiencies, with ultra-low emissions of nitrogen oxides (NOx) and particulate matter (PM). However, controlling the combustion timing and maintaining robust operation remains a challenge for LTGC engines. One promising technique to overcoming these challenges is spark assist (SA). In this work, well-controlled, fully premixed experiments are performed in a single-cylinder LTGC research engine at 1200 rpm using a cylinder head modified to accommodate a spark plug. Compression ratios (CR) of 16:1 and 14:1 were used during the experiments. Two different fuels were also tested, with properties representative of premium- and regular-grade market gasolines. SA was found to work well for both CRs and fuels. The equivalence ratio limits and the effect of intake-pressure boost on the ability of SA to compensate for a reduced Tin were studied. For the conditions studied, =0.42 was found to be most effective for SA. At lower equivalence ratios the flame propagation was too weak, whereas =0.45 was closer to the CI knock/stability limit, which resulted in a smaller range of CA50 control and Tin compensation. At =0.42, SA worked well from Pin = 1.0 to 1.6 bar, but the range of effective Tin compensation dropped progressively with boost from 21 °C at Pin = 1.0 bar to the equivalent of 12 °C at Pin = 1.6 bar. The amount of control authority using SA was demonstrated by varying the spark timing, advancing CA50 to the onset of strong knocking and then retarding CA50 to near misfire. SA provided good control, however the CA50 control range decreased from 7.2° CA at Pin = 1.0 bar to 4.2° CA at Pin = 1.6 bar. For all intake pressures at these well-mixed conditions, NOx emissions for SA were less than for compression ignition only, and all were below the US-2010 Heavy Duty limit.

The development of scramjet engines is an important research area for advancing hypersonic and orbital flights. Progress toward optimal engine designs requires accurate flow simulations together with uncertainty quantification. However, performing uncertainty quantification for scramjet simulations is challenging due to the large number of uncertainparameters involvedandthe high computational costofflow simulations. These difficulties are addressedin this paper by developing practical uncertainty quantification algorithms and computational methods, and deploying themin the current studyto large-eddy simulations ofajet incrossflow inside a simplified HIFiRE Direct Connect Rig scramjet combustor. First, global sensitivity analysis is conducted to identify influential uncertain input parameters, which can help reduce the system's stochastic dimension. Second, because models of different fidelity are used in the overall uncertainty quantification assessment, a framework for quantifying and propagating the uncertainty due to model error is presented. These methods are demonstrated on a nonreacting jet-in-crossflow test problem in a simplified scramjet geometry, with parameter space up to 24 dimensions, using static and dynamic treatments of the turbulence subgrid model, and with two-dimensional and three-dimensional geometries.

Teixobactin (Txb) is a recently discovered antibiotic against Gram-positive bacteria that induces no detectable resistance. The bactericidal mechanism is believed to be the inhibition of cell wall biosynthesis by Txb binding to lipid II and lipid III. Txb binding specificity likely arises from targeting of the shared lipid component, the pyrophosphate moiety. Despite synthesis and functional assessment of numerous chemical analogs of Txb, and consequent identification of the Txb pharmacophore, the detailed structural information of Txb-substrate binding is still lacking. Here, we use molecular modeling and microsecond-scale molecular dynamics simulations to capture the formation of Txb-lipid II complexes at a membrane surface. Two dominant binding conformations were observed, both showing characteristic lipid II phosphate binding by the Txb backbone amides near the C-terminal cyclodepsipeptide (d-Thr8-Ile11) ring. Additionally, binding by Txb also involved the side chain hydroxyl group of Ser7, as well as a secondary phosphate binding provided by the side chain of l-allo-enduracididine. Interestingly, those conformations differ by swapping two groups of hydrogen bond donors that coordinate the two phosphate moieties of lipid II, resulting in opposite orientations of lipid II binding. In addition, residues d-allo-Ile5 and Ile6 serve as the membrane anchors in both Txb conformations, regardless of the detailed phosphate binding interactions near the cyclodepsipeptide ring. The role of hydrophobic residues in Txb activity is primarily for its membrane insertion, and subsidiarily to provide non-polar interactions with the lipid II tail. Based on the Txb-lipid II interactions captured in their complexes, as well as their partitioning depths into the membrane, we propose that the bactericidal mechanism of Txb is to arrest cell wall synthesis by selectively inhibiting the transglycosylation of peptidoglycan, while possibly leaving the transpeptidation step unaffected. The observed "pyrophosphate caging" mechanism of lipid II inhibition appears to be similar to some lantibiotics, but different from that of vancomycin or bacitracin.

We report on mode selection and tuning properties of vertical-external-cavity surface-emitting lasers (VECSELs) containing coupled semiconductor and external cavities of total length less than 1 mm. Our goal is to create narrowlinewidth (<1MHz) single-frequency VECSELs that operate near 850 nm on a single longitudinal cavity resonance and tune versus temperature without mode hops. We have designed, fabricated, and measured VECSELs with external-cavity lengths ranging from 25 to 800 μm. We compare simulated and measured coupled-cavity mode frequencies and discuss criteria for single mode selection.

Sandia National Laboratories and the National Renewable Energy Laboratory conducted a field campaign at the Scaled Wind Farm Technology (SWiFT) Facility using a customized scanning lidar from the Technical University of Denmark. The results from this field campaign were used to assess the predictive capability of computational models to capture wake dissipation and wake trajectory downstream of a wind turbine. The present work used large-eddy simulations of the wind turbine wake and a virtual SpinnerLidar to quantify the uncertainty of wind turbine wake position due to the line-of-sight sampling and probe volume averaging effects of the lidar. The LES simulations were of the SWiFT wind turbine at both a 0° and 30° yaw offset with a stable inflow. The wake position extracted from the simulated lidar sampling had an uncertainty of 2.8 m and m as compared to the wake position extracted from the full velocity field with 0° and 30° yaw offset, respectively. The larger uncertainty in calculated wake position of the 30° yaw offset case was due to the increased angle of the wake position relative to the axial flow direction and the resulting decrease in the line-of-sight velocity relative the axial velocity.

Effectively using a graphics processing unit (GPU) for Monte Carlo particle transport is a challenging task due to its memory storage requirements and traditionally divergent algorithms. Most efforts in this area have focused on the entire transport process, choosing to use atomic operations or tally replication Tor computing tallies. This work isolates the performance of the tallies from the rest of the transport process, and studies the impact of using different approaches for tallying on the GPU. Five implementations of a photon escape tally are compared, using both single and double precision data types. Results show that replicating tallies is clearly the best option overall, if there is enough memory available on the GPU to store them. When insufficient memory becomes an issue, the best method to use depends on the size, data type, and update frequency of the tally. Global atomic updates can be a reasonable option in some cases, especially if they arc infrequently used. However, there arc two alternatives for general-purpose tallying that were shown to be more effective in most of the scenarios considered. These two alternatives arc based on NVIDIA's warp shuffle feature, which allows 32 threads to simultaneously exchange or broadcast data, minimizing the number of atomic operations needed to get the final tally result.

We implemented a computationally efficient model for a corner-supported, thin, rectangular, orthotropic polyvinylidene fluoride (PVDF) laminate membrane, actuated by a two-dimensional array of segmented electrodes. The laminate can be used as shape-controlled electromagnetic reflector and the model estimates the reflector's shape given an array of control voltages. In this paper, we describe a model to determine the shape of the laminate for a given distribution of control voltages. Then, we investigate the surface shape error and its sensitivity to the model parameters. Subsequently, we analyze the simulated deflection of the actuated bimorph using a Zernike polynomial decomposition. Finally, we provide a probabilistic description of reflector performance using statistical methods to quantify uncertainty. We make design recommendations for nominal parameter values and their tolerances based on optimization under uncertainty using multiple methods.

An 11-channel 1-GHz bandwidth silicon photonic AWG was fabricated and measured in the lab. Two photonic architectures are presented: (1) RF-envelope detector, and (2) RF downconvertor for digital systems. The RF-envelope detector architecture was modeled based on the demonstrated AWG characteristics to determine estimated system-level RF receiver performance.

Mathematical models of Redox Flow Batteries (RFBs) can be used to analyze cell performance, optimize battery operation, and control the energy storage system efficiently. Among many other models, physics-based electrochemical models are capable of predicting internal states of the battery, such as temperature, state-of-charge, and state-of-health. In the models, estimating parameters is an important step that can study, analyze, and validate the models using experimental data. A common practice is to determine these parameters either through conducting experiments or based on the information available in the literature. However, it is not easy to investigate all proper parameters for the models through this way, and there are occasions when important information, such as diffusion coefficients and rate constants of ions, has not been studied. Also, the parameters needed for modeling charge-discharge are not always available. In this paper, an efficient way to estimate parameters of physics-based redox battery models will be proposed. This paper also demonstrates that the proposed approach can study and analyze aspects of capacity loss/fade, kinetics, and transport phenomena of the RFB system.

We consider the problem of selecting a small set (mosaic) of sensor images (footprints) whose union covers a two-dimensional Region Of Interest (ROI) on Earth. We take the approach of modeling the mosaic problem as a Mixed-Integer Linear Program (MILP). This allows solutions to this subproblem to feed into a larger remote-sensor collection-scheduling MILP. This enables the scheduler to dynamically consider alternative mosaics, without having to perform any new geometric computations. Our approach to set up the optimization problem uses maximal disk sampling and point-in-polygon geometric calculations. Footprints may be of any shape, even non-convex, and we show examples using a variety of shapes that may occur in practice. The general integer optimization problem can become computationally expensive for large problems. In practice, the number of placed footprints is within an order of magnitude of ten, making the time to solve to optimality on the order of minutes. This is fast enough to make the approach relevant for near real-time mission applications. We provide open source software for all our methods, "GeoPlace."

Abstract not provided.

Kerogen plays a central role in hydrocarbon generation in an oil/gas reservoir. In a subsurface environment, kerogen is constantly subjected to stress confinement or relaxation. The interplay between mechanical deformation and gas adsorption of the materials could be an important process for shale gas production but unfortunately is poorly understood. Using a hybrid Monte Carlo/molecular dynamics simulation, we show here that a strong chemo-mechanical coupling may exist between gas adsorption and mechanical strain of a kerogen matrix. The results indicate that the kerogen volume can expand by up to 5.4% and 11% upon CH4 and CO2 adsorption at 192 atm, respectively. The kerogen volume increases with gas pressure and eventually approaches a plateau as the kerogen becomes saturated. The volume expansion appears to quadratically increase with the amount of gas adsorbed, indicating a critical role of the surface layer of gas adsorbed in the bulk strain of the material. Furthermore, gas uptake is greatly enhanced by kerogen swelling. Swelling also increases the surface area, porosity, and pore size of kerogen. Our results illustrate the dynamic nature of kerogen, thus questioning the validity of the current assumption of a rigid kerogen molecular structure in the estimation of gas-in-place for a shale gas reservoir or gas storage capacity for subsurface carbon sequestration. The coupling between gas adsorption and kerogen matrix deformation should be taken into consideration.

Thousands of facilities worldwide are engaged in biological research activities. One of DTRA's missions is to fully understand the types of facilities involved in collecting, investigating, and storing biological materials. This characterization enables DTRA to increase situational awareness and identify potential partners focused on biodefense and biosecurity. As a result of this mission, DTRA created a database to identify biological facilities from publicly available, open-source information. This paper describes an on-going effort to automate data collection and entry of facilities into this database. To frame our analysis more concretely, we consider the following motivating question: How would a decision maker respond to a pathogen outbreak during the 2018 Winter Olympics in South Korea? To address this question, we aim to further characterize the existing South Korean facilities in DTRA's database, and to identify new candidate facilities for entry, so that decision makers can identify local facilities properly equipped to assist and respond to an event. We employ text and social analytics on bibliometric data from South Korean facilities and a list of select pathogen agents to identify patterns and relationships within scientific publication graphs.

In situ Raman microscopy was used to study polysulfide speciation in the bulk ether electrolyte during the discharge and charge of a Li-S electrochemical cell to assess the complex interplay between chemical and electrochemical reactions in solution. During discharge, long chain polysulfides and the S3− radical appear in the electrolyte at 2.4 V indicating a rapid equilibrium of the dissociation reaction to form S3−. When charging, however, an increase in the concentration of all polysulfide species was observed. This highlights the importance of the electrolyte to sulfur ratio and suggests a loss in the useful sulfur inventory from the cathode to the electrolyte.

This study addresses predicting the internal thermochemical state in buoyant fire plumes using largeeddy simulations (LES) with a tabular flamelet library for the underlying flame chemistry. Buoyant fire plumes are characterized by moderate turbulent mixing, soot growth and oxidation and radiation transport. Soot moments, mixture fraction and enthalpy evolve in the LES with soot source terms given by the non-adiabatic flamelet library. Participating media radiation transport is predicted using the discrete ordinates method with source terms also from the flamelet library, and the LES subgrid-scale modeling is based on a one-equation kinetic-energy sub-filter model. This library is generated with flamelet states that include unsteady heat loss through extinction nominally representing radiative quenching. We describe the performance of this model both in the context of a laminar coflow configuration where extensive measurements are available and in buoyant turbulent fire plumes where measurements are more global.

Apparent char kinetic rates are commonly used to predict pulverized coal char burning rates. These kinetic rates quantify the char burning rate based on the temperature of the particle and the oxygen concentration at the particle surface, thereby inherently neglecting the impact of variations in the penetration of oxygen into the char on the predicted burning rate. To investigate the impact of variable extents of penetration during Zone II burning conditions, experimental measurements were performed of char particle combustion temperature and burnout for a common U.S. subbituminous coal burning in an optical laminar entrained flow reactor with either helium or nitrogen diluents. The combination of much higher thermal conductivity and mass diffusivity in the helium environments resulted in substantially cooler char combustion temperatures than in equivalent N2 environments. Measured char burnout was similar in the two environments for a given bulk oxygen concentration but was approximately 60% higher in helium environments for a given char combustion temperature. Detailed particle simulations of the experimental conditions confirmed a 60% higher burning rate in the helium environments as a function of char temperature, whereas catalyst theory predicts that the burning rate in helium could be as high as 90% greater than in nitrogen, in the limit of large Thiele modulus (i.e. near the diffusion limit). For application combustion in CO2 environments (e.g. for oxy-fuel combustion), these results demonstrate that due to differences in oxygen diffusivity the apparent char oxidation rates will be lower, but by no more than 9% relative to burning rates measured in nitrogen environments.

Considering that simulations of turbulent combustion are computationally expensive, this chapter takes a decidedly different perspective, that of high-performance computing (HPC). The cost scaling arguments of non-reacting turbulence simulations are revisited and it is shown that the cost scaling for reacting flows is much more stringent for comparable conditions, making parallel computing and HPC indispensable. Hardware abstractions of typical parallel supercomputers are presented which show that for design of an efficient and optimal program, it is essential to exploit both distributed memory parallelism and shared-memory parallelism, i.e. hierarchical parallelism. Principles of efficient programming at various levels of parallelism are illustrated using archetypal code examples. The vast array of numerical methods, particularly schemes for spatial and temporal discretization, are examined in terms of tradeoffs they present from an HPC perspective. Aspects of data analytics that invariably result from large feature-rich data sets generated by combustion simulations are covered briefly.

Abstract not provided.

Apparent char kinetic rates are commonly used to predict pulverized coal char burning rates. These kinetic rates quantify the char burning rate based on the temperature of the particle and the oxygen concentration at the particle surface, thereby inherently neglecting the impact of variations in the penetration of oxygen into the char on the predicted burning rate. To investigate the impact of variable extents of penetration during Zone II burning conditions, experimental measurements were performed of char particle combustion temperature and burnout for a common U.S. subbituminous coal burning in an optical laminar entrained flow reactor with either helium or nitrogen diluents. The combination of much higher thermal conductivity and mass diffusivity in the helium environments resulted in substantially cooler char combustion temperatures than in equivalent N2 environments. Measured char burnout was similar in the two environments for a given bulk oxygen concentration but was approximately 60% higher in helium environments for a given char combustion temperature. Detailed particle simulations of the experimental conditions confirmed a 60% higher burning rate in the helium environments as a function of char temperature, whereas catalyst theory predicts that the burning rate in helium could be as high as 90% greater than in nitrogen, in the limit of large Thiele modulus (i.e. near the diffusion limit). For application combustion in CO2 environments (e.g. for oxy-fuel combustion), these results demonstrate that due to differences in oxygen diffusivity the apparent char oxidation rates will be lower, but by no more than 9% relative to burning rates measured in nitrogen environments.

Geological carbon storage (GCS) is a promising technology for mitigating increasing concentrations of carbon dioxide (CO2) in the atmosphere. The injection of supercritical CO2into geological formations perturbs the physical and chemical state of the subsurface. The reservoir rock, as well as the overlying caprock, can experience changes in the pore fluid pressure, thermal state, chemical reactivity and stress distribution. These changes can cause mechanical deformation of the rock mass, opening/closure of preexisting fractures or/and initiation of new fractures, which can influence the integrity of the overall geological carbon storage (GCS) systems over thousands of years, required for successful carbon storage. GCS sites are inherently unified systems; however, given the scientific framework, these systems are usually divided based on the physics and temporal/spatial scales during scientific investigations. For many applications, decoupling the physics by treating the adjacent system as a boundary condition works well. Unfortunately, in the case of water and gas flow in porous media, because of the complexity of geological subsurface systems, the decoupling approach does not accurately capture the behavior of the larger relevant system. The coupled processes include various combinations of thermal (T), hydrological (H), chemical (C), mechanical (M), and biological (B) effects. These coupled processes are time- and length-scale- dependent, and can manifest in one- or two-way coupled behavior. There is an undeniable need for understanding the coupling of processes during GCS, and how these coupled phenomena can result in emergent behaviors arising from the interplay of physics and chemistry, including self - focusing of flow, porosity collapse, and changes in fracture networks. In this chapter, the first section addresses the subsurface system response to the injection of CO2, examined at field and laboratory scales, as well as in model systems, addressed from a perspective of single disciplines. The second section reviews coupling between processes during GCS observed either in the field or anticipated based on laboratory results.

Methods for the efficient representation of fracture response in geoelectric models impact an impressively broad range of problems in applied geophysics. We adopt the recently-developed hierarchical material property representation in finite element analysis (Weiss, 2017) to model the electrostatic response of a discrete set of vertical fractures in the near surface and compare these results to those from anisotropic continuum models. We also examine the power law behavior of these results and compare to continuum theory. We find that in measurement profiles from a single point source in directions both parallel and perpendicular to the fracture set, the fracture signature persists over all distances. Furthermore, the homogenization limit (distance at which the individual fracture anomalies are too small to be either measured or of interest) is not strictly a function of the geometric distribution of the fractures, but also their conductivity relative to the background. Hence, we show that the definition of “representative elementary volume”, that distance over which the statistics of the underlying heterogeneities is stationary, is incomplete as it pertains to the applicability of an equivalent continuum model. We also show that detailed interrogation of such intrinsically heterogeneous models may reveal power law behavior that appears anomalous, thus suggesting a possible mechanism to reconcile emerging theories in fractional calculus with classical electromagnetic theory.

Many types of dark regions occur naturally or artificially in Synthetic Aperture Radar (SAR) and Coherent Change Detection (CCD) products. Occluded regions in SAR imagery, known as shadows, are created when incident radar energy is obstructed by a target with height from illuminating resolution cells immediately behind the target in the ground plane. No return areas are also created from objects or terrain that produce little scattering in the direction of the receiver, such as still water or flat plates for monostatic systems. Depending on the size of the dark region, additive and multiplicative noise levels are commonly measured for SAR performance testing. However, techniques for radar performance testing of CCD using dark regions are not common in the literature. While dark regions in SAR imagery also produce dark regions in CCD products, additional dark regions in CCD may further arise from decorrelation of bright regions in SAR imagery due to clutter or terrain that has poor wide-sense stationarity (such as foliage in wind), man-made disturbances of the scene, or unintended artifacts introduced by the radar and image processing. By comparing dark regions in CCD imagery over multiple passes, one can identify unintended decorrelation introduced by poor radar performance rather than phenomenology. This paper addresses select dark region automated measurement techniques for the evaluation of radar performance during SAR and CCD field testing.

Effectively using a graphics processing unit (GPU) for Monte Carlo particle transport is a challenging task due to its memory storage requirements and traditionally divergent algorithms. Most efforts in this area have focused on the entire transport process, choosing to use atomic operations or tally replication Tor computing tallies. This work isolates the performance of the tallies from the rest of the transport process, and studies the impact of using different approaches for tallying on the GPU. Five implementations of a photon escape tally are compared, using both single and double precision data types. Results show that replicating tallies is clearly the best option overall, if there is enough memory available on the GPU to store them. When insufficient memory becomes an issue, the best method to use depends on the size, data type, and update frequency of the tally. Global atomic updates can be a reasonable option in some cases, especially if they arc infrequently used. However, there arc two alternatives for general-purpose tallying that were shown to be more effective in most of the scenarios considered. These two alternatives arc based on NVIDIA's warp shuffle feature, which allows 32 threads to simultaneously exchange or broadcast data, minimizing the number of atomic operations needed to get the final tally result.

This paper introduces the "Discrete Direct" (DD) model calibration and uncertainty propagation approach for computational models calibrated to data from sparse replicate tests of stochastically varying systems. The DD approach generates and propagates various discrete realizations of possible calibration parameter values corresponding to possible realizations of the uncertain inputs and outputs of the experiments. This is in contrast to model calibration methods that attempt to assign or infer continuous probability density functions for the calibration parameters-which adds unjustified information to the calibration and propagation problem. The DD approach straightforwardly accommodates aleatory variabilities and epistemic uncertainties in system properties and behaviors, in input initial and boundary conditions, and in measurement uncertainties in the experiments. The approach appears to have several advantages over Bayesian and other calibration approaches for capturing and utilizing the information obtained from the typically small number of experiments in model calibration situations. In particular, the DD methodology better preserves the fundamental information from the experimental data in a way that enables model predictions to be more directly traced back to the supporting experimental data. The approach is also presently more viable for calibration involving sparse realizations of random function data (e.g. stress-strain curves) and random field data. The DD methodology is conceptually simpler than Bayesian calibration approaches, and is straightforward to implement. The methodology is demonstrated and analyzed in this paper on several illustrative calibration and uncertainty propagation problems.

Many companies rely on user experience metrics, such as Net Promoter scores, to monitor changes in customer attitudes toward their products. This paper suggests that similar metrics can be used to assess the user experience of the pilots and sensor operators who are tasked with using our radar, EO/IR, and other remote sensing technologies. As we have previously discussed, the problem of making our national security remote sensing systems useful, usable and adoptable is a human-system integration problem that does not get the sustained attention it deserves, particularly given the high-throughput, information-dense task environments common to military operations. In previous papers, we have demonstrated how engineering teams can adopt well-established human-computer interaction principles to fix significant usability problems in radar operational interfaces. In this paper, we describe how we are using a combination of Situation Awareness design methods, along with techniques from the consumer sector, to identify opportunities for improving human-system interactions. We explain why we believe that all stakeholders in remote sensing-including program managers, engineers, or operational users-can benefit from systematically incorporating some of these measures into the evaluation of our national security sensor systems. We will also provide examples of our own experience adapting consumer user experience metrics in operator-focused evaluation of currently deployed radar interfaces.

We discuss uncertainty quantification in multisensor data integration and analysis, including estimation methods and the role of uncertainty in decision making and trust in automated analytics. The challenges associated with automatically aggregating information across multiple images, identifying subtle contextual cues, and detecting small changes in noisy activity patterns are well-established in the intelligence, surveillance, and reconnaissance (ISR) community. In practice, such questions cannot be adequately addressed with discrete counting, hard classifications, or yes/no answers. For a variety of reasons ranging from data quality to modeling assumptions to inadequate definitions of what constitutes "interesting" activity, variability is inherent in the output of automated analytics, yet it is rarely reported. Consideration of these uncertainties can provide nuance to automated analyses and engender trust in their results. In this work, we assert the importance of uncertainty quantification for automated data analytics and outline a research agenda. We begin by defining uncertainty in the context of machine learning and statistical data analysis, identify its sources, and motivate the importance and impact of its quantification. We then illustrate these issues and discuss methods for data-driven uncertainty quantification in the context of a multi-source image analysis example. We conclude by identifying several specific research issues and by discussing the potential long-term implications of uncertainty quantification for data analytics, including sensor tasking and analyst trust in automated analytics.

Distinguishing whether a signal corresponds to a single source or a limited number of highly overlapping point spread functions (PSFs) is a ubiquitous problem across all imaging scales, whether detecting receptor-ligand interactions in cells or detecting binary stars. Super-resolution imaging based upon compressed sensing exploits the relative sparseness of the point sources to successfully resolve sources which may be separated by much less than the Rayleigh criterion. However, as a solution to an underdetermined system of linear equations, compressive sensing requires the imposition of constraints which may not always be valid. One typical constraint is that the PSF is known. However, the PSF of the actual optical system may reflect aberrations not present in the theoretical ideal optical system. Even when the optics are well characterized, the actual PSF may reflect factors such as non-uniform emission of the point source (e.g. fluorophore dipole emission). As such, the actual PSF may differ from the PSF used as a constraint. Similarly, multiple different regularization constraints have been suggested including the l1-norm, l0-norm, and generalized Gaussian Markov random fields (GGMRFs), each of which imposes a different constraint. Other important factors include the signal-to-noise ratio of the point sources and whether the point sources vary in intensity. In this work, we explore how these factors influence super-resolution image recovery robustness, determining the sensitivity and specificity. As a result, we determine an approach that is more robust to the types of PSF errors present in actual optical systems.

The Defense Advanced Research Project Agency has identified a need for low-standby-power systems which react to physical environmental signals in the form of an electrical wakeup signal. To address this need, we design piezoelectric aluminum nitride based microelectromechanical resonant accelerometers that couple with a near-zero power, complementary metal-oxide-semiconductor application specific integrated circuit. The piezoelectric accelerometer operates near resonance to form a passive mechanical filter of the vibration spectrum that targets a specific frequency signature. Resonant vibration sensitivities as large as 490 V/g (in air) are obtained at frequencies as low as 43 Hz. The integrated circuit operates in the subthreshold regime employing current starvation to minimize power consumption. Two accelerometers are coupled with the circuit to form the wakeup system which requires only 5.25 nW before wakeup and 6.75 nW after wakeup. The system is shown to wake up to a generator signal and reject confusers in the form of other vehicles and background noise.

Microtubules exhibit a dynamic instability between growth and catastrophic depolymerization. GTP-tubulin (αβ-dimer bound to GTP) self-assembles, but dephosphorylation of GTP- to GDP-tubulin within the tubule results in destabilization. While the mechanical basis for destabilization is not fully understood, one hypothesis is that dephosphorylation causes tubulin to change shape, frustrating bonds and generating stress. To test this idea, we perform molecular dynamics simulations of microtubules built from coarse-grained models of tubulin, incorporating a small compression of α-subunits associated with dephosphorylation in experiments. We find that this shape change induces depolymerization of otherwise stable systems via unpeeling "ram's horns" characteristic of microtubules. Depolymerization can be averted by caps with uncompressed α-subunits, i.e., GTP-rich end regions. Thus, the shape change is sufficient to yield microtubule behavior.

We report on progress for increasing the laser-induced damage threshold of dichroic beam combiner coatings for high transmission at 527 nm and high reflection at 1054 nm (22.5° angle of incidence, S-polarization). The initial coating consisted of HfO2 and SiO2 layers deposited with electron beam evaporation, and the laser-induced damage threshold was 7 J/cm2 at 532 nm with 3.5 ns pulses. This study introduces different coating strategies that were utilized to increase the laser damage threshold of this coating to 12.5 J/cm2.

Compressive sensing is a powerful technique for recovering sparse solutions of underdetermined linear systems, which is often encountered in uncertainty quantification analysis of expensive and high-dimensional physical models. We perform numerical investigations employing several compressive sensing solvers that target the unconstrained LASSO formulation, with a focus on linear systems that arise in the construction of polynomial chaos expansions. With core solvers l1_ls, SpaRSA, CGIST, FPC_AS, and ADMM, we develop techniques to mitigate overfitting through an automated selection of regularization constant based on cross-validation, and a heuristic strategy to guide the stop-sampling decision. Practical recommendations on parameter settings for these techniques are provided and discussed. The overall method is applied to a series of numerical examples of increasing complexity, including large eddy simulations of supersonic turbulent jet-in-crossflow involving a 24-dimensional input. Through empirical phase-transition diagrams and convergence plots, we illustrate sparse recovery performance under structures induced by polynomial chaos, accuracy, and computational trade-offs between polynomial bases of different degrees, and practicability of conducting compressive sensing for a realistic, high-dimensional physical application. Across test cases studied in this paper, we find ADMM to have demonstrated empirical advantages through consistent lower errors and faster computational times.

The mechanism by which aerodynamic effects of jet/fin interaction arise from the flow structure of a jet in crossflow is explored using particle image velocimetry measurements of the crossplane velocity field as it impinges on a downstream fin instrumented with high-frequency pressure sensors. A Mach 3.7 jet issues into a Mach 0.8 crossflow from either a normal or inclined nozzle, and three lateral fin locations are tested. Conditional ensemble-averaged velocity fields are generated based upon the simultaneous pressure condition. Additional analysis relates instantaneous velocity vectors to pressure fluctuations. The pressure differential across the fin is driven by variations in the spanwise velocity component, which substitutes for the induced angle of attack on the fin. Pressure changes at the fin tip are strongly related to fluctuations in the streamwise velocity deficit, wherein lower pressure is associated with higher velocity and vice versa. The normal nozzle produces a counter-rotating vortex pair that passes above the fin, and pressure fluctuations are principally driven by the wall horseshoe vortex and the jet wake deficit. The inclined nozzle produces a vortex pair that impinges the fin and yields stronger pressure fluctuations driven more directly by turbulence originating from the jet mixing.

Multiple physical time-scales can arise in electromagnetic simulations when dissipative effects are introduced through boundary conditions, when currents follow external time-scales, and when material parameters vary spatially. In such scenarios, the time-scales of interest may be much slower than the fastest time-scales supported by the Maxwell equations, therefore making implicit time integration an efficient approach. The use of implicit temporal discretizations results in linear systems in which fast time-scales, which severely constrain the stability of an explicit method, can manifest as so-called stiff modes. This study proposes a new block preconditioner for structure preserving (also termed physics compatible) discretizations of the Maxwell equations in first order form. The intent of the preconditioner is to enable the efficient solution of multiple-time-scale Maxwell type systems. An additional benefit of the developed preconditioner is that it requires only a traditional multigrid method for its subsolves and compares well against alternative approaches that rely on specialized edge-based multigrid routines that may not be readily available. Results demonstrate parallel scalability at large electromagnetic wave CFL numbers on a variety of test problems.

Ceramic fiber insulation materials, such as Fiberfrax and Min-K products, are used in a number of applications (e.g. aerospace, fire protection, and military) for their stability and performance in extreme conditions. However, the thermal properties of these materials have not been thoroughly characterized for many of the conditions that they will be exposed to, such as high temperatures and pressures. This complicates the design of systems using these insulations as the uncertainty in the thermal properties is high. In this study, the thermal conductivity of three ceramic fiber insulations, Fiberfrax T-30LR laminate, Fiberfrax 970-H paper, and Min-K TE1400 board, was measured as a function of atmospheric temperature and compression. Measurements were taken using the transient plane source technique. The results of this study are compared against three published data sets.

Drinking water systems face multiple challenges, including aging infrastructure, water quality concerns, uncertainty in supply and demand, natural disasters, environmental emergencies, and cyber and terrorist attacks. All of these incidents have the potential to disrupt a large portion of a water system causing damage to critical infrastructure, threatening human health, and interrupting service to customers. Recent incidents, including the floods and winter storms in the southern United States, highlight vulnerabilities in water systems and the need to minimize service loss. Simulation and analysis tools can help water utilities better understand how their system would respond to a wide range of disruptive incidents and inform planning to make systems more resilient over time. The Water Network Tool for Resilience (WNTR) is a new open source Python package designed to meet this need. WNTR integrates hydraulic and water quality simulation, a wide range of damage and response options, and resilience metrics into a single software framework, allowing for end-Toend evaluation of water network resilience. WNTR includes capabilities to 1) generate and modify water network structure and operations, 2) simulate disaster scenarios, 3) model response and repair strategies, 4) simulate pressure dependent demand and demand-driven hydraulics, 5) simulate water quality, 6) calculate resilience metrics, and 7) visualize results. These capabilities can be used to evaluate resilience of water distribution systems to a wide range of hazards and to prioritize resilience-enhancing actions. Furthermore, the flexibility of the Python environment allows the user to easily customize analysis. For example, utilities can simulate a specific incident or run stochastic analysis for a range of probabilistic scenarios. The U.S. Environmental Protection Agency and Sandia National Laboratories are working with water utilities to ensure that WNTR can be used to efficiently evaluate resilience under different use cases. The software has been used to evaluate resilience under earthquake and power outage scenarios, run fire-fighting capacity and pipe criticality analysis, evaluate sampling and flushing locations, and prioritize repair strategies. This paper includes discussion on WNTR capabilities, use cases, and resources to help get new users started using the software. WNTR can be downloaded from the U.S. Environmental Protection Agency GitHub site at https://github.com/USEPA/WNTR. The GitHub site includes links to software documentation, software testing results, and contact information.

Data movement is considered the main performance concern for exascale, including both on-node memory and off-node network communication. Indeed, many application traces show significant time spent in MPI calls, potentially indicating that faster networks must be provisioned for scalability. However, equating MPI times with network communication delays ignores synchronization delays and software overheads independent of network hardware. Using point-to-point protocol details, we explore the decomposition of MPI time into communication, synchronization and software stack components using architecture simulation. Detailed validation using Bayesian inference is used to identify the sensitivity of performance to specific latency/bandwidth parameters for different network protocols and to quantify associated uncertainties. The inference combined with trace replay shows that synchronization and MPI software stack overhead are at least as important as the network itself in determining time spent in communication routines.

The solution of the Optimal Power Flow (OPF) and Unit Commitment (UC) problems (i.e., determining generator schedules and set points that satisfy demands) is critical for efficient and reliable operation of the electricity grid. For computational efficiency, the alternating current OPF (ACOPF) problem is usually formulated with a linearized transmission model, often referred to as the DCOPF problem. However, these linear approximations do not guarantee global optimality or even feasibility for the true nonlinear alternating current (AC) system. Nonlinear AC power flow models can and should be used to improve model fidelity, but successful global solution of problems with these models requires the availability of strong relaxations of the AC optimal power flow constraints. In this paper, we use McCormick envelopes to strengthen the well-known second-order cone (SOC) relaxation of the ACOPF problem. With this improved relaxation, we can further include tight bounds on the voltages at the reference bus, and this paper demonstrates the effectiveness of this for improved bounds tightening. We present results on the optimality gap of both the base SOC relaxation and our Strengthened SOC (SSOC) relaxation for the National Information and Communications Technology Australia (NICTA) Energy System Test Case Archive (NESTA). For the cases where the SOC relaxation yields an optimality gap more than 0.1 %, the SSOC relaxation with bounds tightening further reduces the optimality gap by an average of 67 % and ultimately reduces the optimality gap to less than 0.1 % for 58 % of all the NESTA cases considered. Stronger relaxations enable more efficient global solution of the ACOPF problem and can improve computational efficiency of MINLP problems with AC power flow constraints, e.g., unit commitment.

Real-time energy pricing has caused a paradigm shift for process operations with flexibility becoming a critical driver of economics. As such, incorporating real-time pricing into planning and scheduling optimization formulations has received much attention over the past two decades (Zhang and Grossman, 2016). These formulations, however, focus on 1-hour or longer time discretizations and neglect process dynamics. Recent analysis of historical price data from the California electricity market (CAISO) reveals that a majority of economic opportunities come from fast market layers, i.e., real-time energy market and ancillary services (Dowling et al., 2017). We present a dynamic optimization framework to quantify the revenue opportunities of chemical manufacturing systems providing frequency regulation (FR). Recent analysis of first order systems finds that slow process dynamics naturally dampen high frequency harmonics in FR signals (Dowling and Zavala, 2017). As a consequence, traditional chemical processes with long time constants may be able to provide fast flexibility without disrupting product quality, performance of downstream unit operations, etc. This study quantifies the ability of a distillation system to provide sufficient dynamic flexibility to adjust energy demands every 4 seconds in response to market signals. Using a detailed differential algebraic equation (DAE) model (Hahn and Edgar, 2002) and historic data from the Texas electricity market (ECROT), we estimate revenue opportunities for different column designs. We implement our model using the algebraic modeling language Pyomo (Hart et al., 2011) and its dynamic optimization extension Pyomo.DAE (Nicholson et al., 2017). These software packages enable rapid development of complex optimization models using high-level modelling constructs and provide flexible tools for initializing and discretizing DAE models.

The fluid injection into deep geological formations altar the states of pore pressure and stress on the faults, potentially causing earthquakes. In the multiphase flow system, the interaction between fluid flow and mechanical deformation in porous media is critical to determine the spatio-temporal distribution of pore pressure and stress. The contrast of fluid and rock properties between different structures produces the changes in pressure gradients and subsequently stress fields. Assuming two-phase fluid flow (gas-water system), we simulate the two-dimensional reservoir including a basement fault, in which injection-induced pressure encounters the fault directly given injection scenarios. The single-phase flow model with the same setting is also conducted to evaluate the multiphase flow effects on mechanical response of the fault to gas injection. A series of sensitivity tests are performed by varying the fault permeability. The presence of gaseous phase reduces the pressure buildup within the gas-saturated region, causing less Coulomb stress change. The low-permeability fault prevent diffusion initially as observed in the single-phase flow system. Once gaseous phase approaches, the fault acts as a capillary barrier that causes increases in pressure within the fault zone, potentially inducing earthquakes even without direct diffusion.

Turbulent viscosities have been calculated from stereoscopic particle image velocimetry (PIV) data for a supersonic jet exhausting into a transonic crossflow. Image interrogation must be optimized to produce useful turbulent viscosity fields. High-accuracy image reconstruction should be used for the final iteration, whereas efficient algorithms produce spatial artifacts in derivative fields. Mean strain rates should be calculated from large windows (128 pixel) with 75% overlap. Turbulent stresses are optimally computed using multiple (more than two) iterations of image interrogation and 75% overlap, both of which increase the signal bandwidth. However, the improvement is modest and may not justify the considerable increase in computational expense. The turbulent viscosity may be expressed in tensor notation to include all three axes of velocity data. In this formulation, a least-squares fit to the multiple equations comprising the tensor generated a scalar turbulent viscosity that eliminated many of the artifacts and noise present in the single-component formulation. The resulting experimental turbulent viscosity fields will be used to develop data-driven turbulence models that can improve the fidelity of predictive computations.

The thermal environment generated during an intense radiation event like a nuclear weapon airburst, lightning strike, or directed energy weaponry has a devastating effect on many exposed materials. Natural and engineered materials can be damaged and ignite from the intense thermal radiation, potentially resulting in sustained fires. Understanding material behavior in such an event is essential for mitigating the damage to a variety of defense systems, such as aircraft and weaponry. Flammability and ignition studies in this regime (very high heat flux, short duration) are less plentiful than in the heat flux regimes representative of typical fires. The flammability and ignition behavior of a material may differ at extreme heat flux due to the balance of the heat conduction into the material compared to other processes. Length scale effects may also be important in flammability and ignition behavior, especially in the high heat flux regime. A variety of materials have recently been subjected to intense thermal loads (~100–1000 kW/m2) in testing at both the Solar Furnace and the Solar Tower at the National Solar Thermal Test Facility at Sandia National Laboratories. The Solar Furnace, operating at a smaller scale (≈30 cm2 area), provides the ability to test a wide range of materials under controlled radiative flux conditions. The Solar Tower exposes objects and materials to the same flux on a much larger scale (≈4 m2 area), integrating complex geometry and scale effects. Results for a variety of materials tested in both facilities are presented and compared. Material response often differs depending on scale, suggesting a significant scale effect. Mass loss per unit energy tends to go down as scale increases, and ignition probability tends to increase with scale.

This study demonstrates a systematic methodology for establishing the design loads of a wave energy converter. The proposed design load methodology incorporates existing design guidelines, where they exist, and follows a typical design progression; namely, advancing from many, quick, order-ofmagnitude accurate, conceptual stage design computations to a few, computationally intensive, high-fidelity, design validation simulations. The goal of the study is to streamline and document this process based on quantitative evaluations of the design loads' accuracy at each design step and consideration for the computational efficiency of the entire design process. For the wave energy converter, loads, and site conditions considered, this study demonstrates an efficient and accurate methodology of evaluating the design loads.

This paper presents an evaluation of alternative particle heat-exchanger designs, including moving packed-bed and fluidized-bed designs, for high-temperature heating of a solardriven supercritical CO2 (sCO2) Brayton power cycle. The design requirements for high pressure (> 20 MPa) and high temperature (> 700 °C) operation associated with sCO2 posed several challenges requiring high-strength materials for piping and/or diffusion bonding for plates. Designs from several vendors for a 100 kW-thermal particle-to-sCO2 heat exchanger were evaluated as part of this project. Cost, heat-transfer coefficient, structural reliability, manufacturability, parasitics and heat losses, scalability, compatibility, erosion and corrosion, transient operation, and inspection ease were considered in the evaluation. An analytical hierarchy process was used to weight and compare the criteria for the different design options. The fluidized-bed design fared the best on heat transfer coefficient, structural reliability, scalability and inspection ease, while the moving packed-bed designs fared the best on cost, parasitics and heat losses, manufacturability, compatibility, erosion and corrosion, and transient operation. A 100 kWt shell-and-plate design was ultimately selected for construction and integration with Sandia's falling particle receiver system.

Nuclear weapon airbursts can create extreme radiative heat fluxes for a short duration. The radiative heat transfer from the fireball can damage and ignite materials in a region that extends beyond the zone damaged by the blast wave itself. Directed energy weapons also create extreme radiative heat fluxes. These scenarios involve radiative fluxes much greater than the environments typically studied in flammability and ignition tests. Furthermore, the vast majority of controlled experiments designed to obtain material response and flammability data at high radiative fluxes have been performed at relatively small scales (order 10 cm2 area). A recent series of tests performed on the Solar Tower at the National Solar Thermal Test Facility exposed objects and materials to fluxes of 100 – 2,400 kW/m2 at a much larger scale (≈1 m2 area). This paper provides an overview of testing performed at the Solar Tower for a variety of materials including aluminum, fabric, and two types of plastics. Tests with meter-scale objects such as tires and chairs are also reported, highlighting some potential effects of geometry that are difficult to capture in small-scale tests. The aluminum sheet melted at the highest heat flux tested. At the same flux, the tire ignited but the flames were not sustained when the external heat flux was removed; the damage appeared to be limited to the outer portion of the tire, and internal pressure was maintained.

A new approach to denoising Time-Resolved Particle Image Velocimetry data is proposed by incorporating measurement uncertainties estimated using the correlation statistics method. The denoising algorithm of Oxlade et al (Experiments in Fluids, 2012) has been modified to add the frequency dependence of PIV noise by obtaining it from the uncertainty estimates, including the correlated term between velocity and uncertainty that is zero only if white noise is assumed. Although the present approach was only partially effective in denoising the 400-kHz “postage-stamp PIV” data, important and novel insights were obtained into the behavior of PIV uncertainty. The belief that PIV noise is white noise has been shown to be inaccurate, though it may serve as a reasonable approximation for measurements with a high dynamic range. Noise spectra take a similar shape to the velocity spectra because increased velocity fluctuations correspond to higher shear and therefore increased uncertainty. Coherence functions show that correlation between velocity fluctuations and uncertainty is strongest at low and mid frequencies, tapering to a much weaker correlation at high frequencies where turbulent scales are small with lower shear magnitudes.

We use broken symmetry III-V semiconductor Fano metasurfaces to substantially improve the efficiency of second-harmonic generation (SHG) in the near infrared, compared to SHG obtained from metasurfaces created using symmetrical Mie resonators.

In this work, we experimentally demonstrate simultaneous occurrence of second-,third-, fourth-harmonic generation, sum-frequency generation, four-wave mixing and six-wave mixing processes in III-V semiconductor metasurfaces with spectra spanning from the UV to the near-IR.

The radiation effects community embraces the importance of quantifying uncertainty in model predictions and the importance of propagating this uncertainty into the integral metrics used to validate models, but they are not always aware of the importance of addressing the energy- and reaction-dependent correlations in the underlying uncertainty contributors. This paper presents a rigorous high-fidelity Total Monte Carlo approach that addresses the correlation in the underlying uncertainty components and quantifies the role of both energy and reaction-dependent correlations in a sample application that addresses the damage metrics relevant to silicon semiconductors.

Anomaly detection is an important problem in various fields of complex systems research including image processing, data analysis, physical security and cybersecurity. In image processing, it is used for removing noise while preserving image quality, and in data analysis, physical security and cybersecurity, it is used to find interesting data points, objects or events in a vast sea of information. Anomaly detection will continue to be an important problem in domains intersecting with “Big Data”. In this paper we provide a novel algorithm for anomaly detection that uses phase-coded spiking neurons as basic computational elements.

Economic barriers to the replacement of bridges and other civil structures have created an aging infrastructure and placed greater demands on the deployment of effective and rapid health monitoring methods. To gain access for inspections, structure and sealant must be removed, disassembly processes must be completed and personnel must be transported to remote locations. Reliable Structural Health Monitoring (SHM) systems can automatically process data, assess structural condition, and signal the need for specific maintenance actions. They can reduce the costs associated with the increasing maintenance and surveillance needs of aging structures. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location of hidden damage, and the isolated location of the structure. Furthermore, prevention of unexpected flaw growth and structural failure could be improved if on-board SHM systems were used to regularly, or even continuously, assess structural integrity. A research program was completed to develop and validate Comparative Vacuum Monitoring (CVM) sensors for crack detection. Sandia National Labs, in conjunction with private industry and the U.S. Department of Transportation, completed a series of CVM validation and certification programs aimed at establishing the overall viability of these sensors for monitoring bridge structures. Factors that affect SHM sensitivity include flaw size, shape, orientation and location relative to the sensors, along with operational environments. Statistical methods using one-sided tolerance intervals were employed to derive Probability of Flaw Detection (POD) levels for typical application scenarios. Complimentary, multi-year field tests were also conducted to study the deployment and long-term operation of CVM sensors on aircraft and bridges. This paper presents the quantitative crack detection capabilities of the CVM sensor, its performance in actual operating environments, and the prospects for structural health monitoring applications on a wide array of civil structures.

Structural dynamic models of mechanical, aerospace, and civil structures often involve connections of multiple subcomponents with rivets, bolts, press fits, or other joining processes. Recent model order reduction advances have been made for jointed structures using appropriately defined whole joint models in combination with linear substructuring techniques. A whole joint model condenses the interface nodes onto a single node with multi-point constraints resulting in drastic increases in computational speeds to predict transient responses. One drawback to this strategy is that the whole joint models are empirical and require calibration with test or high-fidelity model data. A new framework is proposed to calibrate whole joint models by computing global responses from high-fidelity finite element models and utilizing global optimization to determine the optimal joint parameters. The method matches the amplitude dependent damping and natural frequencies predicted for each vibration mode using quasi-static modal analysis.

Flow maldistribution in microchannel heat exchanger(MCHEs) can negatively impact heat exchanger effectiveness.Several rules of thumb exist about designing for uniform flow,but very little data are published to support these claims. In thiswork, complementary experiments and computational fluiddynamics (CFD) simulations of MCHEs enable a solidunderstanding of flow uniformity to a higher level of detail thanpreviously seen. Experiments provide a validation data source toassess CFD predictive capability. The traditional semi-circularheader geometry is tested. Experiments are carried out in a clearacrylic MCHE and water flow is measured optically with particleimage velocimetry. CFD boundary conditions are matched tothose in the experiment and the outputs, specifically velocity andturbulent kinetic energy profiles, are compared.

Self-assembled giant polymer vesicles prepared from double-hydrophilic diblock copolymers, poly(ethylene oxide)-b-poly(acrylic acid) (PEO-PAA) show significant degradation in response to pH changes. Because of the switching behavior of the diblock copolymers at biologically-relevant pH environments (2 to 9), these polymer vesicles have potential biomedical applications as smart delivery vehicles.

Performance modeling of networks through simulation requires application endpoint models that inject traffic into the simulation models. Endpoint models today for system-scale studies consist mainly of post-mortem trace replay, but these off-line simulations may lack flexibility and scalability. On-line simulations running so-called skeleton applications run reduced versions of an application that generate traffic that is the same or similar to the full application. These skeleton apps have advantages for flexibility and scalability, but they often must be custom written for the simulator itself. Auto-skeletonization of existing application source code via compiler tools would provide endpoint models with minimal development effort. These source-to-source transformations have been only narrowly explored. We introduce a pragma language and corresponding Clang-driven source-to-source compiler that performs auto-skeletonization based on provided pragma annotations. We describe the compiler toolchain, validate the generated skeletons, and show scalability of the generated simulation models beyond 100Â K endpoints for example MPI applications. Overall, we assert that our proposed auto-skeletonization approach and the flexible skeletons it produces can be an important tool in realizing balanced exascale interconnect designs.

Advanced friction models are often mathematically defined as nonlinear differential equations or complicated algebraic operations acting in single degree-of-freedom systems; however, such simplified conditions are not relevant to most design applications. As a result, current designers of practical structures typically simplify friction modeling to classical, Coulomb-like descriptions. In order to be viable for design purposes, friction models must be applicable to realistic structures and available in standard commercial codes. The goal of this work is to implement several different friction models into the commercial code, Abaqus, as user-defined contact models and to explore their properties in a dynamic simulation. A verification problem of interest to the joints community is utilized to evaluate efficacy. Several output quantities of the model will be presented and discussed, including frictional energy dissipation, amplitude, and frequency. The selected results are comparable to commonly observed experimental phenomena in mechanics of jointed structures.

This paper details the development and validation of a numerical model of the Wavestar wave energy converter (WEC) developed in WEC-Sim. This numerical model was developed in support of the WEC Control Competition (WECCCOMP), a competition with the objective of maximizing WEC performance over costs through innovative control strategies. WECCCOMP has two stages: numerical implementation of control strategies, and experimental implementation. The work presented in this paper is for support of the numerical implementation, where contestants are provided a WEC-Sim model of the 1:20 scale Wavestar device to develop their control algorithms. This paper details the development of the numerical model in WEC-Sim and of its validation through comparison to experimental data.

Modern supercomputers are shared among thousands of users running a variety of applications. Knowing which applications are running in the system can bring substantial benefits: knowledge of applications that intensively use shared resources can aid scheduling; unwanted applications such as cryptocurrency mining or password cracking can be blocked; system architects can make design decisions based on system usage. However, identifying applications on supercomputers is challenging because applications are executed using esoteric scripts along with binaries that are compiled and named by users. This paper introduces a novel technique to identify applications running on supercomputers. Our technique, Taxonomist, is based on the empirical evidence that applications have different and characteristic resource utilization patterns. Taxonomist uses machine learning to classify known applications and also detect unknown applications. We test our technique with a variety of benchmarks and cryptocurrency miners, and also with applications that users of a production supercomputer ran during a 6 month period. We show that our technique achieves nearly perfect classification for this challenging data set.

In modern shared-memory NUMA systems which typically consist of two or more multi-core processor packages with local memory, affinity of data to computation is crucial for achieving high performance with an OpenMP program. OpenMP* 3.0 introduced support for task-parallel programs in 2008 and has continued to extend its applicability and expressiveness. However, the ability to support data affinity of tasks is missing. In this paper, we investigate several approaches for task-to-data affinity that combine locality-aware task distribution and task stealing. We introduce the task affinity clause that will be part of OpenMP 5.0 and provide the reasoning behind its design. Evaluation with our experimental implementation in the LLVM OpenMP runtime shows that task affinity improves execution performance up to 4.5x on an 8-socket NUMA machine and significantly reduces runtime variability of OpenMP tasks. Our results demonstrate that a variety of applications can benefit from task affinity and that the presented clause is closing the gap of task-to-data affinity in OpenMP 5.0.

Experimental hydrology data from the Mizunami Underground Research Laboratory in Central Japan have been used to develop a site-scale fracture model and a flow model for the study area. The discrete fracture network model was upscaled to a continuum model to be used in flow simulations. A flow model was developed centered on the research tunnel, and using a highly refined regular mesh. In this study development and utilization of the model is presented. The modeling analysis used permeability and porosity fields from the discrete fracture network model as well as a homogenous model using fixed values of permeability and porosity. The simulations were designed to reproduce hydrology of the modeling area and to predict inflow of water into the research tunnel during excavation. Modeling results were compared with the project hydrology data. Successful matching of the experimental data was obtained for simulations based on the discrete fracture network model.

Abstract not provided.

Developing sound methods to evaluate risk of seabed mobility and alteration of sediment transport patterns in the near-shore coastal regions due to the presence of Offshore Wind (OW) infrastructure is critical to project planning, permitting, and operations. OW systems may include seafloor foundations, cabling, floating structures with gravity anchors, or a combination of several of these systems. Installation of these structures may affect the integrity of the sediment bed, thus affecting seabed dynamics and stability. It is therefore necessary to evaluate hydrodynamics and seabed dynamics and the effects of OW subsea foundations and cables on sediment transport. A methodology is presented here to map a site's sediment (seabed) stability and can in turn support the evaluation of the potential for these processes to affect OW deployments and the local ecology. Sediment stability risk maps are developed for a site offshore of Central Oregon. A combination of geophysical site characterization, metocean analysis, and numerical modeling is used to develop a quantitative assessment of local scour and overall seabed stability. The findings generally show the presence of structures reduces the sediment transport in the lee area of the array by altering current and wave fields. The results illustrate how the overall regional patterns of currents and waves influence local scour near pilings and cables.

Abstract not provided.

The Mizunami Underground Research Laboratory is located in the Tono area (Central Japan). Its main purpose is providing a scientific basis for the research and development of technologies needed for deep geological disposal of radioactive waste in fractured crystalline rocks. The current work is focused on the experiments in the research tunnel (500 m depth). The collected tunnel and borehole data were shared with the participants of DEvelopment of COupled models and their VALidation against EXperiments (DECOVALEX) project. This study describes how these data were used to (1) develop the fracture model of the granite rocks around the research tunnel and (2) validate the model.

A new time domain electric field integral equation is proposed to solve low frequency problems. This new formulation uses the current and charge densities as unknowns, with a form of the continuity equation that is weighted by a Green's function as a second constraining equation. This equation can be derived from a scalar potential equivalence principle integral equation, which is in contrast to the traditional strong form of the continuity equation that has been used in an ad-hoc manner in the augmented EFIE. Numerical results demonstrate the improved stability of this approach, as well as the accuracy at low frequencies.

Combustion of aluminum droplets in solid rocket propellants is studied using laser diagnostic techniques. The time-resolved droplet velocity, temperature, and size are measured using high speed digital in-line holography and imaging pyrometry at 20 kHz.

Femtosecond coherent anti-Stokes Raman scattering thermometry in a solid-fuel propellant flame is demonstrated by tuning the lasers to the rovibrational Raman transitions of diatomic hydrogen (H<inf>2</inf>).

The surface topology of a solid subjected to destructive environments is often difficult to quantify. In thermal environments, the size and shape of the solid changes as it pyrolyzes, ablates, warps, or chars. Quantitative descriptions of such responses are valuable for data reporting and model validation. In this work, a three-dimensional scanner is evaluated for non-destructive material analysis. The scans spatially resolve the response of materials to a high-heat-flux environment. To account for the effect of distortion induced in thin materials, back-side scans of the sample are used to characterize the displacement of the bulk material. Data spanning the area of the sample, rather than using a net or average quantity, enhances the evaluation of the crater formed by the incident flux. The 3D reconstruction of the sample also provides the ability to perform volumetric calculations. The data obtained from this methodology may be useful for characterizing materials exposed to a variety of destructive environments.

Intense, dynamic radiant heat loads damage and ignite many common materials, but are outside the scope of typical fire studies. Explosive, directed-energy, and nuclear-weapon environments subject materials to this regime of extreme heating. The Solar Furnace at the National Solar Test Facility simulated this environment for an extensive experimental study on the response of many natural and engineered materials. Solar energy was focused onto a spot (∼10 cm2 area) in the center of the tested materials, generating an intense radiant load (∼100 kW m−2 –1000 kW m−2) for approximately 3 seconds. Using video photography, the response of the material to the extreme heat flux was carefully monitored. The initiation time of various events was monitored, including charring, pyrolysis, ignition, and melting. These ignition and damage thresholds are compared to historical ignition results predominantly for black, α-cellulose papers. Reexamination of the historical data indicates ignition behavior is predicted from simplified empirical models based on thermal diffusion. When normalized by the thickness and the thermal properties, ignition and damage thresholds exhibit comparable trends across a wide range of materials. This technique substantially reduces the complexity of the ignition problem, improving ignition models and experimental validation.

Process-induced residual stresses occur in composite structures composed of dissimilar materials. As these residual stresses could result in fracture, their consideration when designing composite parts is necessary. However, the experimental determination of residual stresses in prototype parts can be time and cost prohibitive. Alternatively, it is possible for computational tools to predict potential residual stresses. Therefore, a process modeling methodology was developed and implemented into Sandia National Laboratories' SIERRA/Solid Mechanics code. This method requires the specification of many model parameters to form accurate predictions. These parameters, which are related to the mechanical and thermal behaviors of the modeled composite material, can be determined experimentally, but at a potentially prohibitive cost. Furthermore, depending upon a composite part's specific geometric and manufacturing process details, it is possible that certain model parameters may have an insignificant effect on the simulated prediction. Therefore, to streamline the material characterization process, formal parameter sensitivity studies can be applied to determine which of the required input parameters are truly relevant to the simulated prediction. Then, only those model parameters found to be critical will require rigorous experimental characterization. Numerous sensitivity analysis methods exist in the literature, each offering specific strengths and weaknesses. Therefore, the objective of this study is to compare the performance of several accepted sensitivity analysis methods during the simulation of a bi-material composite strip's manufacturing process. The examined sensitivity analysis methods include both simple techniques, such Monte Carlo and Latin Hypercube sampling, as well as more sophisticated approaches, such as the determination of Sobol indices via a polynomial chaos expansion or a Gaussian process. The relative computational cost and critical parameter list are assessed for each of the examined methods and conclusions are drawn regarding the ideal sensitivity analysis approach for future residual stress investigations.

The thermal environment generated during an intense radiation event like a nuclear weapon airburst, lightning strike, or directed energy weaponry has a devastating effect on many exposed materials. Natural and engineered materials can be damaged and ignite from the intense thermal radiation, potentially resulting in sustained fires. Understanding material behavior in such an event is essential for mitigating the damage to a variety of defense systems, such as aircraft and weaponry. Flammability and ignition studies in this regime (very high heat flux, short duration) are less plentiful than in the heat flux regimes representative of typical fires. The flammability and ignition behavior of a material may differ at extreme heat flux due to the balance of the heat conduction into the material compared to other processes. Length scale effects may also be important in flammability and ignition behavior, especially in the high heat flux regime. A variety of materials have recently been subjected to intense thermal loads (~100–1000 kW/m²) in testing at both the Solar Furnace and the Solar Tower at the National Solar Thermal Test Facility at Sandia National Laboratories. The Solar Furnace, operating at a smaller scale (≈30 cm² area), provides the ability to test a wide range of materials under controlled radiative flux conditions. The Solar Tower exposes objects and materials to the same flux on a much larger scale (≈4 m² area), integrating complex geometry and scale effects. Results for a variety of materials tested in both facilities are presented and compared. Material response often differs depending on scale, suggesting a significant scale effect. Mass loss per unit energy tends to go down as scale increases, and ignition probability tends to increase with scale.

Containment bypass scenarios in nuclear power plants can lead to large early release of radionuclides. A residual heat removal (RHR) system interfacing system loss of coolant accident (ISLOCA) has the potential to cause a hazardous environment in the auxiliary building, a loss of coolant from the primary system, a pathway for early release of radionuclides, and the failure of a system important to safely shutting down the plant. Prevention of this accident sequence relies on active systems that may be vulnerable to cyber failures in new or retrofitted plants with digital instrumentation and control systems. RHR ISLOCA in a hypothetical pressurized water reactor is analyzed in a dynamic framework to evaluate the time-dependent effects of various uncertainties on the state of the nuclear fuel, the auxiliary building environment, and the release of radionuclides. The ADAPT dynamic event tree code is used to drive both the MELCOR severe accident analysis code and the RADTRAD dose calculation code to track the progression of the accident from the initiating event to its end states. The resulting data set is then mined for insights into key events and their impacts on the final state of the plant and radionuclide releases.

Sodium Fast Reactors (SFRs) have an extensive operational history that can be leveraged to accelerate the licensing process for modern designs. Sandia National Laboratories (SNL) has recently reconstituted the United States SFR data from the Centralized Reliability Database Organization (CREDO) into a new modern database called the Sodium (Na) System Component Reliability Database (NaSCoRD). This new database is currently undergoing validation and usability testing to better understand the strengths and limitations of this historical data. The most common class of equipment found in the NaSCoRD database are valves. NaSCoRD contains a record of over 4,000 valves that have operated in EBR-II, FFTF, and test loops including those operated by Westinghouse and the Energy Technology Engineering Center. Valve failure events in NaSCoRD can be categorized by working fluid (e.g., sodium, water, gas), valve type (e.g., butterfly, check, throttle, block), failure mode (e.g., failure to open, failure to close, rupture), operating facility, operating temperature, or other user defined categories. Sodium valve reliability estimates will be presented in comparison to estimates provided in historical studies. The impacts of EG&G Idaho’s suggested corrections and various prior distributions on these reliability estimates will also be presented.

Metamaterials provide a means to tailor the spectral response of a surface. Given the periodic nature of the metamaterial, proper design of the unit cell requires intimate knowledge of the parameter space for each design variable. We present a detailed study of the parameter space surrounding vertical split-ring resonators and planar split-ring resonators, and demonstrate widening of the perfect absorption bandwidth based on the understanding of its parameter space.

This paper describes the ongoing study of nuclear facility safety enhancement using Sandia National Laboratories’ (SNL) computer codes, supported by U.S. Department of Energy (DOE) Nuclear Safety Research and Development (NSR&D) Program. Continued DOE NSR&D support, since 2014 has allowed the use of the SNL engineering code suite (SIERRA Mechanics) to further substantiate data in the DOE Handbook published in 1994: DOE-HDBK-3010-94, “Airborne Release Fractions/Rates and Respirable Fractions for Nonreactor Nuclear Facilities.” The use of SIERRA codes allows for a better understanding of the mechanics, dynamics, chemistry and overall physics of airborne release scenarios. SIERRA codes provide insights into the contributing phenomena of source term releases from events such as liquid fires. The 1994 Handbook documents small-scaled, bench-top and limited experiments involving liquid fires, powder spills, pressurized releases, and mechanical insult-induced fragmentation scenarios. Data recorded from these scenarios has been substantiated using SIERRA solid mechanics and fluid mechanics codes. Data passing among multi-physics SIERRA codes predicted the contaminant release from a drum rupture due to fire even though there is no experimental data available. In the anticipated revision effort of the Handbook by DOE, these computational capabilities could enhance the data in a broader usage and could provide confidence in the safety analysis SIERRA codes can provide the initial source term to be used in the leak path factor (LPF) analyses, which predicts the ST release out of the facility. Typical LPF analysis is done using the MELCOR code, developed at SNL for the U.S. Nuclear Regulatory Commission. Widely used in nuclear reactor applications, MELCOR is a toolbox safety code in the DOE’s central registry for LPF applications. A recent LPF guidance study done by SNL indicated that MELCOR 2.1, along with updated guidance, should replace the obsolete MELCOR 1.8.5 guidance. This new guidance is significantly improved over the previous guidance, utilizing extensive MELCOR validation, including applicable reactor experiments and experiments described in the DOE-HDBK-3010-94 for LPF applications. The latest version of MELCOR should be included in DOE’s central registry, and should be used by safety analysts for LPF analyses.

In this work, we describe new capabilities for the Pyomo.GDP modeling environment, moving beyond classical reformulation approaches to include non-standard reformulations and a new logic-based solver, GDPopt. Generalized Disjunctive Programs (GDPs) address optimization problems involving both discrete and continuous decision variables. For difficult problems, advanced reformulations such as the disjunctive “basic step” to intersect multiple disjunctions or the use of procedural reformulations may be necessary. Complex nonlinear GDP models may also be tackled using logic-based outer approximation. These expanded capabilities highlight the flexibility that Pyomo.GDP offers modelers in applying novel strategies to solve difficult optimization problems.

New modelling and optimization platforms have enabled the creation of frameworks for solution strategies that are based on solving sequences of dynamic optimization problems. This study demonstrates the application of the Python-based Pyomo platform as a basis for formulating and solving Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) problems, which enables fast on-line computations through large-scale nonlinear optimization and Nonlinear Programming (NLP) sensitivity. We describe these underlying approaches and sensitivity computations, and showcase the implementation of the framework with large DAE case studies including tray-by-tray distillation models and Bubbling Fluidized Bed Reactors (BFB).

A Butler-matrix-inspired beam-forming network has been developed to provide phasing for a switched-beam 2×2 element antenna array. The network uses an arrangement of double-box quadrature hybrids to achieve wide instantaneous bandwidth in a small, planar form factor. The planar feed structure has been designed to integrate with an array aperture to form a low-profile array stackup.

An ENUN 32P cask supplied by Equipos Nucleares S.A. (ENSA) was transported 9,600 miles by road, sea, and rail in 2017 in order to collect shock and vibration data on the cask system and surrogate spent fuel assemblies within the cask. The task of examining 101,857 ASCII data files – 6.002 terabytes of data (this includes binary and ASCII files) – has begun. Some results of preliminary analyses are presented in this paper. A total of seventy-seven accelerometers and strain gauges were attached by Sandia National Laboratories (SNL) to three surrogate spent fuel assemblies, the cask basket, the cask body, the transport cradle, and the transport platforms. The assemblies were provided by SNL, Empresa Nacional de Residuos Radiactivos, S.A. (ENRESA), and a collaboration of Korean institutions. The cask system was first subjected to cask handling operations at the ENSA facility. The cask was then transported by heavy-haul truck in northern Spain and shipped from Spain to Belgium and subsequently to Baltimore on two roll-on/roll-off ships. From Baltimore, the cask was transported by rail using a 12- axle railcar to the American Association of Railroads’ Transportation Technology Center, Inc. (TTCI) near Pueblo, Colorado where a series of special rail tests were performed. Data were continuously collected during this entire sequence of multi-modal transportation events. (We did not collect data on the transfer between modes of transportation.) Of particular interest – indeed the original motivation for these tests – are the strains measured on the zirconium-alloy tubes in the assemblies. The strains for each of the transport modes are compared to the yield strength of irradiated Zircaloy to illustrate the margin against rod failure during normal conditions of transport. The accelerometer data provides essential comparisons of the accelerations on the different components of the cask system exhibiting both amplification and attenuation of the accelerations at the transport platforms through the cradle and cask and up to the interior of the cask. These data are essential for modeling cask systems. This paper concentrates on analyses of the testing of the cask on a 12-axle railcar at TTCI.

Sandia National Laboratories (SNL) conducted in the summer of 2017 its third fracture challenge (i.e., the Third Sandia Fracture Challenge or SFC3). The challenge, which was open to the public, asked participants to predict, without foreknowledge of the outcome, the fracture response predictions of an additively manufactured tensile test coupon of moderate geometric complexity when loaded to failure. This paper outlines the approach taken by our team, one of the SNL teams that participated in the challenge, to make a prediction. To do so, we employed a traditional finite element approach coupled with a continuum damage mechanics constitutive model. Constitutive model parameters were determined through a calibration process of the model response with the provided longitudinal and transverse tensile test coupon data. Comparison of model predictions with the challenge coupon test results are presented and general observations gleaned from the exercise are provided.

Fiber reinforced composites are increasingly used in advanced applications due to advantageous qualities including high strength-To-weight ratio. The ability to tailor composite structures to meet specific performance criteria is particularly desirable. In practice designs must often balance multiple objectives with conflicting behavior. Objectives of this work were to optimize lamina orientations of a three-ply carbon fiber reinforced composite structure for the coupled solid mechanics and dynamics considerations of minimizing max principal stress while maximizing fundamental frequency. Two approaches were investigated: Pareto set optimization (PSO), and multi-objective genetic algorithm (MOGA). In PSO, a single objective function is constructed as a weighted sum of multiple objective terms. Multiple weighting sets are evaluated to determine a Pareto set of solutions. MOGA mimics evolutionary principles, where the best design points populate subsequent generations. Instead of weight factors, MOGA uses a domination count that ranks population members. Results showed both methods converged to solutions along the same Pareto front. The PSO method calculated fewer function evaluations, but provided many fewer final data points. At a certain threshold, MOGA provides more solutions with fewer calculations. The PSO method requires more user intervention which may introduce bias, but can largely be run in parallel. In contrast, MOGA generation are evaluated in series. The Pareto front showed the trend of increasing frequency with increasing stress. At the low stress and frequency extreme, the stacking sequence tended toward (45°/90°/45°) with max principal stress located in the inner ply in the hoop direction. At high stress and frequency, the stacking sequences (90°/∗/90°) indicated that the middle ply orientation was less significant. A mesh convergence study and dynamic validation experiments gave confidence to the computational model. Future work will include an uncertainty quantification about selected solutions. The final selected solution will be fabricated and experimental validation testing will be conducted.

A new approach to denoising Time-Resolved Particle Image Velocimetry data is proposed by incorporating measurement uncertainties estimated using the correlation statistics method. The denoising algorithm of Oxlade et al (Experiments in Fluids, 2012) has been modified to add the frequency dependence of PIV noise by obtaining it from the uncertainty estimates, including the correlated term between velocity and uncertainty that is zero only if white noise is assumed. Although the present approach was only partially effective in denoising the 400-kHz “postage-stamp PIV” data, important and novel insights were obtained into the behavior of PIV uncertainty. The belief that PIV noise is white noise has been shown to be inaccurate, though it may serve as a reasonable approximation for measurements with a high dynamic range. Noise spectra take a similar shape to the velocity spectra because increased velocity fluctuations correspond to higher shear and therefore increased uncertainty. Coherence functions show that correlation between velocity fluctuations and uncertainty is strongest at low and mid frequencies, tapering to a much weaker correlation at high frequencies where turbulent scales are small with lower shear magnitudes.

Optimization problems under uncertainty involve making decisions without the full knowledge of the impact the decisions will have and before all the facts relevant to those decisions are known. These problems are common, for example, in process synthesis and design, planning and scheduling, supply chain management, and generation and distribution of electric power. The sources of uncertainty in optimization problems fall into two broad categories: endogenous and exogenous. Exogenous uncertain parameters are realized at a known stage (e.g., time period or decision point) in the problem irrespective of the values of the decision variables. For example, demand is generally considered to be independent of any capacity expansion decisions in process industries, and hence, is regarded as an exogenous uncertain parameter. In contrast, decisions impact endogenous uncertain parameters. The impact can either be in the resolution or in the distribution of the uncertain parameter. The realized values of a Type-I endogenous uncertain parameter are affected by the decisions. An example of this type of uncertainty would be facility protection problem where the likelihood of a facility failing to deliver goods or services after a disruptive event depends on the level of resources allocated as protection to that facility. On the other hand, only the realization times of Type-II endogenous uncertain parameters are affected by decisions. For example, in a clinical trial planning problem, whether a clinical trial is successful or not is only realized after the clinical trial has been completed, and whether the clinical trial is successful or not is not impacted by when the clinical trial is started. There are numerous approaches to modelling and solving optimization problems with exogenous and/or endogenous uncertainty, including (adjustable) robust optimization, (approximate) dynamic programming, model predictive control, and stochastic programming. Stochastic programming is a particularly attractive approach, as there is a straightforward translation from the deterministic model to the stochastic equivalent. The challenge with stochastic programming arises through the rapid, sometimes exponential, growth in the program size as we sample the uncertainty space or increase the number of recourse stages. In this talk, we will give an overview of our research activities developing practical stochastic programming approaches to problems with exogeneous and/or endogenous uncertainty. We will highlight several examples from power systems planning and operations, process modelling, synthesis and design optimization, artificial lift infrastructure planning for shale gas production, and clinical trial planning. We will begin by discussing the straightforward case of exogenous uncertainty. In this situation, the stochastic program can be expressed completely by a deterministic model, a scenario tree, and the scenario-specific parameterizations of the deterministic model. Beginning with the deterministic model, modelers create instances of the deterministic model for each scenario using the scenario-specific data. Coupling the scenario models occurs through the addition of nonanticipativity constraints, equating the stage decision variables across all scenarios that pass through the same stage node in the scenario tree. Modelling tools like PySP (Watson, 2012) greatly simplify the process of composing large stochastic programs by beginning either with an abstract representation of the deterministic model written in Pyomo (Hart, et al., 2017) and scenario data, or a function that will return the deterministic Pyomo model for a specific scenario. PySP automatically can create the extensive form (deterministic equivalent) model from a general representation of the scenario tree. The challenge with large scale stochastic programs with exogenous uncertainty arises through managing the growth of the problem size. Fortunately, there are several well-known approaches to decomposing the problem, both stage-wise (e.g., Benders’ decomposition) and scenario-based (e.g., Lagrangian relaxation or Progressive Hedging), enabling the direct solution of stochastic programs with hundreds or thousands of scenarios. We will then discuss developments in modelling and solving stochastic programs with endogenous uncertainty. These problems are significantly more challenging to both pose and to solve, due to the exponential growth in scenarios required to cover the decision-dependent uncertainties relative to the number of stages in the problem. In this situation, standardized frameworks for expressing stochastic programs do not exist, requiring a modeler to explicitly generate the representations and nonanticipativity constraints. Further, the size of the resulting scenario space (frequently exceeding millions of scenarios) precludes the direct solution of the resulting program. In this case, numerous decomposition algorithms and heuristics have been developed (e.g., Lagrangean decomposition-based algorithms (Tarhan, et al. 2013) or Knapsack-based decomposition Algorithms (Christian and Cremaschi, 2015)).

The Separation and Safeguards Performance Model (SSPM) uses MATLAB/Simulink to provide a tool for safeguards analysis of bulk handling nuclear processing facilities. Models of aqueous and electrochemical reprocessing, enrichment, fuel fabrication, and molten salt reactor facilities have been developed to date. These models are used for designing the overall safeguards system, examining new safeguards approaches, virtually testing new measurement instrumentation, and analyzing diversion scenarios. The key metrics generated by the models include overall measurement uncertainty and detection probability for various material diversion or facility misuse scenarios. Safeguards modeling allows for rapid and cost-effective analysis for Safeguards by Design. The models are currently being used to explore alternative safeguards approaches, including more reliance on process monitoring data to reduce the need for destructive analysis that adds considerable burden to international safeguards. Machine learning techniques are being applied, but these techniques need large amounts of data for training and testing the algorithms. The SSPM can provide that training data. This paper will describe the SSPM and its use for applying both traditional nuclear material accountancy and newer machine learning options.

Drinking water systems face multiple challenges, including aging infrastructure, water quality concerns, uncertainty in supply and demand, natural disasters, environmental emergencies, and cyber and terrorist attacks. All of these incidents have the potential to disrupt a large portion of a water system causing damage to critical infrastructure, threatening human health, and interrupting service to customers. Recent incidents, including the floods and winter storms in the southern United States, highlight vulnerabilities in water systems and the need to minimize service loss. Simulation and analysis tools can help water utilities better understand how their system would respond to a wide range of disruptive incidents and inform planning to make systems more resilient over time. The Water Network Tool for Resilience (WNTR) is a new open source Python package designed to meet this need. WNTR integrates hydraulic and water quality simulation, a wide range of damage and response options, and resilience metrics into a single software framework, allowing for end-Toend evaluation of water network resilience. WNTR includes capabilities to 1) generate and modify water network structure and operations, 2) simulate disaster scenarios, 3) model response and repair strategies, 4) simulate pressure dependent demand and demand-driven hydraulics, 5) simulate water quality, 6) calculate resilience metrics, and 7) visualize results. These capabilities can be used to evaluate resilience of water distribution systems to a wide range of hazards and to prioritize resilience-enhancing actions. Furthermore, the flexibility of the Python environment allows the user to easily customize analysis. For example, utilities can simulate a specific incident or run stochastic analysis for a range of probabilistic scenarios. The U.S. Environmental Protection Agency and Sandia National Laboratories are working with water utilities to ensure that WNTR can be used to efficiently evaluate resilience under different use cases. The software has been used to evaluate resilience under earthquake and power outage scenarios, run fire-fighting capacity and pipe criticality analysis, evaluate sampling and flushing locations, and prioritize repair strategies. This paper includes discussion on WNTR capabilities, use cases, and resources to help get new users started using the software. WNTR can be downloaded from the U.S. Environmental Protection Agency GitHub site at https://github.com/USEPA/WNTR. The GitHub site includes links to software documentation, software testing results, and contact information.

The design of satellites usually includes the objective of minimizing mass due to high launch costs, which is complicated by the need to protect sensitive electronics from the space radiation environment. There is growing interest in automated design optimization techniques to help achieve that objective. Traditional optimization approaches that rely exclusively on response functions (e.g. dose calculations) can be quite expensive when applied to transport problems. Previously we showed how adjoint-based transport sensitivities used in conjunction with gradient-based optimization algorithms can be quite effective in designing mass-efficient electron/proton shields in one-dimensional slab geometries. In this paper we extend that work to two-dimensional Cartesian geometries. This consists primarily of deriving the sensitivities to geometric changes, given a particular prescription for parametrizing the shield geometry. We incorporate these sensitivities into our optimization process and demonstrate their effectiveness in such design calculations.

We describe the three electron-transport algorithms that have been implemented in the ITS Monte Carlo codes. While the underlying cross-section data is similar, each uses a fundamentally unique method, which at a high level are best characterized as condensed history, multigroup, and single scatter. Through a set of comparisons with experimental data and some comparisons of purely numerical results, we discuss various attributes of each of the algorithms and show some of the defects that can affect results.

Anomaly detection is an important problem in various fields of complex systems research including image processing, data analysis, physical security and cybersecurity. In image processing, it is used for removing noise while preserving image quality, and in data analysis, physical security and cybersecurity, it is used to find interesting data points, objects or events in a vast sea of information. Anomaly detection will continue to be an important problem in domains intersecting with “Big Data”. In this paper we provide a novel algorithm for anomaly detection that uses phase-coded spiking neurons as basic computational elements.

Direct kinetic and product studies of Criegee Intermediates reveal insertion and addition mechanisms for multiple co-reactant species. Observation of these highly oxygenated low volatility products indicate the potential role of Criegee Intermediate chemistry in molecular weight growth, and subsequently, secondary organic aerosol formation.

Hybrid composites allow designers to develop efficient structures, which strategically exploit a material's strengths while mitigating possible weaknesses. However, elevated temperature curing processes and exposure to thermally-extreme service environments lead to the development of residual stresses. These stresses form at the hybrid composite's bi-material interfaces, significantly impacting the stress state at the crack tip of any pre-existing flaw within the structure and affecting the probability that small defects will grow into large-scale delaminations. Therefore, in this study, a carbon fiber reinforced composite (CFRP) is co-cured with a glass fiber reinforced composite (GFRP), and the mixed-mode fracture toughness is measured across a wide temperature range (-54°C to +71°C). Upon completion of the testing, the measured results and observations are used to develop high-fidelity finite element models simulating both the formation of residual stresses throughout the composite manufacturing process, as well as the mixed-mode testing of the hybrid composite. The stress fields predicted through simulation assist in understanding the trends observed during the completed experiments. Furthermore, the modeled predictions indicate that failure to account for residual stress effects during the analysis of composite structures could lead to non-conservative structural designs and premature failure.

Decomposition kinetics of stabilised CH2OO and CD2OO Criegee intermediates have been investigated as a function of temperature (450-650 K) and pressure (2-350 Torr) using flash photolysis coupled with time-resolved cavity-enhanced broadband UV absorption spectroscopy. Decomposition of CD2OO was observed to be faster than CH2OO under equivalent conditions. Production of OH radicals following CH2OO decomposition was also monitored using flash photolysis with laser-induced fluorescence (LIF), with results indicating direct production of OH in the v = 0 and v = 1 states in low yields. Master equation calculations performed using the Master Equation Solver for Multi-Energy well Reactions (MESMER) enabled fitting of the barriers for the decomposition of CH2OO and CD2OO to the experimental data. Parameterisations of the decomposition rate coefficients, calculated by MESMER, are provided for use in atmospheric models and implications of the results are discussed. For CH2OO, the MESMER fits require an increase in the calculated barrier height from 78.2 kJ mol-1 to 81.8 kJ mol-1 using a temperature-dependent exponential down model for collisional energy transfer with 〈ΔE〉down = 32.6(T/298 K)1.7 cm-1 in He. The low- and high-pressure limit rate coefficients are k1,0 = 3.2 × 10-4(T/298)-5.81exp(-12770/T) cm3 s-1 and k1,∞ = 1.4 × 1013(T/298)0.06exp(-10010/T) s-1, with median uncertainty of ∼12% over the range of experimental conditions used here. Extrapolation to atmospheric conditions yields k1(298 K, 760 Torr) = 1.1+1.5-1.1 × 10-3 s-1. For CD2OO, MESMER calculations result in 〈ΔE〉down = 39.6(T/298 K)1.3 cm-1 in He and a small decrease in the calculated barrier to decomposition from 81.0 kJ mol-1 to 80.1 kJ mol-1. The fitted rate coefficients for CD2OO are k2,0 = 5.2 × 10-5(T/298)-5.28exp(-11610/T) cm3 s-1 and k2,∞ = 1.2 × 1013(T/298)0.06exp(-9800/T) s-1, with overall error of ∼6% over the present range of temperature and pressure. The extrapolated k2(298 K, 760 Torr) = 5.5+9.2-5.5 × 10-3 s-1. The master equation calculations for CH2OO indicate decomposition yields of 63.7% for H2 + CO2, 36.0% for H2O + CO and 0.3% for OH + HCO with no significant dependence on temperature between 400 and 1200 K or pressure between 1 and 3000 Torr.

Multi-site fatigue damage, hidden cracks in hard-to-reach locations, disbonded joints, erosion, impact, and corrosion are among the major flaws encountered in today's extensive fleet of aging aircraft. The use of in-situ sensors for real-time health monitoring of aircraft structures, coupled with remote interrogation, provides a viable option to overcome inspection impediments stemming from accessibility limitations, complex geometries, and the location and depth of hidden damage. Reliable, Structural Health Monitoring (SHM) systems can automatically process data, assess structural condition, and signal the need for human intervention. Prevention of unexpected flaw growth and structural failure can be improved if on-board health monitoring systems are used to continuously assess structural integrity. Such systems can detect incipient damage before catastrophic failures occurs. Other advantages of on-board distributed sensor systems are that they can eliminate costly and potentially damaging disassembly, improve sensitivity by producing optimum placement of sensors and decrease maintenance costs by eliminating more time-consuming manual inspections. This paper presents the results from successful SHM technology validation efforts that established the performance of sensor systems for aircraft fatigue crack detection. Validation tasks were designed to address the SHM equipment, the health monitoring task, the resolution required, the sensor interrogation procedures, the conditions under which the monitoring will occur, and the potential inspector population. All factors that affect SHM sensitivity were included in this program including flaw size, shape, orientation and location relative to the sensors, operational and environmental variables and issues related to the presence of multiple flaws within a sensor network. This paper will also present the formal certification tasks including formal adoption of SHM systems into aircraft manuals and the release of an Alternate Means of Compliance and a modified Service Bulletin to allow for routine use of SHM sensors on commercial aircraft. This program also established a regulatory approval process that includes FAR Part 25 (Transport Category Aircraft) and shows compliance with 25.571 (fatigue) and 25.1529 (Instructions for Continued Airworthiness).

Charged particles present some unique challenges for radiation transport codes. This is because charged particles have cross sections that are extremely forward peaked, are huge in the limit of small energy transfer, and are highly scattering, which causes slow convergence of the source iterations. The primary application of SCEPTRE is modeling radiation-driven electrical effects, so substantial effort has been invested in SCEPTRE for the efficient modeling of electron transport. This paper will summarize recent and ongoing activities involving the accurate deterministic-transport modeling of charged particles and methods implemented to improve iterative convergence.

Compressive sensing shows promise for sensors that collect fewer samples than required by traditional Shannon-Nyquist sampling theory. Recent sensor designs for hyperspectral imaging encode light using spectral modulators such as spatial light modulators, liquid crystal phase retarders, and Fabry-Perot resonators. The hyperspectral imager consists of a filter array followed by a detector array. It encodes spectra with less measurements than the number of bands in the signal, making reconstruction an underdetermined problem. We propose a reconstruction algorithm for hyperspectral images encoded through spectral modulators. Our approach constrains pixels to be similar to their neighbors in space and wavelength, as natural images tend to vary smoothly, and it increases robustness to noise. It combines L1 minimization in the wavelet domain to enforce sparsity and total variation in the image domain for smoothness. The alternating direction method of multipliers (ADMM) simplifies the optimization procedure. Our algorithm constrains encoded, compressed hyperspectral images to be smooth in their reconstruction, and we present simulation results to illustrate our technique. This work improves the reconstruction of hyperspectral images from encoded, multiplexed, and sparse measurements.

Advancements in machine learning (ML) and deep learning (DL) have enabled imaging systems to perform complex classification tasks, opening numerous problem domains to solutions driven by high quality imagers coupled with algorithmic elements. However, current ML and DL methods for target classification typically rely upon algorithms applied to data measured by traditional imagers. This design paradigm fails to enable the ML and DL algorithms to influence the sensing device itself, and treats the optimization of the sensor and algorithm as separate sequential elements. Additionally, this current paradigm narrowly investigates traditional images, and therefore traditional imaging hardware, as the primary means of data collection. We investigate alternative architectures for computational imaging systems optimized for specific classification tasks, such as digit classification. This involves a holistic approach to the design of the system from the imaging hardware to algorithms. Techniques to find optimal compressive representations of training data are discussed, and most-useful object-space information is evaluated. Methods to translate task-specific compressed data representations into non-traditional computational imaging hardware are described, followed by simulations of such imaging devices coupled with algorithmic classification using ML and DL techniques. Our approach allows for inexpensive, efficient sensing systems. Reduced storage and bandwidth are achievable as well since data representations are compressed measurements which is especially important for high data volume systems.

One of the most efficient methods for supplying gaseous hydrogen long distances is by using steel pipelines. However, steel pipelines exhibit accelerated fatigue crack growth rates in gaseous hydrogen relative to air. Despite conventional expectations that higher strength steels would be more susceptible to hydrogen embrittlement, recent testing on a variety of pipeline steel grades has shown a notable independence between strength and hydrogen assisted fatigue crack growth rate. It is thought that microstructure may play a more defining role than strength in determining the hydrogen susceptibility. Among the many factors that could affect hydrogen accelerated fatigue crack growth rates, this study was conducted with an emphasis on orientation dependence. The orientation dependence of toughness in hot rolled steels is a well-researched area; however, few studies have been conducted to reveal the relationship between fatigue crack growth rate in hydrogen and orientation. In this work, fatigue crack growth rates were measured in hydrogen for high strength steel pipeline with different orientations. A significant reduction in fatigue crack growth rates were measured when cracks propagated perpendicular to the rolling direction. A detailed microstructural investigation was performed, in an effort to understand the orientation dependence of fatigue crack growth rate performance of pipeline steels in hydrogen environments.

Supercomputing hardware is undergoing a period of significant change. In order to cope with the rapid pace of hardware and, in many cases, programming model innovation, we have developed the Kokkos Programming Model – a C++-based abstraction that permits performance portability across diverse architectures. Our experience has shown that the abstractions developed can significantly frustrate debugging and profiling activities because they break expected code proximity and layout assumptions. In this paper we present the Kokkos Profiling interface, a lightweight, suite of hooks to which debugging and profiling tools can attach to gain deep insights into the execution and data structure behaviors of parallel programs written to the Kokkos interface.

Sandia National Laboratories has developed a method that applies machine learning methods to high-energy spectral X-ray computed tomography data to identify material composition for every reconstructed voxel in the field-of-view. While initial experiments led by Koundinyan et al. demonstrated that supervised machine learning techniques perform well in identifying a variety of classes of materials, this work presents an unsupervised approach that differentiates isolated materials with highly similar properties, and can be applied on spectral computed tomography data to identify materials more accurately compared to traditional performance. Additionally, if regions of the spectrum for multiple voxels become unusable due to artifacts, this method can still reliably perform material identification. This enhanced capability can tremendously impact fields in security, industry, and medicine that leverage non-destructive evaluation for detection, verification, and validation applications.

A new approach is presented for conducting and extrapolating combined environment (radiation plus thermal) accelerated aging experiments. The method involves a novel way of applying the time-temperature-dose rate (t-T-R) approach derived many years ago, which assumes that by simultaneously accelerating the thermal-initiation rate (from Arrhenius T-only analysis) and the radiation dose rate R by the same factor x, the overall degradation rate will increase by the factor x. The dose rate assumption implies that equal dose yields equal damage, which is equivalent to assuming the absence of dose-rate effects (DRE).Aplot of inverse absolute temperature versus the log of the dose rate is used to indicate experimental conditions consistent with themodel assumptions, which can be derived along lines encompassing so-called matched accelerated conditions (MAC lines). Aging trends taken along MAC lines for several elastomers confirms the underlying model assumption and therefore indicates, contrary to many past published results, that DRE are typically not present. In addition, the MAC approach easily accommodates the observation that substantial degradation chemistry changes occur as aging conditions transition R-T space from radiation domination (high R, low T) to temperature domination (low R, high T). The MAC-line approach also suggests an avenue for gaining more confidence in extrapolations of accelerated MAC-line data to ambient aging conditions by using ultrasensitive oxygen consumption (UOC) measurements taken along the MAC line both under the accelerated conditions and at ambient. From UOC data generated under combined R-T conditions, this approach is tested and quantitatively confirmed for one of thematerials. In analogy to the wear-out approach developed previously for thermo-oxidative aging, the MAC-line concept can also be used to predict the remaining lifetimes of samples extracted periodically from ambient environments.

Securing the rapidly expanding Internet of Things (IoT) is critical. Many of these "things" are vulnerable bare-metal embedded systems where the application executes directly on hardware without an operating system. Unfortunately, the integrity of current systems may be compromised by a single vulnerability, as recently shown by Google's P0 team against Broadcom's WiFi SoC. We present ACES (Automatic Compartments for Embedded Systems)1, an LLVM-based compiler that automatically infers and enforces inter-component isolation on bare-metal systems, thus applying the principle of least privileges. ACES takes a developer-specified compartmentalization policy and then automatically creates an instrumented binary that isolates compartments at runtime, while handling the hardware limitations of baremetal embedded devices. We demonstrate ACES' ability to implement arbitrary compartmentalization policies by implementing three policies and comparing the compartment isolation, runtime overhead, and memory overhead. Our results show that ACES' compartments can have low runtime overheads (13% on our largest test application), while using 59% less Flash, and 84% less RAM than the Mbed μVisor-the current state-of-the-art compartmentalization technique for bare-metal systems. ACES' compartments protect the integrity of privileged data, provide control-flow integrity between compartments, and reduce exposure to ROP attacks by 94.3% compared to μVisor.

Advancements in machine learning (ML) and deep learning (DL) have enabled imaging systems to perform complex classification tasks, opening numerous problem domains to solutions driven by high quality imagers coupled with algorithmic elements. However, current ML and DL methods for target classification typically rely upon algorithms applied to data measured by traditional imagers. This design paradigm fails to enable the ML and DL algorithms to influence the sensing device itself, and treats the optimization of the sensor and algorithm as separate sequential elements. Additionally, this current paradigm narrowly investigates traditional images, and therefore traditional imaging hardware, as the primary means of data collection. We investigate alternative architectures for computational imaging systems optimized for specific classification tasks, such as digit classification. This involves a holistic approach to the design of the system from the imaging hardware to algorithms. Techniques to find optimal compressive representations of training data are discussed, and most-useful object-space information is evaluated. Methods to translate task-specific compressed data representations into non-traditional computational imaging hardware are described, followed by simulations of such imaging devices coupled with algorithmic classification using ML and DL techniques. Our approach allows for inexpensive, efficient sensing systems. Reduced storage and bandwidth are achievable as well since data representations are compressed measurements which is especially important for high data volume systems.

Compressive sensing shows promise for sensors that collect fewer samples than required by traditional Shannon-Nyquist sampling theory. Recent sensor designs for hyperspectral imaging encode light using spectral modulators such as spatial light modulators, liquid crystal phase retarders, and Fabry-Perot resonators. The hyperspectral imager consists of a filter array followed by a detector array. It encodes spectra with less measurements than the number of bands in the signal, making reconstruction an underdetermined problem. We propose a reconstruction algorithm for hyperspectral images encoded through spectral modulators. Our approach constrains pixels to be similar to their neighbors in space and wavelength, as natural images tend to vary smoothly, and it increases robustness to noise. It combines L1 minimization in the wavelet domain to enforce sparsity and total variation in the image domain for smoothness. The alternating direction method of multipliers (ADMM) simplifies the optimization procedure. Our algorithm constrains encoded, compressed hyperspectral images to be smooth in their reconstruction, and we present simulation results to illustrate our technique. This work improves the reconstruction of hyperspectral images from encoded, multiplexed, and sparse measurements.

GaN is an attractive material for high-power electronics due to its wide bandgap and large breakdown field. Verticalgeometry devices are of interest due to their high blocking voltage and small form factor. One challenge for realizing complex vertical devices is the regrowth of low-leakage-current p-n junctions within selectively defined regions of the wafer. Presently, regrown p-n junctions exhibit higher leakage current than continuously grown p-n junctions, possibly due to impurity incorporation at the regrowth interfaces, which consist of c-plane and non-basal planes. Here, we study the interfacial impurity incorporation induced by various growth interruptions and regrowth conditions on m-plane p-n junctions on free-standing GaN substrates. The following interruption types were investigated: (1) sample in the main MOCVD chamber for 10 min, (2) sample in the MOCVD load lock for 10 min, (3) sample outside the MOCVD for 10 min, and (4) sample outside the MOCVD for one week. Regrowth after the interruptions was performed on two different samples under n-GaN and p-GaN growth conditions, respectively. Secondary ion mass spectrometry (SIMS) analysis indicated interfacial silicon spikes with concentrations ranging from 5e16 cm-3 to 2e18 cm-3 for the n-GaN growth conditions and 2e16 cm-3 to 5e18 cm-3 for the p-GaN growth conditions. Oxygen spikes with concentrations ∼1e17 cm-3 were observed at the regrowth interfaces. Carbon impurity levels did not spike at the regrowth interfaces under either set of growth conditions. We have correlated the effects of these interfacial impurities with the reverse leakage current and breakdown voltage of regrown m-plane p-n junctions.

MPI usage patterns are changing as applications move towards fully-multithreaded runtimes. However, the impact of these patterns on MPI message matching is not well-studied. In particular, MPI’s mechanic for receiver-side data placement, message matching, can be impacted by increased message volume and nondeterminism incurred by multithreading. While there has been significant developer interest and work to provide an efficient MPI interface for multithreaded access, there has not been a study showing how these patterns affect messaging patterns and matching behavior. In this paper, we present a framework for studying the effects of multithreading on MPI message matching. This framework allows us to explore the implications of different common communication patterns and thread-level decompositions. We present a study of these impacts on the architecture of two of the Top 10 supercomputers (NERSC’s Cori and LANL’s Trinity). This data provides a baseline to evaluate reasonable matching engine queue lengths, search depths, and queue drain times under the multithreaded model. Furthermore, the study highlights surprising results on the challenge posed by message matching for multithreaded application performance.

As research in superconducting electronics matures, it is necessary to have failure analysis techniques to identify parameters that impact yield and failure modes in the fabricated product. However, there has been significant skepticism regarding the ability of laser-based failure analysis techniques to dctect defects at room temperature in superconducting electronics designed to operate at cryogenic temperatures. In this paper, we describe preliminary data showing the use of Thermally Induced Voltage Alteration (1∗1 VA) [l| at ambient temperature to locate defects in known defective circuits fabricated using state-of-the-art techniques for superconducting electronics.

Many candidate power system architectures are being evaluated for the Navy’s next generation all-electric warship. One proposed power system concept involves the use of dual-wound generators to power both the Port and Starboard side buses using different 3-phase sets from the same machine (Doerry, 2015). This offers the benefit of improved efficiency through reduced engine light-loading and improved dispatch flexibility, but the approach couples the two busses through a common generator, making one bus vulnerable to faults and other dynamic events on the other bus. Thus, understanding the dynamics of cross-bus coupling is imperative to the successful implementation of a dual-wound generator system. In (Rashkin, 2017), a kilowatt-scale system was analysed that considered the use of a dual-wound permanent magnet machine, two passive rectifiers, and two DC buses with resistive loads. For this system, dc voltage variation on one bus was evaluated in the time domain as a function of load changes on the other bus. Therein, substantive cross-bus coupling was demonstrated in simulation and hardware experiments. The voltage disturbances were attributed to electromechanical (i.e. speed disturbances) as well as electromagnetic coupling mechanisms. In this work, a 25 MVA dual-wound generator was considered, and active rectifier models were implemented in Matlab both using average value modelling and switching (space vector modulation) simulation models. The frequency dynamics of the system between the load on one side and the dc voltage on the other side was studied. The coupling is depicted in the frequency domain as a transfer function with amplitude and phase and is shown to have distinct characteristics (i.e. frequency regimes) associated with physical coupling mechanisms such as electromechanical and electromagnetic coupling as well as response characteristics associated with control action by the active rectifiers. In addition, based on requirements outlined in draft Military Standard 1399-MVDC, an approach to derive specifications will be discussed and presented. This method will aid in quantifying the allowable coupling of energy from one bus to another in various frequency regimes as a function of other power system parameters. Finally, design and control strategies will be discussed to mitigate cross-bus coupling. The findings of this work will inform the design, control, and operation of future naval warship power systems.

We detail the development of a flexible simulation program (NMR_DIFFSIM) that solves the nuclear magnetic resonance (NMR) spin diffusion equation for arbitrary polymer architectures. The program was used to explore the proton (1H) NMR spin diffusion behavior predicted for a range of geometrical models describing polymer exchange membranes. These results were also directly compared with the NMR spin diffusion behavior predicted for more complex domain structures obtained from molecular dynamics (MD) simulations. The numerical implementation and capabilities of NMR_DIFFSIM were demonstrated by evaluating the experimental NMR spin diffusion behavior for the hydrophilic domain structure in sulfonated Diels-Alder Poly(Phenylene) (SDAPP) polymer membranes. The impact of morphology variations as a function of sulfonation and hydration level on the resulting NMR spin diffusion behavior were determined. These simulations allowed us to critically address the ability of NMR spin diffusion to discriminate between different structural models, and to highlight the extremely high fidelity experimental data required to accomplish this. A direct comparison of experimental double-quantum-filtered 1H NMR spin diffusion in SDAPP membranes to the spin diffusion behavior predicted for MD-proposed morphologies revealed excellent agreement, providing experimental support for the MD structures at low to moderate hydration levels. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2018, 56, 62–78.

Today’s computational, experimental, and observational sciences rely on computations that involve many related tasks. The success of a scientific mission often hinges on the computer automation of these workflows. In April 2015, the US Department of Energy (DOE) invited a diverse group of domain and computer scientists from national laboratories supported by the Office of Science, the National Nuclear Security Administration, from industry, and from academia to review the workflow requirements of DOE’s science and national security missions, to assess the current state of the art in science workflows, to understand the impact of emerging extreme-scale computing systems on those workflows, and to develop requirements for automated workflow management in future and existing environments. This article is a summary of the opinions of over 50 leading researchers attending this workshop. We highlight use cases, computing systems, workflow needs and conclude by summarizing the remaining challenges this community sees that inhibit large-scale scientific workflows from becoming a mainstream tool for extreme-scale science.

The rapidly growing penetration levels of distributed photovoltaic (PV) systems requires more comprehensive studies to understand their impact on distribution feeders. IEEE P.1547 highlights the need for Quasi-Static Time Series (QSTS) simulation in conducting distribution impact studies for distributed resource interconnection. Unlike conventional scenario-based simulation, the time series simulation can realistically assess time-dependent impacts such as the operation of various controllable elements (e.g. voltage regulating tap changers) or impacts of power fluctuations. However, QSTS simulations are still not widely used in the industry because of the computational burden associated with running yearlong simulations at a 1-s granularity, which is needed to capture device controller effects responding to PV variability. This paper presents a novel algorithm that reduces the number of times that the non-linear 3-phase unbalanced AC power flow must be solved by storing and reassigning power flow solutions as it progresses through the simulation. Each unique power flow solution is defined by a set of factors affecting the solution that can easily be queried. We demonstrate a computational time reduction of 98.9% for a yearlong simulation at 1-s resolution with minimal errors for metrics including: number of tap changes, capacitor actions, highest and lowest voltage on the feeder, line losses, and ANSI voltage violations. The key contribution of this work is the formulation of an algorithm capable of: (i) drastically reducing the computational time of QSTS simulations, (ii) accurately modeling distribution system voltage-control elements with hysteresis, and (iii) efficiently compressing result time series data for post-simulation analysis.

Aluminized ammonium perchlorate composite propellants can form large molten agglomerated particles that may result in poor combustion performance, slag accumulation, and increased two-phase flow losses. Quantifying agglomerate size distributions are needed to gain an understanding of agglomeration dynamics and ultimately design new propellants for improved performance. Due to complexities of the reacting multiphase environment, agglomerate size diagnostics are difficult and measurement accuracies are poorly understood. To address this, the current work compares three agglomerate sizing techniques applied to two propellant formulations. Particle collection on a quench plate and backlit videography are two relatively common techniques, whereas digital inline holography is an emerging alternative for three-dimensional measurements. Atmospheric pressure combustion results show that all three techniques are able to capture the qualitative trends; however, significant differences exist in the quantitative size distributions and mean diameters. For digital inline holography, methods are proposed that combine temporally resolved high-speed recording with lower-speed but higher spatial resolution measurements to correct for size- velocity correlation biases while extending the measurable size dynamic range. The results from this work provide new guidance for improved agglomerate size measurements along with statistically resolved datasets for validation of agglomerate models.

In this study, a novel cross-sectional battery cellwas developed to characterize lithium-sulfur batteries usingX-ray spectromicroscopy. Chemically sensitive X-raymapswere collected operando at energies relevant to the expected sulfur species andwere used to correlate changes in sulfur species with electrochemistry. Significant changes in the sulfur/carbon composite electrode were observed from cycle to cycle including rearrangement of the elemental sulfur matrix and PEO10LiTFSI binder. Polysulfide concentration and area of spatial diffusion increased with cycling, indicating that some polysulfide dissolution is irreversible, leading to polysulfide shuttle. Fitting of the maps using standard sulfur and polysulfide XANES spectra indicated that upon subsequent discharge/charge cycles, the initial sulfur concentration was not fully recovered; polysulfides and lithium sulfide remained at the cathodes with higher order polysulfides as the primary species in the region of interest. Quantification of the polysulfide concentration across the electrolyte and electrode interfaces shows that the polysulfide concentration before the first discharge and after the third charge is constant within the electrolyte, but while cycling, a significant increase in polysulfides and a gradient toward the lithium metal anode forms. This chemically and spatially sensitive characterization and analysis provides a foundation for further operando spectromicroscopy of lithium-sulfur batteries.

Structural dynamic models of mechanical, aerospace, and civil structures often involve connections of multiple subcomponents with rivets, bolts, press fits, or other joining processes. Recent model order reduction advances have been made for jointed structures using appropriately defined whole joint models in combination with linear substructuring techniques. A whole joint model condenses the interface nodes onto a single node with multi-point constraints resulting in drastic increases in computational speeds to predict transient responses. One drawback to this strategy is that the whole joint models are empirical and require calibration with test or high-fidelity model data. A new framework is proposed to calibrate whole joint models by computing global responses from high-fidelity finite element models and utilizing global optimization to determine the optimal joint parameters. The method matches the amplitude dependent damping and natural frequencies predicted for each vibration mode using quasi-static modal analysis.

Nano-scale spatial confinement can alter chemistry at mineral-water interfaces. These nano-scale confinement effects can lead to anomalous fate and transport behavior of aqueous metal species. When a fluid resides in a nano-porous environments (pore size under 100 nm), the observed density, surface tension, and dielectric constant diverge from those measured in the bulk. To evaluate the impact of nano-scale confinement on the adsorption of copper (Cu2+), we performed batch adsorption studies using mesoporous silica. Mesoporous silica with the narrow distribution of pore diameters (SBA-15; 8, 6, and 4 nm pore diameters) was chosen since the silanol functional groups are typical to surface environments. Batch adsorption isotherms were fit with adsorption models (Langmuir, Freundlich, and Dubinin-Radushkevich) and adsorption kinetic data were fit to a pseudo-first-order reaction model. We found that with decreasing pore size, the maximum surface area-normalized uptake of Cu2+ increased. The pseudo-first-order kinetic model demonstrates that the adsorption is faster as the pore size decreases from 8 to 4 nm. We attribute these effects to the deviations in fundamental water properties as pore diameter decreases. In particular, these effects are most notable in SBA-15 with a 4-nm pore where the changes in water properties may be responsible for the enhanced Cu mobility, and therefore, faster Cu adsorption kinetics.

Complex mechanical structures are often subjected to random vibration environments. One strategy to analyze these nonlinear structures numerically is to use finite element analysis with an explicit solver to resolve interactions in the time domain. However, this approach is impractical because the solver is conditionally stable and requires thousands of iterations to resolve the contact algorithms. As a result, only short runs can be performed practically because of the extremely long runtime needed to obtain sufficient sampling for long-time statistics. The proposed approach uses a machine learning algorithm known as the Long Short-Term Memory (LSTM) network to model the response of the nonlinear system to random input. The LSTM extends the capability of the explicit solver approach by taking short samples and extending them to arbitrarily long signals. The efficient LSTM algorithm enables the capability to perform Monte Carlo simulations to quantify model-form and aleatoric uncertainty due to the random input.

Recent theoretical predictions indicate that functional groups and additives could have a favorable impact on the hydrogen adsorption characteristics of sorbents; however, no definite evidence has been obtained to date and little is known about the impact of such modifications on the thermodynamics of hydrogen uptake and overall capacity. In this work, we investigate the effect of two types of additives on the cryoadsorption of hydrogen to mesoporous silica. First, Lewis and Brønsted acid sites were evaluated by grafting aluminum to the surface of mesoporous silica (MCF-17) and characterizing the resulting silicate materials' surface area and the concentration of Brønsted and Lewis acid sites created. Heat of adsorption measurements found little influence of surface acidity on the enthalpy of hydrogen cryoadsorption. Secondly, platinum nanoparticles of 1.5 nm and 7.1 nm in diameter were loaded into MCF-17, and characterized by TEM. Hydrogen absorption measurements revealed that the addition of small amounts of metallic platinum nanoparticles increases by up to two-fold the amount of hydrogen adsorbed at liquid nitrogen temperature. Moreover, we found a direct correlation between the size of platinum particles and the amount of hydrogen stored, in favor of smaller particles.

We review the physical foundations of Landauer’s Principle, which relates the loss of information from a computational process to an increase in thermodynamic entropy. Despite the long history of the Principle, its fundamental rationale and proper interpretation remain frequently misunderstood. Contrary to some misinterpretations of the Principle, the mere transfer of entropy between computational and non-computational subsystems can occur in a thermodynamically reversible way without increasing total entropy. However, Landauer’s Principle is not about general entropy transfers; rather, it more specifically concerns the ejection of (all or part of) some correlated information from a controlled, digital form (e.g., a computed bit) to an uncontrolled, non-computational form, i.e., as part of a thermal environment. Any uncontrolled thermal system will, by definition, continually re-randomize the physical information in its thermal state, from our perspective as observers who cannot predict the exact dynamical evolution of the microstates of such environments. Thus, any correlations involving information that is ejected into and subsequently thermalized by the environment will be lost from our perspective, resulting directly in an irreversible increase in thermodynamic entropy. Avoiding the ejection and thermalization of correlated computational information motivates the reversible computing paradigm, although the requirements for computations to be thermodynamically reversible are less restrictive than frequently described, particularly in the case of stochastic computational operations. There are interesting possibilities for the design of computational processes that utilize stochastic, many-to-one computational operations while nevertheless avoiding net entropy increase that remain to be fully explored.

Abstract not provided.

Simultaneous pressure sensitive paint (PSP) and stereo digital image correlation (DIC) measurements on a jointed beam structure are presented. Tests are conducted in a shock tube, providing an impulsive starting condition followed by approximately uniform high-speed flow conditions for 5.0 msec. The unsteady pressure loading generated by shock waves and vortex shedding results in the excitation of various structural modes in the beam. The combined data characterizes the structural loading input (pressure) and the resulting structural behavior output (deformation). Time-series filtering is used to remove external bias errors such as shock tube motion, and proper orthogonal decomposition (POD) is used to extract mode shapes from the deformation data. This demonstrates the utility of using fast-response PSP together with stereo digital image correlation (DIC), which provides a valuable capability for validating structural dynamics simulations.

When making computational simulation predictions of multi-physics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis a verification, validation and uncertainty quantification workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias are used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes computing resources.

A BDDC domain decomposition preconditioner is defined by a coarse component, expressed in terms of primal constraints, a weighted average across the interface between the subdomains, and local components given in terms of solvers of local subdomain problems. BDDC methods for vector field problems discretized with Raviart-Thomas finite elements are introduced. The methods are based on a deluxe type of weighted average and an adaptive selection of primal constraints developed to deal with coefficients with high contrast even inside individual subdomains. For problems with very many subdomains, a third level of the preconditioner is introduced. Under the assumption that the subdomains are all built from elements of a coarse triangulation of the given domain, that the meshes of each subdomain are quasi uniform and that the material parameters are constant in each subdomain, a bound is obtained for the condition number of the preconditioned linear system which is independent of the values and the jumps of these parameters across the interface between the subdomains as well as the number of subdomains. Numerical experiments, using the PETSc library, are also presented which support the theory and show the effectiveness of the algorithms even for problems not covered by the theory. Included are also experiments with Brezzi-Douglas-Marini finite element approximations.

This letter demonstrates the design of continuously graded elastic cylinders to achieve passive cloaking from harmonic acoustic excitation, both at single frequencies and over extended bandwidths. The constitutive parameters in a multilayered, constant-density cylinder are selected in a partial differential equation-constrained optimization problem, such that the residual between the pressure field from an unobstructed spreading wave in a fluid and the pressure field produced by the cylindrical inclusion is minimized. The radial variation in bulk modulus appears fundamental to the cloaking behavior, while the shear modulus distribution plays a secondary role. Such structures could be realized with functionally-graded elastic materials.

The corrosion of pulsed-laser deposited Fe thin films by aqueous acetic acid solution was explored in real time by performing dynamic microfluidic experiments in situ in a transmission electron microscope. The films were examined in both the as-deposited condition and after annealing. In the as-deposited films, discrete events featuring the localized dissolution of grains were observed with the dissolved volumes ranging in size from ~1.5 x 10-5 μm3 to 3.4 x 10-7 μm3. The annealed samples had larger grains than the as-deposited samples, were more resistant to corrosion, and did not show similar discrete dissolution events. The electron beam was observed to accelerate the corrosion, especially on the as-deposited samples. The effects of grain surface energy, grain boundary energy and the electron beam-specimen interactions are discussed in relation to the observed behavior.

Simulation models can improve decisions meant to control the consequences of disruptions to critical infrastructures. We describe a dynamic flow model on networks purposed to inform analyses by those concerned about consequences of disruptions to infrastructures and to help policy makers design robust mitigations. We conceptualize the adaptive responses of infrastructure networks to perturbations as market transactions and business decisions of operators. We approximate commodity flows in these networks by a diffusion equation, with nonlinearities introduced to model capacity limits. To illustrate the behavior and scalability of the model, we show its application first on two simple networks, then on petroleum infrastructure in the United States, where we analyze the effects of a hypothesized earthquake.