Publications

Energy storage technologies are positioned to play a substantial role in power delivery systems. They are being touted as an effective new resource to maintain reliability and allow for increased penetration of renewable energy. However, due to their relative infancy, there is a lack of knowledge on how these resources truly operate over time. Data analysis can help ascertain the operational and performance characteristics of these emerging technologies. Rigorous testing and data analysis are important for all stakeholders to ensure a safe, reliable system that performs predictably on a macro level. Standardizing testing and analysis approaches to verifying the performance of energy storage devices, equipment, and systems when integrating them into the grid will improve the understanding and benefit of energy storage over time from technical and economic vantage points. Demonstrating the life-cycle value and capabilities of energy storage systems begins with the data the provider supplies for analysis. After review of energy storage data received from several providers, it has become clear that some of these data are inconsistent and incomplete, raising the question of their efficacy for robust analysis. This report reviews and proposes general guidelines such as sampling rates and data points that providers must supply for robust data analysis to take place. Consistent guidelines are the basis of the proper protocol and ensuing standards to (a) reduce the time it takes data to reach those who are providing analysis; (b) allow them to better understand the energy storage installations; and (c) enable them to provide high-quality analysis of the installations. This report is intended to serve as a starting point for what data points should be provided when monitoring. As battery technologies continue to advance and the industry expands, this report will be updated to remain current.

JEDEC matrix trays are an industry standard for the safe handling, transport, and storage of electronics. Manufacturing tolerances of the trays (typical for injection-molded components) forces the creation of an envelope around components rather than a secure interface to prevent the trays themselves from damaging the electronics during use. However, this allows relative motion between the components, another potential damage source. This paper covers the design of tray features (flexures) than enable intentional, tuned contact preload to prevent relative motion, maximizing component safety while keeping the JEDEC tray form-factor. The target preload is balanced between gravity and shipment forces that could be experience during adverse handling or transport to prevent loss of contact or slip and keeping the load itself low enough to prevent component damage. Typical features/dimensions for JECEC trays were employed to maximize utility and minimize tray fabrication difficulty. The design also increases usability and component safety by making the chips visible while still sandwiched between trays. The point design described here exemplifies a simple, easy-to-manufacture tray-matrix flexure that significantly improves the security of supported components

Gamma-ray dose rates computed by two radiation source model-based methods and five measured spectrum-based methods are compared. With the exception of a Detective DX-100, which reported dose rates that are about 25% less than the mean, dose rates are generally within 10% of mean values. Methods for computation of dose rates employed by the Gamma Detector Response and Analysis Software (GADRAS) are described and critically evaluated. Results are presented for a feasibility evaluation that seeks to use spectra recorded inside vehicles to estimate the minimum dose outside the vehicles.

Three codes are used for comparisons in this report to evaluate the adequacy of MACCS in the nearfield, AERMOD, ARCON96 and QUIC. Test cases were developed to give a broad range of weather conditions, building dimensions and plume buoyancy. Based on the comparisons of MACCS with AERMOD, ARCON96 and QUIC across the test cases, the following observations are made: MACCS calculations configured with point-source, ground-level, nonbuoyant plumes provide nearfield results that bound the centerline, ground-level air concentrations from AERMOD, ARCON96, and QUIC. MACCS calculations with ground-level, nonbuoyant plumes that include the effects of the building wake (area source) provide nearfield results that bound the results from AERMOD and QUIC and the results from ARCON96 at distances >250 m. If using a point-source is too conservative and it is desired to bound the results from all three codes, another alternative is to use area source parameters in MACCS that are less than the standard values, i.e., an area source intermediate between the standard recommendation and a point source. All these options provide results from MACCS that are bounding for the test cases evaluated. Based on these observations, it appears that MACCS is adequate for use in nearfield calculations, given the correct parameterization.

This project plan gives a high-level description of the US Department of Energy Office of Nuclear Energy (DOE-NE) Spent Fuel and Waste Disposition (SFWD) campaign in situ borehole heater test project planned for the Waste Isolation Pilot Plant (WIPP) site, titled the Brine Availability Test in Salt (BATS). BATS is the first stage in a planned sequence of tests to bolster the technical basis for disposal of heat generating waste in salt. This plan provides an overview of the schedule and responsibilities of the parties involved. This project is a collaborative effort by Sandia, Los Alamos, and Lawrence Berkeley National Laboratories to execute a series of small-diameter borehole heater tests in salt for the DOE-NE SFWD campaign. Design of a heater test in salt at WIPP has evolved over several years. The experiment has begun in January 2020 and the first phase will continue for several months with the possibility for follow on testing. BATS comprises a suite of modular tests, which consist of a group of adjacent horizontal boreholes in the wall of drifts at WIPP. Each test is centered around a packer-isolated heated borehole (12.2 cm [4.81 diameter) containing equipment for water-vapor collection and borehole closure monitoring, surrounded by smaller-diameter (up to 5.3 cm [2.11 diameter) satellite observation boreholes. Observation boreholes contain grouted-in temperature sensors, electrical resistivity tomography (ERT) sensors, and fiber optics; packer-isolated tracer release and sampling intervals; and acoustic emission (AE) piezoelectric sensors. A larger-diameter (12.2 cm [4.81) satellite borehole includes sorel and salt cement plugs, as part of an engineered barrier sealing test. The first two tests, to be implemented in parallel, are heated (target borehole wall temperature of 120 °C) and unheated, with similar arrays of observation borehole monitoring changes. Follow-on tests will be designed using information gathered from the first two tests, and may be conducted at other borehole wall temperatures, use multiple observation boreholes, and may include different measurement types and test designs. This 2020 update of the original 2018 project plan satisfies DOE-NE Spent Fuel and Waste Science and Technology (SFWST) milestone M3SF-205N010303034, as part of the SNL "Salt Disposal R&D" work package.

This report describes research and development (R&D) activities conducted during fiscal year 2019 (FY19) specifically related to the Engineered Barrier System (EBS) R&D Work Package in the Spent Fuel and Waste Science and Technology (SFWST) Campaign supported by the United States (U.S.) Department of Eneregy (DOE). The R&D activities focus on understanding EBS component evolution and interactions within the EBS, as well as interactions between the host media and the EBS. A primary goal is to advance the development of process models that can be implemented directly within the Genreric Disposal System Analysis (GDSA) platform or that can contribute to the safety case in some manner such as building confidence, providing further insight into the processes being modeled, establishing better constraints on barrier performance, etc.The FY19 EBS activities involved not only modeling and analysis work, but experimental work as well. The report documents the FY19 progress made in seven different research areas as follows: (1) thermal analysis for the disposal of dual purpose canisters (DPCs) in sedimentary host rock using the semianalytical method, (2) tetravalent uranium solubility and speciation, (3) modeling of high temperature, thermal-hydrologic-mechanical-chemical (THMC) coupled processes, (4) integration of coupled thermalhydrologic- chemical (THC) model with GDSA using a Reduced-Order Model, (5) studying chemical controls on montmorillonite structure and swelling pressure, (6) transmission x-ray microscope for in-situ nanotomography of bentonite and shale, and (7) in-situ electrochemical testing of uranium dioxide under anoxic conditions. The R&D team consisted of subject matter experts from Sandia National Laboratories, Lawrence Berkeley National Laboratory (LBNL), Los Alamos National Laboratory (LANL), Pacific Northwest National Laboratory (PNNL), the Bureau de Recherches Géologiques et Minières (BRGM), the University of California Berkeley, and Mississippi State University. In addition, the EBS R&D work leverages international collaborations to ensure that the DOE program is active and abreast of the latest advances in nuclear waste disposal. For example, the FY19 work on modeling coupled THMC processes at high temperatures relied on the bentonite properties from the Full-scale Engineered Barrier EXperiment (FEBEX) Field Test conducted at the Grimsel Test Site in Switzerland. Overall, significant progress has been made in FY19 towards developing the modeling tools and experimental capabilities needed to investigate the performance of EBS materials and the associated interactions in the drift and the surrounding near-field environment under a variety of conditions including high temperature regimes.

Organic linkers in metal-organic framework (MOF) materials exhibit differences in hydrogen bonding (H-bonding), which can alter the geometric, electronic, and optical properties of the MOF. Density functional theory (DFT) simulations were performed on a photoluminescent Y-2,5-dihydroxyterephthalic acid (DOBDC) MOF with H-bonding concentrations between 0 and 100%; the H-bonds were located on both bidentate-and monodentate-bound DOBDC linkers. At 0% H-bond concentration in the framework, the lattice parameters contracted, the density increased, and simulated X-ray diffraction patterns shifted. Comparison with published experimental data identified that Y-DOBDC MOF structures must have a degree of H-bond concentration. The concentration of H-bonds in the system shifted the calculated band gap energy from 2.25 eV at 100% to 3.00 eV at 0%. The band gap energies also indicate a distinction of H-bonds formed on bidentate-coordinated linkers compared to those on monodentate linkers. Additionally, when the calculated optical spectra are compared with experimental data, the ligand-to-ligand charge-transfer luminescence in Y-DOBDC MOFs is expected to result from an average of 20-40% H-bonding with at least 50% of the bidentate linkers containing H-bonding. Therefore, the type of H-bonding within the DOBDC linker determines the electronic structure and the optical absorption of the MOF framework structure. Tuning of the H-bonding in rare-earth MOFs provides an opportunity to control the specific optical and adsorption properties of the MOF framework on the basis of reactions between the linker and the environment.

Security models for the bioeconomy have largely been developed on risk profiles borrowed from the financial industry and, in some cases, industrial control systems. The bioeconomy, however, has unique characteristics that require domain-specific knowledge of the risks to environmental, health and economic impact from directly targeted attacks. Key questions need to be assessed and answered for any facility engaged in bioproduction. These include: how much technical information must the attacker possess in order to target a specific process or facility? Which processes cause the largest economic impact if disrupted? Can an attacker lead companies down the wrong path of research, leading to irrecoverable losses of time, resources and capital? Can attackers disrupt venture capital strategies and affect financial returns? What are the resources required to attack key workflows, and subsequently what is the cost of defense? What strategies are effective against such attackers, and what are their cost? Can government provide an active role in assurance of material, process and results for critical bioeconomic infrastructure?

A recently completed LDRD project has provided a new facility at Sandia for testing effects of energetic neutrons on electronic components. 14 MeV neutrons are produced with a deuterium ion beam onto a thin-film tritide target. The goal of the project was to increase the neutron fluence to levels needed for radiation effects testing and qualification. This goal was achieved through two technical advances. First, a new multi-layer target concept was developed to reduce the rate of tritium loss from the target by isotope exchange, thereby reducing tritium usage and increasing target lifetime. The second advance was the construction of a new test chamber designed to maximize neutron flux at test locations. Together, these increased the available neutron fluence by several orders of magnitude. This new capability is being used in tests for Sandia nuclear weapon programs, evaluation of commercial parts such as highly-scaled CMOS SRAM lCs, and tests of new devices under development at Sandia such as lll-V HBTs, gallium nitride high-voltage diodes, and for fundamental studies of physical mechanisms of device failure.

Assess the evolving integration potential of light-duty (LDV) and heavy-duty vehicle (HDV) technologies, fuels, and infrastructure and their contributions to lowering emissions and petroleum consumption. Leverage existing LDV and build HDV ParaChoice capability to conduct parametric analyses that explore the trade-space for key factors that influence consumer choice and technology, fuel and infrastructure development. ParaChoice provides the unique capability to examine tipping points and tradeoffs, and can help quantify the effects of and mitigate uncertainty introduced by data sources and assumptions.

Transition metal ions provide a rich set of optically active defect spins in wide bandgap semiconductors. Chromium (Cr⁴⁺) in silicon-carbide (SiC) produces a spin-1 ground state with a narrow, spectrally isolated, spin-selective, near-telecom optical interface. However, previous studies were hindered by material quality resulting in limited coherent control. In this work, we implant Cr into commercial 4H-SiC and show optimal defect activation after annealing above 1600 °C. We measure an ensemble optical hole linewidth of 31 MHz, an order of magnitude improvement compared to as-grown samples. An in-depth exploration of optical and spin dynamics reveals efficient spin polarization, coherent control, and readout with high fidelity (79%). We report T₁ times greater than 1 s at cryogenic temperatures (15 K) with a T₂^* = 317 ns and a T₂ = 81 μs, where spin dephasing times are currently limited by spin-spin interactions within the defect ensemble. Our results demonstrate the potential of Cr⁴⁺ in SiC as an extrinsic, optically active spin qubit.

In the case for each of the tasks, implementation started in a private topic branch. That branch was later submitted as a merge request where the code was run through regression tests across multiple test platforms. The merge requests were also subjected to human reviewers for approval. After necessary modifications were made, the code was merged to VTK-m's master branch. Subsequently, documentation was written for the VTK-m User's Guide.

The goal of the DOE OE Energy Storage System Safety Roadmap is to foster confidence in the safety and reliability of energy storage systems. There are three interrelated objectives to support the realization of that goal: research, codes and standards (C/S) and communication/coordination. The objective focused on C/S is "To apply research and development to support efforts that refocused on ensuring that codes and standards are available to enable the safe implementation of energy storage systems in a comprehensive, non-discriminatory and science-based manner."

Sandia and SLAC staff evaluated Princeton Plasma Physics Laboratory (PPPL) engineering procedures. The report documents, team members, interviewees and documents consulted, lines of inquiry, strengths and best practices, weaknesses and risks, opportunities for improvement, and general comments.

Bacterial tRNA have been found interrupted at various positions in the anticodon loop by group I introns, in four types. The primary bioinformatic tool for group I intron discovery is a covariance model that can identify conserved features in the catalytic core and can sometimes identify the typical uridine residue at the -1 position, preceding the 5-prime splice site, but cannot identify the typical guanidine residue at the omega position, preceding the 3-prime splice site, to achieve precise mapping. One approach to complete the automation of group I intron mapping is to focus instead on the exons, which is enabled by the regularity of tRNAs. We develop a software module, within a larger package (tFind) aimed at mapping bacterial tRNA and tmRNA genes precisely, that expands this list of four known classes of intron-interrupted tRNAs to 21 cases. A new covariance model for these introns is presented. The wobble base pair formed by the -1 uridine is considered a determinant of the 5-prime splice site, yet one reasonably large new type bears a cytidine nucleotide at that position.

All experiments involving the Pecos target chamber in calendar year (CY) 2019 were dedicated to Pre-Heat studies in the context of Magnetized Liner Inertial Fusion (MagLIF). Activities at the target area included actual laser shots but also diagnostic and maintenance, and preparatory work for experiments with cryogenically cooled targets, which are anticipated for CY 2020. Since the Z-Beamlet and Z-Petawatt lasers support multiple campaigns, we can only anticipate laser and laser-operator time for one out of three shot windows in a day, and typically not on every day of the week. For that reason, many of our non-shot activities need to be traded against shots as well.

Abstract not provided.

Fatty alcohols (FOHs) are important feedstocks in the chemical industry to produce detergents, cosmetics, and lubricants. Microbial production of FOHs has become an attractive alternative to production in plants and animals due to growing energy demands and environmental concerns. However, inhibition of cell growth caused by intracellular FOH accumulation is one major issue that limits FOH titers in microbial hosts. In addition, identification of FOH-specific exporters remains a challenge and previous studies towards this end are limited. To alleviate the toxicity issue, we exploited nonionic surfactants to promote the export of FOHs in Rhodosporidium toruloides, an oleaginous yeast that is considered an attractive next-generation host for the production of fatty acid-derived chemicals. Our results showed FOH export efficiency was dramatically improved and the growth inhibition was alleviated in the presence of small amounts of tergitol and other surfactants. As a result, FOH titers increase by 4.3-fold at bench scale to 352.6 mg/L. With further process optimization in a 2-L bioreactor, the titer was further increased to 1.6 g/L. The method we show here can potentially be applied to other microbial hosts and may facilitate the commercialization of microbial FOH production.

Terry steam turbines are employed in the safety systems of many nuclear Boiling Water Reactors to drive pumps and provide cooling water to the nuclear reactor core. While the turbine efficiency is low, the more important feature is high reliability under off-normal conditions. An important aspect of reliability is the ability to function with two-phase steam-water injection into the turbine, as most likely occurred in the Fukushima Dai-ichi nuclear accidents. This study investigates the characteristics of a Terry turbine during air-water injection with gas mass fractions ranging from 1 (dry gas) to 0.05 (wet gas), to better understand the Terry turbine's true operational capabilities and provide justification for extended Terry turbine use for reactor safety. Other parameters investigated are the inlet pressure, the exhaust backpressure and the turbine's rotational speed. The turbine performance is presented in terms of dynamometer loading and pump performance change as functions of the gas mass fraction.

This chapter first describes the traditional view of “context” in systems engineering and identifies challenges to this view related to “the Fourth Industrial Revolution”. It then explores gaps in traditional views, introduces nontraditional approaches to context for systems, and provides more detail on the “context of use” concept for advanced systems engineering. In response to technological evolution(s), advanced systems engineering should seek to more clearly and comprehensively describe operating environments - to include accounting for contextual descriptions consisting of the interrelated human behavior, social, and organizational factors that impact system performance and success. Three academic literatures - systems theory, organization science, and engineering systems - offer insights to better understand and incorporate context into advanced systems engineering. To further make the case for including the context of use in advanced systems engineering, the chapter explores improving systems engineering approaches for security at high consequence facilities.

Simultaneous control of nanoscale surface morphology and composition remains a challenge in preparing bimetallic catalysts, particularly at the large scale required for industrial application and with high-surface-area substrates. Atomic layer electroless deposition (ALED) is a scalable approach to prepare surface-modified metal powders in which elements more noble than the surface hydrides of the substrate metal are deposited layer-by-layer in a surface-limited fashion. Herein we demonstrate that high-surface-area Pt powder is a viable substrate for controlled deposition of Pd adlayers using this technique, with the potential for large-scale preparation, for use in electrocatalytic and catalytic applications such as fuel cells and functionalization of petrochemical feedstocks. Two different growth mechanisms have been proposed based on bulk and surface Pd atomic fractions obtained from atomic absorption spectroscopy and X-ray photoelectron spectroscopy, respectively. Further, spectral simulations were performed to strengthen the proposed growth mechanisms, favoring conformal growth in initial deposition followed by island formation in subsequent cycles. Observation of multiple pathways suggests a means of controlling adlayer surface morphology of ALED materials, in which an initial cycle of deposition sets the fractional coverage and subsequent cycles tune adlayer thickness.

Determine what the baseline radiological concentrations are at SNL/NM for water discharge to the sanitary sewer system and provide limits for other analyzed radionuclides.

This report documents the work performed in characterizing the vacuum conductance of porous structures made by conventional sintering (purchased as commercially available products from Mott Corporation ranging from 20 to 100 media grade) and additive manufacturing (powder bed fusion). The additively manufactured structures described in this report were originally intended to be the first iteration of several in an effort to produce desirable conductance characteristics. While resources were not available to link the experimental results to a modeling effort to better understand why certain characteristics were observed, the author hopes that this report may provide a useful set of data for future use, especially as sintered porous structures are not uncommonly procured from Mott corporation for research and development purposes. For that reason, all of the raw data is tabulated in the Appendix: it is possible to reproduce all of the figures shown in this report independently. For a quick order-of magnitude scan of the conductances, refer to Figures 5, 6, 8 and 9.

With the dawn of the exascale era, computer scientists and engineers are faced with tremendous challenges across all facets of the HPC system - scalability, performance, reliability, and power consumption. In particular, the power-performance benefit from one processor generation to the next is seeing ever-diminishing returns and will require fundamental changes in the way we approach computation. In fact, it is likely that different applications will require different types of accelerators in order to meet power, performance, and reliability requirements at scale. One potential type of accelerator, a dataflow architecture, diverges from the traditional sequentially executed instruction model into one that reflects the inherent instruction-level parallelism in a program. This work presents the initial steps toward a tool that can extract the control-dataflow graph from an application.

The performance of Nuclear Deterrence (ND) systems could be improved by adopting advanced commercial CMOS technologies. However, the radiation hardness of these ND systems must be assured. This project quantified dose rate upset thresholds, allowing us to evaluate whether advanced commercial technologies can be used in rad hard applications. This project also evaluated the susceptibility of advanced commercial technologies to neutron displacement damage and single event effects, and developed hardness assurance methods. As a result of this work, Sandia is now collaborating more closely with DoD agencies and their contractors to understand and improve the radiation hardness of advanced commercial technologies. Sandia Capabilities (SPHINX, lon Beam Lab, FPGA test capabilities) were developed and are being utilized by other programs. Staff were trained to do radiation survivability testing and developed proposals that were funded. Internal and external collaborations and partnerships were developed, including with Georgia Tech. This project received the Best Paper award at the 2019 Hardened Electronics And Radiation Technology (HEART) Conference.

The high-energy upgrade of the Z-Petawatt (ZPW) laser system involved a shot hiatus starting in October 2018 to allow the installation of full-aperture optics in the chain, including in the main amplifier section as well as in the branches associated with the ZPW compressor and co-injection (the narrowband nanosecond operating mode that is co-bored with Z-Beamlet). Laser shots resumed May 1^st, with most shots since being in the ns mode to reduce complexity (while conditioning optics and calibrating diagnostics) as well as to support experimental requests. To facilitate experimental needs while a final full-sized co-injection optic was prepared, a sub-apertured beam has been used for experimental applications and a full-apertured beam has been used to validate the system. In the latter case, the beam is directed to a large calorimeter placed before branching occurs to the ZPW compressor or co-injection. As of September 2019, full-aperture co-injection shots had ramped up to 960J in 2.3ns, with many cross-calibrations being used to map out a relative humidity dependency in the leaky diagnostic mirror. Several such cross-calibration shots have been done using a chirped pulse seed as well, verifying that the diagnostic calibrations are valid in both broadband and narrow-band modes. Summarizing, since May 1^st of 2019 when shots resumed, 93 full system shots were performed with the ZPW laser. This included 2 co-injection shots with ZBL into Z (in support of MagLIF), 26 Pecos gas cell shots (in support of MagLIF), 11 Conchas gas cell shots (in support of UXI applications development), 6 Jemez shots (in support of backlighter development), and 48 calorimeter shots (in support of validating the system performance). Of these 93 shots, 45 shots were in conjunction with ZBL.

Fast charging of lithium ion batteries is a critical enabler for mass EV adoption. Sandia National Laboratories (SNL) has been working with the University of Michigan to develop graphite anodes with novel 3D structures that facilitate faster charging while avoiding lithium plating, a main danger of unaided fast charging. SNL is using its unique high precision cycling capability, developed through ARPA-E funding, to characterize the ability of improved anodes to withstand fast charge and resist lithium plating, and the danger of lithium plating in present-day batteries.

Abstract not provided.

An emergent type of geopolitical warfare in recent years has been coined "Gray Zone competition," "competition short of armed conflict", or simply "competition," because it sits in a nebulous area between peace and conventional conflict. As such, the Gray Zone (GZ) "is a conceptual space between peace and war, occurring when actors use instruments of power to achieve political-security objectives ... [that] ... fall below the level of large-scale direct military conflict". It's not openly declared or defined; it's slower and is prosecuted more subtly using social, psychological, religious, information, cyber and other means to achieve physical or cognitive objectives, with or without violence. The lack of clarity of intent in competition activity makes it challenging to detect, characterize, and counter an enemy fighting this way.

Graph neural networks (GNNs) are an emerging model for learning graph embeddings and making predictions on graph structured data. However, robustness of graph neural networks is not yet well-understood. In this work, we focus on node structural identity predictions, where a representative GNN model is able to achieve near-perfect accuracy. We also show that the same GNN model is not robust to addition of structural noise, through a controlled dataset and set of experiments. Finally, we show that under the right conditions, graph-augmented training is capable of significantly improving robustness to structural noise.

The stakes have been raised with the onslaught of Information and Communications technological disruptive change coupled with wholesale integration of Information Technologies (IT) with Operational Technologies (OT). Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) systems are now virtualized, automated, computerized and connected. Examples include Smart Grid and Smart Metering in the Energy sector; Automated Self Driving Vehicles in the Transportation Systems sector; ICS/SCADA modernization in the Water and Wastewater Systems sector; and Telemedicine in the Healthcare and Public Health sector. Vast broadband expansion brings new connectivity to rural and tribal communities with impacts to business, education, anchor institutions, and first responders.

The increasingly large payloads of Unmanned Aircraft Systems (UASs) are exponentially increasing the threat to the nuclear enterprise. Current mitigation using RF interference is effective, but not feasible for fully autonomous systems and is prohibited in many areas. A new approach to UAS threat mitigation is needed that does not create radio interference but is effective against any type of vehicle. At the present time there is no commercial counter-UAS system that directly assaults the mems gyros and accelerometers in the Inertial Measurement Unit on the aircraft. But lab testing has revealed resonances in some IMUs that make them susceptible to moderate amplitude acoustic monotones. Sandia's energetic materials facility has enabled a quick and thorough exploration of UAS vulnerability to directed acoustic energy by using intense acoustic impulses to destabilize or down a UAS. We have: 1) detonated/deflagrated explosive charges of various sizes; 2) accurately measured impulse pressure and pulse duration; 3) determined what magnitude of acoustic insult to the IMU disrupts flight and for how long and; 4) determined if the air blast/shock wave on aircraft/propellers disrupts flight.

Sandia's work in micro and nanosystems/microelectronics is critical for DOE and other national security missions. Collocated REtD and production Is foundational to current and future capabilities as are partnerships, requiring continuous engagement in the following areas: Advanced REtD that deepens our scientific understanding of technologies to meet national needs and address emerging threats; External partnerships that strengthen the US semiconductor science and industrial base and leverage US and global supply chains; and Trusted and low-volume production for NNSA and synergistic national security missions that can't, won't, or shouldn't be supplied by industry. Microsystems are the primary enabler for agile, responsive weapon development and advanced functionality in the non-nuclear domain, and Sandia's MESA Complex is the only remaining US foundry with the proven ability to deliver the custom trusted and strategically radiation-hardened (TSRH) microelectronic components required to sustain the nation's nuclear deterrent. Four key strategies will advance MESA's technological leadership against future mission challenges.

This report characterizes Mach number effects in the hypersonic range from Mach 3 to Mach 7 for an arbitrary, computationally generated fragment. Force and moment coefficients in the x, y, z direction were successfully obtained and resulting trends were compared with theoretical expectations. Three orientations were tested in the form of unit vectors: [1,0,0], [0,1,0], and [0,0,1]; these represent the i, j, k principle axes. The final results ultimately showcased a trend very similar to that shown in previous literature — drag coefficients for a given orientation decreased with increasing Mach number by a very small amount in the hypersonic range. It was therefore concluded that the aerodynamic quantities used to obtain the fragment trajectory at low hypersonic Mach numbers (such as Mach 3) can be used to characterize the aerodynamic qualities at higher hypersonic Mach numbers (Mach 5, 7) with a reasonably small margin. A sensitivity study was also conducted which ultimately defined drag effects as a result of changing grid spacing and normal extrusion parameters for the unstructured tetrahedral mesh used in the simulation. Further work can be done in the form of experimental validation, specifically with regards to wind tunnels and ballistic range testing. Error can be reduced by testing more mesh variabilities and capturing a larger amount of fragment orientations. The results for force and moment characteristics dependent on meshing parameters and high Mach numbers are satisfactory and consistent with expected trends.

The Integrated Military Systems (IMS) Center at Sandia National Laboratories is a technical systems integrator of high-risk, high-impact systems. It provides cutting-edge technologies, subsystems, solutions, and products that provide a competitive advantage at every point of engagement. Building on more than six decades of experience, IMS seeks to pursue and provide solutions to the most challenging national security issues. We leverage technical capabilities from the Laboratories' core mission and innovatively apply technology to defense problems. IMS' diverse capabilities produce revolutionary technologies in the areas such as precision strike and defense, autonomy, NG&C, directed energy, signal processing/ATR, lethality and threat assessment, and systems engineering. Our talented engineers and staff take a problem from initial design all the way through testing and fielding.

Abuse tests are designed to determine the safe operating limits of HEV\PHEV energy storage devices. Testing is intended to achieve certain worst-case scenarios to yield quantitative data on cell\module\pack response, allowing for failure mode determination and guiding developers toward improved materials and designs. Standard abuse tests with defined start and end conditions are performed on all devices to provide comparison between technologies. New tests and protocols are developed and evaluated to more closely simulate real-world failure conditions. While robust mechanical models for vehicles and vehicle components exist, there is a gap for mechanical modeling of EV batteries. The challenge with developing a mechanical model for a battery is the heterogeneous nature of the materials and components (polymers, metals, metal oxides, liquids). This year saw the stand up of a new drop tower tester capable of providing dynamic mechanical test results.

We propose a bilevel optimization approach for the determination of parameters in nonlocal image denoising. We consider both spatial weights in front of the fidelity term, as well as weights within the kernel of the nonlocal operator. In both cases we investigate the differentiability of the solution operator in function spaces and derive a first order optimality system that characterizes local minima. For the numerical solution of the problems, we propose a second-order optimization algorithm in combination with a finite element discretization of the nonlocal denoising models and a computational strategy for the solution of the resulting dense linear systems. Several experiments are run in order to show the suitability of our approach.

With current lithium ion batteries optimized for performance under relatively low charge rate conditions, implementation of XFC has been hindered by drawbacks including Li plating, kinetic polarization, and heat dissipation. This project will utilize model-informed design of 3-D hierarchical electrodes to tune key XFCrelated variables like 1) bulk porosity/tortuosity 2) vertical pore diameter, spacing, and lattice 3) crystallographic orientation of graphite particles relative to exposed surfaces 4) interfacial chemistry of the graphite surfaces through "artificial sEr' formation using ALD 5) current collector surface roughness (aspect ratio, roughness factor, etc.). A key aspect of implementing novel electrodes is characterizing them in relevant settings. For this project, ultimately led out of University of Michigan by Neil Dasgupta, that includes both coin cell and 2+ Ah pouch cell testing, as well as comparison testing against baselines. Sandia National Labs will be conducting detailed cell characterization on iterative versions/improvements of the model-based hierarchical electrodes, as well as COTS cells for baseline comparisons. Key metrics include performance under fast charge conditions, as well as the absence or degree of lithium plating. Sandia will use their unique high precision cycling and rapid EIS capabilities to accurately characterize performance and any lithium plating during 6C charging and beyond, coupling electrochemical observations with cell teardown. Sandia will also design custom fixturing to cool cells during rapid charge, to decouple any kinetic effects brought about by cell heating and allow comparisons between different cells and charge rates. Using these techniques, Sandia will assess HOH electrodes from the University of Michigan, as well as aiding in iterative model and electrode design.

Neuromorphic computing is known for its integration of algorithms and hardware elements that are inspired by the brain. Conventionally, this nontraditional method of computing is used for many neural or learning inspired applications. Unfortunately, this has resulted in the field of neuromorphic computing being relatively narrow in scope. In this paper we discuss two research areas actively trying to widen the impact of neuromorphic systems. The first is Fugu, a high-level programming interface designed to bridge the gap between general computer scientists and those who specialize in neuromorphic areas. The second aims to map classical scientific computing problems onto these frameworks through the example of random walks. This elucidates a class of scientific applications that are conducive to neuromorphic algorithms.

This co-op was performed at Sandia National Laboratories (SNL) within the Weapon Surety Engineering 11 Department (9435), which is a customer-focused organization that provides engineering and system analysis expertise to ensure a safe deterrent throughout the entire weapon lifecycle. Engineers in 9435 develop a deep knowledge in both component and system function to support rigorous evaluation and mitigation of potential failure modes. They operate across multiple engineering disciplines (mechanical, electrical, computer, materials and systems engineering) with opportunities to develop crossdisciplinary capability. The team supports a variety of stockpile systems and is part of the product teams during development, design and production, as well as advanced concept and exploratory efforts. 9435 engineers use their extensive component and system knowledge, as well as experimental data and modeling, to ensure products function as intended with assured safety. Members of the department learn the function of multiple components, how those components integrate within the system and apply surety principles and processes to ensure a safe, secure, reliable, and effective U.S. nuclear deterrent.

The purpose of this work is to determine MTD effectiveness and cost for protecting non-IP C2 networks, experiment on networks used in satellite systems, and randomize features (e.g. device address) to prevent adversary from conducting reconnaissance.

The National Technology & Engineering Solutions of Sandia, LLC (NTESS) Prime Contract department manages an organizational conflicts of interest (OCI) program that is designed to provide NTESS employees an accessible means to assist in identifying potential OCI risk and providing development support services to avoid, neutralize, or mitigate the risk. The OCI program is responsible for maintaining a regulatory infrastructure, such as corporate OCI policy and training content, to comply with NTESS's obligations set forth in prime contract clauses H-4: ORGANIZATIONAL CONFLICT OF INTEREST (OCI) — SPECIAL PROVISION and Section I Department of Energy Acquisition Regulation 952.209-72, Organizational Conflicts of Interest, Alternate I, paragraph (c)(1), Disclosure After Award. Risk identification is a key OCI program priority. Providing employees with a straight-forward, accessible and navigable mechanism to identify potential OCI risk will assist the OCI program in meeting this priority.

The ever increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/assembly language format in support of manual static code analysis. Because of the highly manual and resource intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight data, unsupervised data flow methodology is described which uses highly-correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis.

Lithographic quantum dots (QDs) are highly controllable few-level quantum systems created in semiconductor nanoelectronic devices, with a variety of scientific applications. These include technologically-driven applications like quantum computing and more fundamental applications in which they serve as a platform for exploring basic many-body physics. This document is a brief summary of my Ph.D. research so far and the directions with which I intend to continue it. Highlights include theoretical efforts to understand and design qubits in germanium hole QDs, as well as explorations of the possibility of using QDs coupled to nearby baths for analog simulation of quantum impurity models.

Molybdenum-99 (Moly 99) is a critical raw material for Technetium (Tc) 99m, a radioactive isotope most widely used in nuclear medicine procedures. Moly 99 has a short half-life of about six hours, which means it cannot be stockpiled. When Moly 99 decays, it turns into Tc 99m, which has a half-life of 214,000 years. The photon energy emitted from the decay of Moly 99 is used in a variety of nuclear imaging technologies such as gamma cameras. Furthermore, radiopharmaceutical manufacturers use the photon energy emitted from the decay of Moly 99 to produce generators for hospitals, clinics, and radiopharmacies. Once Moly 99 decays to Tc 99m, it is used to make individual patient doses for a variety of diagnostic imaging procedures. The Moly 99 Reactor Design is a conceptual blueprint for Moly 99 production that does not use weapon-grade uranium; instead, the reactor has a target core of low-enriched uranium. The reactor design is small, reaching a foot-and-a-half in height and diameter and consumes less than two megawatts of power. The reactor's only purpose is for medical isotope production and with every fission, Moly 99 is produced.

Turbulence simulations play a key role in advancing the general understanding of the physical properties of turbulence and in interpreting astrophysical observations of turbulent plasmas. For the sake of simplicity, however, turbulence simulations are often conducted in the isothermal limit. Given that the majority of astrophysical systems are not governed by isothermal dynamics, we aim to quantify the impact of thermodynamics on the physics of turbulence, through varying adiabatic index, γ, combined with a range of optically thin cooling functions. In this paper, we present a suite of ideal magnetohydrodynamics simulations of thermally balanced stationary turbulence in the subsonic, super-Alfvénic, high (ratio of thermal to magnetic pressure) regime, where turbulent dissipation is balanced by two idealized cooling functions (approximating linear cooling and free-free emission) and examine the impact of the equation of state by considering cases that correspond to isothermal, monatomic, and diatomic gases. We find a strong anticorrelation between thermal and magnetic pressure independent of thermodynamics, whereas the strong anticorrelation between density and magnetic field found in the isothermal case weakens with increasing γ. Similarly, the linear relation between variations in density and thermal pressure with sonic Mach number becomes steeper with increasing γ. This suggests that there exists a degeneracy in these relations with respect to thermodynamics and Mach number in this regime, which is dominated by slow magnetosonic modes. These results have implications for attempts to infer (e.g.,) Mach numbers from (e.g.,) Faraday rotation measurements, without additional information regarding the thermodynamics of the plasma. However, our results suggest that this degeneracy can be broken by utilizing higher-order moments of observable distribution functions.

The mechanical properties of additively manufactured metals tend to show high variability, due largely to the stochastic nature of defect formation during the printing process. This study seeks to understand how automated high throughput testing can be utilized to understand the variable nature of additively manufactured metals at different print conditions, and to allow for statistically meaningful analysis. This is demonstrated by analyzing how different processing parameters, including laser power, scan velocity, and scan pattern, influence the tensile behavior of additively manufactured stainless steel 316L utilizing a newly developed automated test methodology. Microstructural characterization through computed tomography and electron backscatter diffraction is used to understand some of the observed trends in mechanical behavior. Specifically, grain size and morphology are shown to depend on processing parameters and influence the observed mechanical behavior. In the current study, laser-powder bed fusion, also known as selective laser melting or direct metal laser sintering, is shown to produce 316L over a wide processing range without substantial detrimental effect on the tensile properties. Ultimate tensile strengths above 600 MPa, which are greater than that for typical wrought annealed 316L with similar grain sizes, and elongations to failure greater than 40% were observed. It is demonstrated that this process has little sensitivity to minor intentional or unintentional variations in laser velocity and power.

Silicon photonics is a platform that enables densely integrated photonic components and systems and integration with electronic circuits. Depletion mode modulators designed on this platform suffer from a fundamental frequency response limit due to the mobility of carriers in silicon. Lithium niobate-based modulators have demonstrated high performance, but the material is difficult to process and cannot be easily integrated with other photonic components and electronics. In this manuscript, we simultaneously take advantage of the benefits of silicon photonics and the Pockels effect in lithium niobate by heterogeneously integrating silicon photonic-integrated circuits with thin-film lithium niobate samples. We demonstrate the most CMOS-compatible thin-film lithium niobate modulator to date, which has electro-optic 3 dB bandwidths of 30.6 GHz and half-wave voltages of 6.7 V×cm. These modulators are fabricated entirely in CMOS facilities, with the exception of the bonding of a thin-film lithium niobate sample post fabrication, and require no etching of lithium niobate.

Lithium-ion battery technology is rapidly being adopted in transportation applications and energy storage industries. Safety concerns, in particular, fire and explosion hazards, are threatening widespread adoption. In some failure events, lithium-ion cells can undergo thermal runaway, which can result in the release of flammable gases that pose fire and explosion hazards for the compartment housing the cells. However, there is little available information characterizing the flammability properties of the gases released after cell thermal runaway. In this paper, analytical and modeling methods to estimate explosion characteristics, such as lower flammability limit, laminar flame speed, and maximum over-pressure are evaluated for use in quantifying the effect of cell chemistry, state-of-charge and other parameters on the overall explosion hazard potential for confined cells.

Molecular-level understanding and characterization of solvation environments are often needed across chemistry, biology, and engineering. Toward practical modeling of local solvation effects of any solute in any solvent, we report a static and all-quantum mechanics-based cluster-continuum approach for calculating single-ion solvation free energies. This approach uses a global optimization procedure to identify low-energy molecular clusters with different numbers of explicit solvent molecules and then employs the smooth overlap for atomic positions learning kernel to quantify the similarity between different low-energy solute environments. From these data, we use sketch maps, a nonlinear dimensionality reduction algorithm, to obtain a two-dimensional visual representation of the similarity between solute environments in differently sized microsolvated clusters. After testing this approach on different ions having charges 2+, 1+, 1-, and 2-, we find that the solvation environment around each ion can be seen to usually become more similar in hand with its calculated single-ion solvation free energy. Without needing either dynamics simulations or an a priori knowledge of local solvation structure of the ions, this approach can be used to calculate solvation free energies within 5% of experimental measurements for most cases, and it should be transferable for the study of other systems where dynamics simulations are not easily carried out.

Molecular dynamics simulations confirm recent extensional flow experiments showing ring polymer melts exhibit strong extension-rate thickening of the viscosity at Weissenberg numbers Wi « 1. Thickening coincides with the extreme elongation of a minority population of rings that grows with Wi. The large susceptibility of some rings to extend is due to a flow-driven formation of topological links that connect multiple rings into supramolecular chains. Links form spontaneously with a longer delay at lower Wi and are pulled tight and stabilized by the flow. Once linked, these composite objects experience larger drag forces than individual rings, driving their strong elongation. The fraction of linked rings depends non-monotonically on Wi, increasing to a maximum when Wi 1 before rapidly decreasing when the strain rate approaches 1/T_e.

Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of ML-IAPs based on four local environment descriptors --- Behler-Parrinello symmetry functions, smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment tensors --- using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model, and consequently computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

Energy storage systems with Li-ion batteries are increasingly deployed to maintain a robust and resilient grid and facilitate the integration of renewable energy resources. However, appropriate selection of cells for different applications is difficult due to limited public data comparing the most commonly used off-the-shelf Li-ion chemistries under the same operating conditions. This article details a multi-year cycling study of commercial LiFePO4 (LFP), LiNixCoyAl1-x-yO2 (NCA), and LiNixMnyCo1-x-yO2 (NMC) cells, varying the discharge rate, depth of discharge (DOD), and environment temperature. The capacity and discharge energy retention, as well as the round-trip efficiency, were compared. Even when operated within manufacturer specifications, the range of cycling conditions had a profound effect on cell degradation, with time to reach 80% capacity varying by thousands of hours and cycle counts among cells of each chemistry. The degradation of cells in this study was compared to that of similar cells in previous studies to identify universal trends and to provide a standard deviation for performance. All cycling files have been made publicly available at batteryarchive.org, a recently developed repository for visualization and comparison of battery data, to facilitate future experimental and modeling efforts.

An improved meshing scheme allows simulations to proceed further than before, though convergence difficulty is still encountered upon full opening. Changing the initial shape of the window does not greatly affect the rate of opening. Changing the initial window stress greatly affects the initial snap-back upon opening, but the long-term trendline as full opening nears is less strongly affected. At 60 psi pressure, the opening time is approximately 4-4.5 μs for the configurations tested in this study.

It is well established that the variability in mechanical response and ultimate failure of additively manufactured metals correlates to uncertainties introduced in the build process, among which include internal void structure and residual stresses. Here, we quantify the aforementioned variabilities in 316L stainless steels by conducting simulations in Sierra/SM of the specimens/geometries used in Sandia's third fracture challenge (SFC3). We leverage the simulations and experimental work presented in 6 to construct a statistical representation of the internal void structure of the tension specimen used for material parameter calibration as well as the "challenge" geometry. Voided mesh samples of both specimens are generated given a set of statistical variables, and the physics simulations are conducted for multiple sets of realization to determine the effects of void structure on variability in the fracture paths and displacement-to-failure. Lastly, a series of simulations are presented which highlight the effect of the powder bed fusion additive manufacturing process on the formation of residual stresses in the as-built geometries.

Li deposition at the graphitic anode is widely reported in literature as one of the leading causes of capacity fade in lithium-ion batteries (LIBs). Previous literature has linked Li deposition resulting from low-temperature ageing to diminished safety characteristics, however no current research has probed the effects of Li deposition on the abuse response of well-characterized cells. Using overtemperature testing, a relationship between increased concentrations of Li deposition and exacerbated abuse response in 1 Ah pouch cells has been established. A novel Li deposition technique is also investigated, where cells with n:p < 1 (anode-limiting) have been cycled at a high rate to exploit Li+ diffusion limitations at the anode. Scanning Electron Microscopy of harvested anodes indicates substantial Li deposition in low n:p cells after 20 cycles, with intricate networks of Li(s) deposits which hinder Li+ intercalation/deintercalation. Peak broadening and decreased amplitude of differential capacity plots further validates a loss of lithium inventory to Li+ dissolution, and Powder X-ray Diffraction indicates Li+ intercalation with staging in anode interstitial sites as the extent of Li deposition increases. A cradle-to-grave approach is leveraged on cell fabrication and testing to eliminate uncertainty involving the effects of cell additives on Li deposition and other degradation mechanisms.

The heat generated during a single cell failure within a high energy battery system can force adjacent cells into thermal runaway, creating a cascading propagation effect through the entire system. This work examines the response of modules of stacked pouch cells after thermal runaway is induced in a single cell. The prevention of cascading propagation is explored on cells with reduced states of charge and stacks with metal plates between cells. Reduced states of charge and metal plates both reduce the energy stored relative to the heat capacity, and the results show how cascading propagation may be slowed and mitigated as this varies. These propagation limits are correlated with the stored energy density. Results show significant delays between thermal runaway in adjacent cells, which are analyzed to determine intercell contact resistances and to assess how much heat energy is transmitted to cells before they undergo thermal runaway. A propagating failure of even a small pack may stretch over several minutes including delays as each cell is heated to the point of thermal runaway. This delay is described with two new parameters in the form of gap-crossing and cell-crossing time to grade the propensity of propagation from cell to cell.

The mechanical degradation of all-solid-state Li-ion batteries (ASSLBs) is expected to be more severe than that in traditional Li-ion batteries with liquid electrolytes due to the additional mechanical constraints imposed by the solid electrolyte on the deformation of electrodes. Cracks and fractures could occur both inside the solid electrolyte (SE) and at the SE/electrode interfconce. A coupled electrochemical-mechanical model was developed and solved by the Finite Element Method (FEM) to evaluate the stress development in ASSLBs. Two sources of volume change were considered, namely the expansion/shrinkage of electrodes due to lithium concentration change and the interphase formation at the SE/electrode interface due to the decomposition of SEs. The most plausible solid electrolyte decomposition reactions and their associated volume change were predicted by density functional theory (DFT) calculations. It was found that the stress associated with a volume change due to solid electrolyte decomposition can be much more significant than that of electrode volumetric changes associated with Li insertion/extraction. This model can be used to design 3D ASSLB architectures to minimize their internal stress generation.

Nuclear pore complexes (NPCs) regulate all cargo traffic across the nuclear envelope. The transport conduit of NPCs is highly enriched in disordered phenylalanine/glycine-rich nucleoporins (FG-Nups), which form a permeability barrier of still elusive and highly debated molecular structure. Here we present a microfluidic device that triggered liquid-to-liquid phase separation of FG-Nups, which yielded droplets that showed typical properties of a liquid state. On the microfluidic chip, droplets were perfused with different transport-competent or -incompetent cargo complexes, and then the permeability barrier properties of the droplets were optically interrogated. We show that the liquid state mimics permeability barrier properties of the physiological nuclear transport pathway in intact NPCs in cells: that is, inert cargoes ranging from small proteins to large capsids were excluded from liquid FG-Nup droplets, but functional import complexes underwent facilitated import into droplets. Collectively, these data provide an experimental model of how NPCs can facilitate fast passage of cargoes across an order of magnitude in cargo size.

On 12/2/2019 at approximately 1747 hours, a vehicle headed east along East Avenue (LLNL property) made contact with a Sandia subcontractor on foot headed north through the marked crosswalk. The Sandia subcontractor required hospitalization as a result of the incident.

The U.S. Department of Energy (DOE) established a need to understand the thermal-hydraulic properties of dry storage systems for commercial spent nuclear fuel (SNF) in response to a shift towards the storage of high-burnup (HBU) fuel (> 45 gigawatt days per metric ton of uranium, or GWd/MTU). This shift raises concerns regarding cladding integrity, which faces increased risk at the higher temperatures within spent fuel assemblies present within HBU fuel compared to low-burnup fuel (≤ 45 GWd/MTU). The dry cask simulator (DCS) was previously built at Sandia National Laboratories (SNL) in Albuquerque, New Mexico to produce validation-quality data that can be used to test the validity of the modeling used to determine cladding temperatures in modern vertical dry casks. These temperatures are critical to evaluating cladding integrity throughout the storage cycle of commercial spent nuclear fuel. In this study, a model validation exercise was carried out using the data obtained from dry cask simulator testing in the vertical, aboveground configuration. Five modeling institutions – Nuclear Regulatory Commission (NRC), Pacific Northwest National Laboratory (PNNL), Centro de Investigaciones Energéticas, MedioAmbientales y Tecnológicas (CIEMAT), and Empresa Nacional del Uranio, S.A., S.M.E. (ENUSA) in collaboration with Universidad Politécnica de Madrid (UPM) – were granted access to the input parameters from SAND2017-13058R, “Materials and Dimensional Reference Handbook for the Boiling Water Reactor Dry Cask Simulator”, and results from the vertical aboveground BWR dry cask simulator tests reported in NUREG/CR-7250, “Thermal-Hydraulic Experiments Using A Dry Cask Simulator”. With this information, each institution was tasked to calculate minimum, average, and maximum fuel axial temperature profiles for the fuel region as well as the axial temperature profiles of the DCS structures. Transverse temperature profiles and air mass flow rates within the dry cask simulator were also calculated. These calculations were done using modeling codes (ANSYS FLUENT, STARCCM+, or COBRA-SFS), each with their own unique combination of modeling assumptions and boundary conditions. For this validation study, four test cases of the vertical, aboveground dry cask simulator were considered, defined by two independent variables – either 0.5 kW or 5 kW fuel assembly decay heat, and either 100 kPa or 800 kPa internal helium pressure. For the results in this report, each model was assigned a model number. Three of the models used porous media model representations of the fuel, two models used explicit fuel representations, and one model used an explicit subchannel representation of the fuel. Even numbers were assigned to explicit fuel models and odd numbers were assigned to porous media models. The plots provided in Chapter 3 of this report show the axial and transverse temperature profiles obtained from the dry cask simulator experiments in the aboveground configuration and the corresponding models used to describe the thermal-hydraulic behavior of this system. The tables provided in Chapter 3 illustrate the closeness of fit of the model data to the experiment data through root mean square (RMS) calculations of the error in peak cladding temperatures (PCTs), average fuel temperatures across six axial levels, transverse temperatures across the PCT locations for the four test cases, and air mass flow rates. The peak cladding temperature is typically the most important target variable for cask performance, and all models capture the PCT within 5% RMS error. Two models show comparable fits to experimental results when considering the combined RMS error of all target variables. Since one uses a porous media representation of the fuel while the other uses an explicit fuel representation, it can be concluded that the porous media fuel representation can achieve modeling calculation results of peak cladding temperatures, average fuel temperatures, transverse temperatures, and air mass flow rates that are comparable to explicit fuel representation modeling results.

Exposure testing was performed on CoCrFeMnNi equiatomic high entropy alloy (HEA) produced via directed energy deposition additive manufacturing in NaNO3-KNO3 (60-40 wt%) molten salt at 500 °C for 50 h to evaluate the corrosion performance and oxide film chemistry of the HEA. Potentiodynamic electrochemical corrosion testing, scanning electron microscopy, focused ion beam milling coupled with energy dispersive spectroscopy, Raman spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and inductively coupled plasma optical emission spectroscopy were used to analyze the corrosion behavior and chemistry of the HEA/nitrate molten salt system. The CoCrFeMnNi HEA exhibited a higher passive current density during potentiodynamic polarization testing than steel alloys SS316L and 4130 and the high-Ni alloy 800 H in identical conditions. The oxide film was primarily composed of a (Mn,Co,Ni)Fe2O4 spinel with a vertical plate-like morphology at the surface. Cr and Ni were found to be totally depleted at the outer surface of the oxide and dissolved in high concentrations in the molten salt. While Cr was expected to dissolve into the molten salt, the high concentration of dissolved Ni has not been observed with traditional alloys, suggesting that Ni is less stable in the spinel when Mn and Co are present.

Rechargeable alkaline batteries containing zinc anodes suffer from redistribution of active material due to the high solubility of ZnO in the electrolyte, limiting achievable capacity and lifetime. Here, we investigate pre-saturating the KOH electrolyte with ZnO as a strategy to mitigate this issue, utilizing rechargeable Ni-Zn cells. In contrast to previous reports featuring this approach, we use more practical limited-electrolyte cells and systematically study ZnO saturation at different levels of zinc depth-of-discharge (DODZn), where the pre-dissolved ZnO is included in the total system capacity. Starting with 32 wt. % KOH, cells tested at 14%, 21%, and 35% DODZn with ZnO-saturated electrolyte exhibit 191%, 235%, and 110% longer cycle life respectively over identically tested cells with ZnO-free electrolyte, with similar energy efficiency and no voltage-related energy losses. Furthermore, anodes cycled in ZnO-saturated electrolyte develop more favorable compact zinc deposits with less overall mass loss. The effect of initial KOH concentration was also studied, with ZnO saturation enhancing cycle life for 32 wt % and 45 wt % KOH but not for 25 wt % KOH, likely due to cell failure by passivation rather than shorting. The simplicity of ZnO addition and its beneficial effect at high zinc utilization make it a promising means to make secondary alkaline zinc batteries more commercially viable.

The U.S. Navy is investing in the development of new technologies that broaden warship capabilities and maintain U.S. naval superiority. Specifically, Naval Sea Systems Command (NAVSEA) is supporting the development of power systems technologies that enable the Navy to realise an all-electric warship. A challenge to fielding an all-electric power system architecture includes minimising the size of energy storage systems (ESS) while maintaining the response times necessary to support potential pulsed loads. This work explores the trade-off between energy storage size requirements (i.e. mass) and performance (i.e. peak power, energy storage, and control bandwidth) in the context of a power system architecture that meets the needs of the U.S. Navy. In this work, the simulated time domain responses of a representative power system were evaluated under different loading conditions and control parameters, and the results were considered in conjunction with sizing constraints of and estimated specific power and energy densities of various storage technologies. The simulation scenarios were based on representative operational vignettes, and a Ragone plot was used to illustrate the intersection of potential energy storage sizing with the energy and power density requirements of the system. Furthermore, the energy storage control bandwidth requirements were evaluated by simulation for different loading scenarios. Two approaches were taken to design an ESS: one based only on time domain power and energy requirements from simulation and another based on bandwidth (specific frequency) limitations of various technologies.

An open question in the metal hydride community is whether there are simple, physics-based design rules that dictate the thermodynamic properties of these materials across the variety of structures and chemistry they can exhibit. While black box machine learning-based algorithms can predict these properties with some success, they do not directly provide the basis on which these predictions are made, therefore complicating the a priori design of novel materials exhibiting a desired property value. In this work we demonstrate how feature importance, as identified by a gradient boosting tree regressor, uncovers the strong dependence of the metal hydride equilibrium H2 pressure on a volume-based descriptor that can be computed from just the elemental composition of the intermetallic alloy. Elucidation of this simple structure-property relationship is valid across a range of compositions, metal substitutions, and structural classes exhibited by intermetallic hydrides. This permits rational targeting of novel intermetallics for high-pressure hydrogen storage (low-stability hydrides) by their descriptor values, and we predict a known intermetallic to form a low-stability hydride (as confirmed by density functional theory calculations) that has not yet been experimentally investigated.

The effects of 14-MeV neutron displacement damage (DD) on waveguide (WG)-integrated germanium-on-silicon p-i-n photodiodes (PDs) for silicon photonics have been investigated up to the fluences of 7.5× 1012 n/cm2 (14 MeV) or 1.4× 1013 n1-MeVeq/cm2(Si). This article includes the measurements of dark current-voltage characteristics across temperature from 150 to 375 K, measurements of PD junction capacitance, spectral response measurements from 1260 to 1360 nm, and frequency-response measurements. The devices are found to be susceptible to DD-induced carrier removal effects; however, they also continue to operate without meaningful impact to performance for the DD dose levels examined. Since the PD test chips include silicon photonic integrated grating couplers and WGs, which carry the optical signal to the PD, some assessment of the impact of DD on these passive devices can also be inferred. This article does not examine the short-term annealing or transient behavior of the DD, and instead, it has only considered the lasting damage that remains after any initial period of room-temperature annealing.

Residual neural networks (ResNets) are a promising class of deep neural networks that have shown excellent performance for a number of learning tasks, e.g., image classification and recognition. Mathematically, ResNet architectures can be interpreted as forward Euler discretizations of a nonlinear initial value problem whose time-dependent control variables represent the weights of the neural network. Hence, training a ResNet can be cast as an optimal control problem of the associated dynamical system. For similar time-dependent optimal control problems arising in engineering applications, parallel-in-time methods have shown notable improvements in scalability. This paper demonstrates the use of those techniques for efficient and effective training of ResNets. The proposed algorithms replace the classical (sequential) forward and backward propagation through the network layers with a parallel nonlinear multigrid iteration applied to the layer domain. This adds a new dimension of parallelism across layers that is attractive when training very deep networks. From this basic idea, we derive multiple layer-parallel methods. The most efficient version employs a simultaneous optimization approach where updates to the network parameters are based on inexact gradient information in order to speed up the training process. Using numerical examples from supervised classification, we demonstrate that the new approach achieves a training performance similar to that of traditional methods, but enables layer-parallelism and thus provides speedup over layer-serial methods through greater concurrency.

Knowing when, why, and how materials evolve, degrade, or fail in radiation environments is pivotal to a wide range of fields from semiconductor processing to advanced nuclear reactor design. A variety of methods, including optical and electron microscopy, mechanical testing, and thermal techniques, have been used in the past to successfully monitor the microstructural and property evolution of materials exposed to extreme radiation environments.Acoustic techniques have also been used in the past for this purpose, although most methodologies have not achieved widespread adoption. However, with an increasing desire to understand microstructure and property evolution in situ, acoustic methods provide a promising pathway to uncover information not accessible to more traditional characterization techniques. This work highlights how two different classes of acoustic techniques may be used to monitor material evolution during in situ ion beam irradiation. The passive listening technique of acoustic emission is demonstrated on two model systems, quartz and palladium, and shown to be a useful tool in identifying the onset of damage events such as microcracking.An active acoustic technique in the form of transient grating spectroscopy is used to indirectly monitor the formation of small defect clusters in copper irradiated with self-ions at high temperature through the evolution of surface acoustic wave speeds.These studies together demonstrate the large potential for using acoustic techniques as in situ diagnostics. Such tools could be used to optimize ion beam processing techniques or identify modes and kinetics of materials degradation in extreme radiation environments.

The US Department of Energy Office of Nuclear Energy is conducting a brine availability heater test to characterize the thermal, mechanical, hydrological and chemical response of salt at elevated temperatures. In the heater test, brines will be collected and analyzed for chemical compositions. In order to support the geochemical modeling of chemical evolutions of the brines during the heater test, we are recalibrating and validating the solubility models for the mineral constituents in salt formations up to 100°C, based on the solubility data in multiple component systems as well as simple systems from literature. In this work, we systematically compare the model-predicted values based on the various solubility models related to the constituents of salt formations, with the experimental data. As halite is the dominant constituent in salt formations, we first test the halite solubility model in the Na-Mg-Cl dominated brines. We find the existing halite solubility model systematically over-predict the solubility of halite. We recalibrate the halite model, which can reproduce halite solubilities in Na-Mg-Cl dominated brines well. As gypsum/anhydrite in salt formations controls the sulfate concentrations in associated brines, we test the gypsum solubility model in NaCl solutions up to 5.87 mol•kg-1 from 25°C to 50°C. The testing shows that the current gypsum solubility model reproduces the experimental data well when NaCl concentrations are less than 1 mol•kg-1. However, at NaCl concentrations higher than 1, the model systematically overpredicts the solubility of gypsum. In the Na - Cl - SO4 - CO3 system, the validation tests up to 100°C demonstrate that the model excellently reproduces the experimental data for the solution compositions equilibrated with one single phase such as halite (NaCl) or thenardite (Na2SO4), with deviations equal to, or less than, 1.5 %. The model is much less ideal in reproducing the compositions in equilibrium with the assemblages of halite + thenardite, and of halite + thermonatrite (Na2CO3•H2O), with deviations up to 31 %. The high deviations from the experimental data for the multiple assemblages in this system at elevated temperatures may be attributed to the facts that the database has the Pitzer interaction parameters for Cl - CO3 and SO4 - CO3 only at 25°C. In the Na - Ca - SO4 - HCO3 system, the validation tests also demonstrate that the model reproduces the equilibrium compositions for one single phase such as gypsum better than the assemblages of more than one phase.

Kinetic simulations of plasma phenomena during and after formation of the conductive plasma channel of a nanosecond pulse discharge are analyzed and compared to existing experimental measurements. Particle-in-cell with direct simulation Monte Carlo collisions (PIC-DSMC) modeling is used to analyze a discharge in helium at 200 Torr and 300 K over a 1 cm gap. The analysis focuses on physics that would not be reproduced by fluid models commonly used at this high number density and collisionality, specifically non-local and stochastic phenomena. Similar analysis could be used to improve the predictive capability of lower fidelity or reduced order models. First, the modeling results compare favorably with experimental measurements of electron number density, temperature, and 1D electron energy distribution function at the same conditions. Second, it is shown that the ionization wave propagates in a stochastic, stepwise manner, dependent on rare, random ionization events ahead of the ionization wave when the ionization fraction in front of the ionization wave is very low, analagous to the stochastic branching of streamers in 3D. Third, analysis shows high-energy runaway electrons accelerated in the cathode layer produce electron densities in the negative glow region over an order of magnitude above those in the positive column. Future work to develop reduced order models of these two phenomena would improve the accuracy of fluid plasma models without the cost of PIC-DSMC simulations.

Classical molecular dynamics (MD) simulations were performed to provide a conceptual understanding of the amorphous-crystalline interface for a candidate negative thermal expansion (NTE) material, ZrW2O8. Simulations of pressure-induced amorphization at 300 K indicate that an amorphous phase forms at pressures of 10 GPa and greater, and this phase persists when the pressure is subsequently decreased to 1 bar. However, the crystalline phase is recovered when the slightly distorted 5 GPa phase is relaxed to 1 bar. Simulations were also performed on a two-phase model consisting of the high-pressure amorphous phase in direct contact with the crystalline phase. Upon equilibration at 300 K and 1 bar, the crystalline phase remains unchanged beyond a thin layer of disrupted structure at the crystalline-amorphous interface. Differences in local atomic structure at the interface are quantified from the simulation trajectories.

Pyroshock events from the actuation of separation devices in satellites and launch vehicles are potentially damaging, very short, high intensity events with high frequency content. The pyroshock damage risk is mitigated somewhat by the fact that the shock intensity is attenuated by the spacecraft structure. The NASA and MIL standards, developed from extensive tests performed in the 1960’s, provide pyroshock attenuation guidelines for various structures common to spacecraft and launch vehicles. In this paper, we present the results from a numerical investigation of pyroshock attenuation in cylindrical shell structures. Pyroshock events were modeled using Sandia National Laboratories’ engineering mechanics simulation codes, specifically Sierra/SD. Upon verifying the numerical simulation results against a NASA-HDBK-7005 curve, various structural features were added and design variables were varied to investigate their effects on pyroshock wave propagation and attenuation. The results showed that current numerical simulation tools, given appropriate tuning parameters, are capable of modeling pyroshock events in a simple cylindrical geometry at a reasonable cost. The numerical simulations showed that the presence of geometric features had greater attenuating effects than previously understood. However, shock attenuation levels were less sensitive to design variables of the structural features than expected.

Porosity in additively manufactured metals can reduce material strength and is generally undesirable. Although studies have shown relationships between process parameters and porosity, monitoring strategies for defect detection and pore formation are still needed. In this paper, instantaneous anomalous conditions are detected in-situ via pyrometry during laser powder bed fusion additive manufacturing and correlated with voids observed using post-build micro-computed tomography. Large two-color pyrometry data sets were used to estimate instantaneous temperatures, melt pool orientations and aspect ratios. Machine learning algorithms were then applied to processed pyrometry data to detect outlier images and conditions. It is shown that melt pool outliers are good predictors of voids observed post-build. With this approach, real time process monitoring can be incorporated into systems to detect defect and void formation. Alternatively, using the methodology presented here, pyrometry data can be post processed for porosity assessment.

A hybrid femtosecond/picosecond CARS instrument probed the Q-branch of molecular hydrogen in the multiphase plume of an aluminized solid propellant burn. A single 50 fs regenerative amplifier pumped an OPA and etalon, providing the Stokes and probe pulses respectively. The spectra were recorded at 1 kHz and fit to synthetic spectra to infer the gas rotational temperature. Recorded spectra required dynamic background corrections due to the intense emission of the propellant plume. Two different days of propellant burns were studied, with the lessons learned from nonresonant background issues with the first test applied to the second. For the second attempt, three burns were examined, with mean temperatures differing only by 30 K with a combined mean of 2574 K.

Three ultra-high-speed, 10 MHz, cameras imaged the time-resolved decay of laser-induced incandescence (LII) from soot in a turbulent non-premixed ethylene jet flame. Cameras were equipped with a stereoscope allowing each CMOS array to capture two separate views of the flame. The resulting six views were reconstructed into a volumetric soot decay series using commercially available DaVis tomographic software by LaVision. Primary soot particle sizes were estimated from the decay time history on a per voxel basis by comparing measured signals to an LII model. Experimentally quantified soot particle sizes agree with existing predictions and previous measurements.

Two techniques have extended the effective frequency limits of postage-stamp PIV, in which a pulse-burst laser and very small fields of view combine to achieve high repetition rates. An interpolation scheme reduced measurement noise, raising the effective frequency response of previous 400-kHz measurements from about 120 kHz to 200 kHz. The other technique increased the PIV acquisition rate to very nearly MHz rates (990 kHz) by using a faster camera. Charge leaked through the camera shift register at these framing rates but this was shown not to bias the measurements. The increased framing rate provided oversampled data and enabled use of multi-frame correlation algorithms for a lower noise floor, increasing the effective frequency response to 240 kHz where the interrogation window size begins to spatially filter the data. Good agreement between the interpolation technique and the MHz-rate PIV measurements was established. The velocity spectra suggest turbulence power-law scaling in the inertial subrange steeper than the theoretical-5/3 scaling, attributed to an absence of isotropy.

This paper presents a practical methodology for propagating and combining the effects of random variations of several continuous scalar quantities and several random-function quantities affecting the failure pressure of a heated pressurized vessel. The random functions are associated with stress-strain curve test-to-test variability in replicate material strength tests (uniaxial tension tests) on nominally identical material specimens. It is demonstrated how to effectively propagate the curve-to-curve discrete variations and appropriately account for the small sample size of functional data realizations. This is coordinated with the propagation of aleatory variability described by uncertainty distributions for continuous scalar quantities of pressure-vessel wall thickness, weld depth, and thermal-contact factor. Motivated by the high expense of the pressure vessel simulations of heating, pressurization, and failure, a simple dimension-and order-adaptive polynomial response surface approach is used to propagate effects of the random variables and enable uncertainty estimates on the error contributed by using the surrogate model. Linear convolution is used to aggregate the resultant aleatory uncertainty from the parametrically propagated random variables with an appropriately conservative probability distribution of aleatory effects from propagating the multiple stress-strain curves for each material. The response surface constructions, Monte Carlo sampling of them for uncertainty propagation, and linear sensitivity analysis and convolution procedures, are demonstrated with standard EXCEL spreadsheet functions (no special software needed).

We demonstrate simultaneous monitoring of temperature and pressure using a hybrid femtosecond/picosecond pure-rotational CARS technique in a one-dimensional line-imaging configuration. The method employs two detection channels and two 60-ps-duration probe laser beams with independently adjustable time delays from the broadband 35-fs pump/Stokes pulse. Simultaneous temperature and pressure monitoring is demonstrated along the centerline of a canonical underexpanded compressible air jet flow emanating from a choked, sonic nozzle. Temperature is measured almost independently of pressure by analyzing CARS spectra obtained with a probe pulse near zero time delay for nearly collision-free acquisition. Pressure is obtained from spectra acquired with long probe time delays to sample the impact of gas-phase collisions. The CARS measurements were obtained in both time-averaged and single-laser-shot mode with 67 µm spatial resolution along the jet axis along a nominally 6-mm line. The measurements span a temperature and pressure range of T = 70-300 K and P = 0.05-1.2 atm.

Multi-Input-Multi-Output (MIMO) vibration testing is considered more representative of the true loading environment (flight or wind induced vibration) where the inputs are not through a single point. The derivation of N inputs for testing typically involves matching the response at M locations (outputs). This involves inversion of a N × M Transfer Functions (TRF) matrix corresponding to the N input and M output locations. The matrix inversion is affected by both mathematical and physical parameters (ill-conditioned matrix, structural modes, signal noise). Tikhonov regularization is commonly used in inverting an ill-conditioned N × M matrix. A low value of the Tikhonov regularization parameter minimizes the distortion of the original equations while a higher value can minimize error. In practice this introduces an interesting dilemma where obtaining realistic input loads and maintaining accuracy of output are often pitted against each other. A study was conducted using data synthesized from a simply-supported plate structure with known vibration modes with added noise at outputs. The objective of the study was to understand how noise or errors in the output and the Transfer function affect the input. This leads to a more judicious choice of the Tikhonov parameter that can achieve a balance between reducing input loads while preserving desired accuracy of output vibration.

We discuss techniques for efficient local detection of silent data corruption in parallel scientific computations, leveraging physical quantities such as momentum and energy that may be conserved by discretized PDEs. The conserved quantities are analogous to “algorithm-based fault tolerance” checksums for linear algebra but, due to their physical foundation, are applicable to both linear and nonlinear equations and have efficient local updates based on fluxes between subdomains. These physics-based checksums enable precise intermittent detection of errors and recovery by rollback to a checkpoint, with very low overhead when errors are rare. We present applications to both explicit hyperbolic and iterative elliptic (unstructured finite-element) solvers with injected memory bit flips.

It has been predicted that several changes will occur when premixed flames are subjected to the extreme levels of turbulence that can be found in practical combustors. This paper is a review of recent experimental and DNS results that have been obtained for the range of extreme turbulence, and it includes a discussion of cases that agree or disagree with predictions. “Extreme turbulence” is defined to correspond to a turbulent Reynolds number (ReΤ, based on integral scale) that exceeds 2800 or a turbulent Karlovitz number that exceeds 100, for reasons that are discussed in Section 2.1. Several data bases are described that include measurements made at Lund University, the University of Sydney, the University of Michigan and the U.S. Air Force Research Lab. The data bases also include DNS results from Sandia National Laboratory, the University of New South Wales, Newcastle University, the California Institute of Technology and the University of Cambridge. Several major observations are: (a) DNS now can be achieved for a realistic geometry (of the Lund University jet burner) even for extreme turbulence levels, (b) state relations (conditional mean profiles) from DNS and experiments do tend to agree with laminar profiles, at least for methane-air and hydrogen-air reactants that are not preheated, and (c) regime boundaries have been measured and they do not agree with predicted boundaries. These findings indicate that the range of conditions for which flamelet models should be valid is larger than what was previously believed. Additional parameters have been shown to be important; for example, broken reactions occur if the “back-support” is insufficient due to the entrainment of cold gas into the product gas. Turbulent burning velocity measurements have been extended from the previous normalized turbulence levels (u’/SL) of 24 up to a value of 163. Turbulent burning velocities no longer follow the trend predicted by Shchelkin but they tend to follow the trend predicted by Damköhler. The boundary where flamelet broadening begins was measured to occur at ReTaylor = 13.8, which corresponds to an integral scale Reynolds number (ReT) of 2800. This measured regime boundary can be explained by the idea that flame structure is altered when the turbulent diffusivity at the Taylor scale exceeds a critical value, rather than the idea that changes occur when Kolmogorov eddies just fit inside a flamelet. A roadmap to extend DNS to complex chemistry and to higher Reynolds numbers is discussed. Exascale computers, machine learning, adaptive mesh refinement and embedded DNS show promise. Some advances are reviewed that have extended the use of line and planar PLIF and CARS laser diagnostics to studies that consider complex hydrocarbon fuels and harsh environments.

Renewed interest in the development of molten salt reactors has created the need for analytical tools that can perform safeguards assessments on these advanced reactors. This work outlines a flexible framework to perform safeguards analyses on a wide range of advanced reactor designs. The framework consists of two parts, a process model and a safeguards tool. The process model, developed in MATLAB Simulink, simulates the flow materials through a reactor facility. These models are linked to SCALE/TRITON and SCALE/ORIGEN to approximate depletion and decay of fuel salts but are flexible enough to accommodate higher fidelity tools if needed. The safeguards tool uses the process data to calculate common statistical quantities of interest such as material unaccounted for (MUF) and Page's trend test on the standardized independent transformed MUF (SITMUF). This paper documents the development of these tools.

Distribution and transmission protection systems are considered vital parts of modern smart grid ecosystems due to their ability to isolate faulted segments and preserve the operation of critical loads. Current protection schemes increasingly utilize cognitive methods to proactively modify their actions according to extreme power system changes. However, the effectiveness and robustness of these information-driven solutions rely entirely on the integrity, authenticity, and confidentiality of the data and control signals exchanged on the underlying relay communication networks. In this paper, we outline a scalable adaptive protection platform for distribution systems, and introduce a novel blockchain-based distributed network architecture to enhance data exchange security among the smart grid protection relays. The proposed mechanism utilizes a tiered blockchain architecture to counter the current technology limitations providing low latency with better scalability. The decentralized nature removes singular points of failure or contamination, enabling direct secure communication between smart grid relays. We also present a security analysis that demonstrates how the proposed framework prohibits any alterations on the blockchain ledger providing integrity and authenticity of the exchanged data (e.g., realtime measurements/relay settings). Finally, the performance of the proposed approach is evaluated through simulation on a blockchain benchmarking framework with the results demonstrating a promising solution for secure smart grid protection system communication.

Fe-Co-2V is a soft ferromagnetic alloy used in electromagnetic applications due to excellent magnetic properties. However, the discontinuous yielding (Luders bands), grain-size-dependent properties (Hall-Petch behavior), and the degree of order/disorder in the Fe-Co-2V alloy makes it difficult to predict the mechanical performance, particularly in abnormal environments such as elevated strain rates and high/low temperatures. Thus, experimental characterization of the high strain rate properties of the Fe-Co-2V alloy is desired, which are used for material model development in numerical simulations. In this study, the high rate tensile response of Fe-Co-2V is investigated with a pulse-shaped Kolsky tension bar over a wide range of strain rates and temperatures. Effects of temperature and strain rate on yield stress, ultimate stress, and ductility are discussed.

[Abstract] To support reduced order modeling of heat transfer for reentry bodies we develop an approximate solution method is identified that provides good estimates for the local wall derivative (and thereby the skin friction and Nusselt numbers) for a wide range of self-similar laminar formulations. These formulations include: Blasius flow, axisymmetric and planar stagnation flows and the Faulkner-Skan flows. The approach utilized is simply an extension of the classical Weyl formulation for the Blasius equation. Using this solution form estimates that naturally represent combined flow behaviors are represented without post-solution interpolation. An important example, namely axisymmetric stagnation equally combined with laminar zero pressure gradient (flat plate) flow, shows a difference of 10% between the pre-solution combination developed here and s simple post-solution arithmetic average. Clearly, the nonlinearity inherent to these solutions prevails in terms of these simple solutions. Compressible extensions to the basic incompressible result are achieved by including a near wall Chapman-Rubesin term making these solutions suitable for adiabatic wall problems. Direct comparison of the wall gradient estimation procedure developed here demonstrates excellent agreement with empirically fit blunt body heat transfer models such as the asymptotically consistent model of Kemp et. al. which are deemed more appropriate than the classical stagnation point scaling approaches.

In this work, we investigated microstructural features of elastomeric foam with the goal of identifying descriptors other than porosity that have a significant effect on the macroscale mechanical response. X-ray computed tomography (XCT) provided three-dimensional images of several flexible polyurethane foam samples prior to mechanical testing. The samples were then compressed to approximately 80% engineering strain. Stereo digital image correlation was used to measure the three-dimensional surface displacement data, from which strain was determined. The strain data, which were calculated with respect to the undeformed coordinates, were then overlaid on the corresponding surface generated from XCT. Heterogeneities in the strain-field were cross-correlated with topological quantities such as pore size distribution. A statistically significant correlation was identified between the distance transform of the pore phase and strain fluctuations.

Recent work has demonstrated that block preconditioning can scalably accelerate the performance of iterative solvers applied to linear systems arising in implicit multiphysics PDE simulations. The idea of block preconditioning is to decompose the system matrix into physical sub-blocks and apply individual specialized scalable solvers to each sub-block. It can be advantageous to block into simpler segregated physics systems or to block by discretization type. This strategy is particularly amenable to multiphysics systems in which existing solvers, such as multilevel methods, can be leveraged for component physics and to problems with disparate discretizations in which scalable monolithic solvers are rare. This work extends our recent work on scalable block preconditioning methods for structure-preserving discretizatons of the Maxwell equations and our previous work in MHD system solvers to the context of multifluid electromagnetic plasma systems. We argue how a block preconditioner can address both the disparate discretization, as well as strongly-coupled off-diagonal physics that produces fast time-scales (e.g. plasma and cyclotron frequencies). We propose a block preconditioner for plasma systems that allows reuse of existing multigrid solvers for different degrees of freedom while capturing important couplings, and demonstrate the algorithmic scalability of this approach at time-scales of interest.

This article describes a parallel implementation of a two-level overlapping Schwarz preconditioner with the GDSW (Generalized Dryja–Smith–Widlund) coarse space described in previous work [12, 10, 15] into the Trilinos framework; cf. [16]. The software is a significant improvement of a previous implementation [12]; see Sec. 4 for results on the improved performance.

We discuss techniques for efficient local detection of silent data corruption in parallel scientific computations, leveraging physical quantities such as momentum and energy that may be conserved by discretized PDEs. The conserved quantities are analogous to “algorithm-based fault tolerance” checksums for linear algebra but, due to their physical foundation, are applicable to both linear and nonlinear equations and have efficient local updates based on fluxes between subdomains. These physics-based checksums enable precise intermittent detection of errors and recovery by rollback to a checkpoint, with very low overhead when errors are rare. We present applications to both explicit hyperbolic and iterative elliptic (unstructured finite-element) solvers with injected memory bit flips.

Concern about memory errors has been widespread in high-performance computing (HPC) for decades. These concerns have led to significant research on detecting and correcting memory errors to improve performance and provide strong guarantees about the correctness of the memory contents of scientific simulations. However, power concerns and changes in memory architectures threaten the viability of current approaches to protecting memory (e.g., Chipkill). Returning to less protective error-correcting codes (ECC), e.g., single-error correction, double-error detection (SECDED), may increase the frequency of memory errors, including silent data corruption (SDC). SDC has the potential to silently cause applications to produce incorrect results and mislead domain scientists. We propose an approach for exploiting unnecessary bits in pointer values to support encoding the pointer with a Reed-Solomon code. Encoding the pointer allows us to provides strong capabilities for correcting and detecting corruption of pointer values. In this paper, we provide a detailed description of how we can exploit unnecessary pointer bits to store Reed-Solomon parity symbols. We evaluate the performance impacts of this approach and examine the effectiveness of the approach against corruption. Our results demonstrate that encoding and decoding is fast (less than 45 per event) and that the protection it provides is robust (the rate of miscorrection is less than 5% even for significant corruption). The data and analysis presented in this paper demonstrates the power of our approach. It is fast, tunable, requires no additional per-pointer storage resources, and provides robust protection against pointer corruption.

Productivity and Sustainability Improvement Planning (PSIP) is a lightweight, iterative workflow that allows software development teams to identify development bottlenecks and track progress to overcome them. In this paper, we present an overview of PSIP and how it compares to other software process improvement (SPI) methodologies, and provide two case studies that describe how the use of PSIP led to successful improvements in team effectiveness and efficiency.

The design, construction, and testing of a high-magnification, long working-distance plenoptic camera is reported. A plenoptic camera uses a microlens array to enable resolution of the spatial and angular information of the incoming light field. Instantaneous images can be numerically refocused and perspective shifted in post-processing to enable threedimensional (3D) resolution of a scene. Prior to this work, most applications of plenoptic imaging were limited to relatively low magnifications (1× or less) or small working distances. Here, a unique system is developed with enables 5× magnification at a working distance of over a quarter meter. Experimental results demonstrate ~25 µm spatial resolution with 3D imaging capabilities. This technology is demonstrated for 3D imaging of the shock structure in a underexpanded, Mach 3.3 free air jet.

Truly predictive numerical simulations can only be obtained by performing Uncertainty Quantification. However, many realistic engineering applications require extremely complex and computationally expensive high-fidelity numerical simulations for their accurate performance characterization. Very often the combination of complex physical models and extreme operative conditions can easily lead to hundreds of uncertain parameters that need to be propagated through high-fidelity codes. Under these circumstances, a single fidelity uncertainty quantification approach, i.e. a workflow that only uses high-fidelity simulations, is unfeasible due to its prohibitive overall computational cost. To overcome this difficulty, in recent years multifidelity strategies emerged and gained popularity. Their core idea is to combine simulations with varying levels of fidelity/accuracy in order to obtain estimators or surrogates that can yield the same accuracy of their single fidelity counterparts at a much lower computational cost. This goal is usually accomplished by defining a priori a sequence of discretization levels or physical modeling assumptions that can be used to decrease the complexity of a numerical model realization and thus its computational cost. Less attention has been dedicated to low-fidelity models that can be built directly from a small number of available high-fidelity simulations. In this work we focus our attention on reduced order models (ROMs). Our main goal in this work is to investigate the combination of multifidelity uncertainty quantification and ROMs in order to evaluate the possibility to obtain an efficient framework for propagating uncertainties through expensive numerical codes. We focus our attention on sampling-based multifidelity approaches, like the multifidelity control variate, and we consider several scenarios for a numerical test problem, namely the Kuramoto-Sivashinsky equation, for which the efficiency of the multifidelity-ROM estimator is compared to the standard (single-fidelity) Monte Carlo approach.

As computer architectures are rapidly evolving (e.g. those designed for exascale), multiple portability frameworks have been developed to avoid new architecture-specific development and tuning. However, portability frameworks depend on compilers for auto-vectorization and may lack support for explicit vectorization on heterogeneous platforms. Alternatively, programmers can use intrinsics-based primitives to achieve more efficient vectorization, but the lack of a gpu back-end for these primitives makes such code non-portable. A unified, portable, Single Instruction Multiple Data (simd) primitive proposed in this work, allows intrinsics-based vectorization on cpus and many-core architectures such as Intel Knights Landing (knl), and also facilitates Single Instruction Multiple Threads (simt) based execution on gpus. This unified primitive, coupled with the Kokkos portability ecosystem, makes it possible to develop explicitly vectorized code, which is portable across heterogeneous platforms. The new simd primitive is used on different architectures to test the performance boost against hard-to-auto-vectorize baseline, to measure the overhead against efficiently vectroized baseline, and to evaluate the new feature called the “logical vector length” (lvl). The simd primitive provides portability across cpus and gpus without any performance degradation being observed experimentally.

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Time series anomaly detection is an important task, with applications in a broad variety of domains. Many approaches have been proposed in recent years, but often they require that the length of the anomalies be known in advance and provided as an input parameter. This limits the practicality of the algorithms, as such information is often unknown in advance, or anomalies with different lengths might co-exist in the data. To address this limitation, previously, a linear time anomaly detection algorithm based on grammar induction has been proposed. While the algorithm can find variable-length patterns, it still requires preselecting values for at least two parameters at the discretization step. How to choose these parameter values properly is still an open problem. In this paper, we introduce a grammar-induction-based anomaly detection method utilizing ensemble learning. Instead of using a particular choice of parameter values for anomaly detection, the method generates the final result based on a set of results obtained using different parameter values. We demonstrate that the proposed ensemble approach can outperform existing grammar-induction-based approaches with different criteria for selection of parameter values. We also show that the proposed approach can achieve performance similar to that of the state-of-the-art distance-based anomaly detection algorithm.

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Streaked visible spectroscopy is well established in dynamic compression research. Infrared measurements remain problematic, however, due to the diminishing sensitivity of streak camera photocathodes beyond 800 nm. Time-stretch techniques offer an alternative method for probing infrared features during single-event experiments. This paper discusses the development of a time-stretch spectroscopy diagnostic using dispersed supercontinuum laser pulses. The technique is applied to near-infrared measurements of liquid water during multiple shock compression.

Most natural and man-made surfaces appear to be rough on many length scales. There is presently no unifying theory of the origin of roughness or the self-affine nature of surface topography. One likely contributor to the formation of roughness is deformation, which underlies many processes that shape surfaces such as machining, fracture, and wear. Using molecular dynamics, we simulate the biaxial compression of single-crystal Au, the high-entropy alloy Ni36.67Co30Fe16.67Ti16.67, and amorphous Cu50Zr50 and show that even surfaces of homogeneous materials develop a self-affine structure. By characterizing subsurface deformation, we connect the self-affinity of the surface to the spatial correlation of deformation events occurring within the bulk and present scaling relations for the evolution of roughness with strain. These results open routes toward interpreting and engineering roughness profiles.

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Accurate diagnosis of failures is critical for meeting photovoltaic (PV) performance objectives and avoiding safety concerns. This analysis focuses on the classification of field-collected string-level current-voltage (IV) curves representing baseline, partial soiling, and cracked failure modes. Specifically, multiple neural network-based architectures (including convolutional and long short-term memory) are evaluated using domain-informed parameters across different portions of the IV curve and a range of irradiance thresholds. The analysis identified two models that were able to accurately classify the relatively small dataset (400 samples) at a high accuracy (99%+). Findings also indicate optimal irradiance thresholds and opportunities for improvements in classification activities by focusing on portions of the IV curve. Such advancements are critical for expanding accurate classification of PV faults, especially for those with low power loss (e.g., cracked cells) or visibly similar IV curve profiles.

Risk assessment of nuclear power plants (NPPs) is commonly driven by computer modeling which tracks the evolution of NPP events over time. To capture interactions between nuclear safety and nuclear security, multiple system codes each of which specializes on one space may need to be linked with information transfer among the codes. A systems analysis based on fixed length time blocks is proposed to allow for such a linking within the ADAPT framework without needing to predetermine in which order the safety/security codes interact. A case study using two instances of the Scribe3D code demonstrates the concept and shows agreement with results from a direct solution.

Ducted fuel injection (DFI) is a technique to attenuate soot formation in compression ignition engines relative to conventional diesel combustion (CDC). The concept is to inject fuel through a small tube inside the combustion chamber to reduce equivalence ratios in the autoignition zone relative to CDC. DFI has been studied at loads as high as 8.5 bar gross indicated mean effective pressure (IMEPg) and as low as 2.5 bar IMEPg using a four-orifice fuel injector. Across previous studies, DFI has been shown to attenuate soot emissions, increase NOx emissions (at constant charge dilution), and slightly decrease fuel conversion efficiencies for most tested points. This study expands on the previous work by testing 1.1 bar IMEPg (low-load/idle) conditions and 10 bar IMEPg (higher-load) conditions with the same four-orifice fuel injector, as well as examining potential causes of the degradations in NOx emissions and fuel conversion efficiencies. DFI and CDC are directly compared at each operating point in the study. At the low-load condition, the intake charge dilution was swept to elucidate the soot and NOx performance of DFI. The low-load range is important because it is the target of impending, more-stringent emissions regulations, and DFI is shown to be a potentially effective approach for helping to meet these regulations. The results also indicate that DFI likely has slightly decreased fuel conversion efficiencies relative to CDC. The increase in NOx emissions with DFI is likely due to longer charge gas residence times at higher temperatures, which arise from shorter combustion durations and advanced combustion phasing relative to CDC.

We consider the problem of decomposing higher-order moment tensors, i.e., the sum of symmetric outer products of data vectors. Such a decomposition can be used to estimate the means in a Gaussian mixture model and for other applications in machine learning. The dth-order empirical moment tensor of a set of p observations of n variables is a symmetric d-way tensor. Our goal is to find a low-rank tensor approximation comprising r < p symmetric outer products. The challenge is that forming the empirical moment tensors costs O(pnd) operations and O(nd) storage, which may be prohibitively expensive; additionally, the algorithm to compute the low-rank approximation costs O(nd) per iteration. Our contribution is avoiding formation of the moment tensor, computing the low-rank tensor approximation of the moment tensor implicitly using O(pnr) operations per iteration and no extra memory. This advance opens the door to more applications of higher-order moments since they can now be efficiently computed. We present numerical evidence of the computational savings and show an example of estimating the means for higher-order moments.

We present a Fourier analysis of wave propagation problems subject to a class of continuous and discontinuous discretizations using high-degree Lagrange polynomials. This allows us to obtain explicit analytical formulas for the dispersion relation and group velocity and, for the first time to our knowledge, characterize analytically the emergence of gaps in the dispersion relation at specific wavenumbers, when they exist, and compute their specific locations. Wave packets with energy at these wavenumbers will fail to propagate correctly, leading to significant numerical dispersion. We also show that the Fourier analysis generates mathematical artifacts, and we explain how to remove them through a branch selection procedure conducted by analysis of eigenvectors and associated reconstructed solutions. The higher frequency eigenmodes, named erratic in this study, are also investigated analytically and numerically.

An optically-segmented single-volume scatter camera is being developed to image MeV-energy neutron sources. The design employs long, thin, optically isolated organic scintillator pillars with 5 mm × 5 mm × 200 mm dimensions (i.e., an aspect-ratio of 1:1:40). Teflon reflector is used to achieve optical isolation and improve light collection. The effect of Teflon on the ability to resolve the radiation interaction locations along such high aspect-ratio pillars is investigated. It was found that reconstruction based on the amplitude of signals collected on both ends of a bare pillar is less precise than reconstruction based on their arrival times. However, this observation is reversed after wrapping in Teflon, such that there is little to no improvement in reconstruction resolution calculated by combining both methods. It may be possible to use another means of optical isolation that does not require wrapping each individual pillar of the camera.

We present a general machine learning algorithm for boundary detection within general signals based on an efficient, accurate, and robust approximation of the universal normalized information distance. Our approach uses an adaptive sliding information distance (SLID) combined with a wavelet-based approach for peak identification to locate the boundaries. Special emphasis is placed on developing an adaptive formulation of SLID to handle general signals with multiple unknown and/or drifting section lengths. Although specialized algorithms may outperform SLID when domain knowledge is available, these algorithms are limited to specific applications and do not generalize. SLID excels in these cases. We demonstrate the versatility and efficacy of SLID on a variety of signal types, including synthetically generated sequences of tokens, binary executables for reverse engineering applications, and time series of seismic events.

As increasingly impressive quantum information processors are realized in laboratories around the world, robust and reliable characterization of these devices is now more urgent than ever. These diagnostics can take many forms, but one of the most popular categories is tomography, where an underlying parameterized model is proposed for a device and inferred by experiments. Here, we introduce and implement efficient operational tomography, which uses experimental observables as these model parameters. This addresses a problem of ambiguity in representation that arises in current tomographic approaches (the gauge problem). Solving the gauge problem enables us to efficiently implement operational tomography in a Bayesian framework computationally, and hence gives us a natural way to include prior information and discuss uncertainty in fit parameters. We demonstrate this new tomography in a variety of different experimentally-relevant scenarios, including standard process tomography, Ramsey interferometry, randomized benchmarking, and gate set tomography.

Recent advances in horizontal cask designs for commercial spent nuclear fuel have significantly increased maximum thermal loading. This is due in part to greater efficiency in internal conduction pathways. Carefully measured data sets generated from testing of full-sized casks or smaller cask analogs are widely recognized as vital for validating thermal-hydraulic models of these storage cask designs. While several testing programs have been previously conducted, these earlier validation studies did not integrate all the physics or components important in a modern, horizontal dry cask system. The purpose of this investigation is to produce data sets that can be used to benchmark the codes and best practices presently used to calculate cladding temperatures and induced cooling air flows in modern, horizontal dry storage systems. The horizontal dry cask simulator (HDCS) has been designed to generate this benchmark data and complement the existing knowledge base. Transverse and axial temperature profiles along with induced-cooling air flow are measured using various backfills of gases for a wide range of decay powers and canister pressures. The data from the HDCS tests will be used to host a blind model validation effort.

We propose a very large family of benchmarks for probing the performance of quantum computers. We call them volumetric benchmarks (VBs) because they generalize IBM's benchmark for measuring quantum volume [1]. The quantum volume benchmark defines a family of square circuits whose depth d and width w are the same. A volumetric benchmark defines a family of rectangular quantum circuits, for which d and w are uncoupled to allow the study of time/space performance trade-offs. Each VB defines a mapping from circuit shapes - (w, d) pairs - to test suites C(w, d). A test suite is an ensemble of test circuits that share a common structure. The test suite C for a given circuit shape may be a single circuit C, a specific list of circuits {C1... CN} that must all be run, or a large set of possible circuits equipped with a distribution Pr(C). The circuits in a given VB share a structure, which is limited only by designers' creativity. We list some known benchmarks, and other circuit families, that fit into the VB framework: several families of random circuits, periodic circuits, and algorithm-inspired circuits. The last ingredient defining a benchmark is a success criterion that defines when a processor is judged to have “passed” a given test circuit. We discuss several options. Benchmark data can be analyzed in many ways to extract many properties, but we propose a simple, universal graphical summary of results that illustrates the Pareto frontier of the d vs w trade-off for the processor being benchmarked.

Estimation of the uncertainty in a critical experiment attributable to uncertainties in the measured experiment temperature is done by calculating the variation of the eigenvalue of a benchmark configuration as a function of temperature. In the low-enriched water-moderated critical experiments performed at Sandia, this is done by 1) estimating the effects of changing the water temperature while holding the UO₂ fuel temperature constant, 2) estimating the effects of changing the UO₂ temperature while holding the water temperature constant, and 3) combining the two results. This assumes that the two effects are separable. The results of such an analysis are nonintuitive and need experimental verification. Critical experiments are being planned at Sandia National Laboratories (Sandia) to measure the effect of temperature on critical systems and will serve to test the methods used in estimating the temperature effects in critical experiments.

Abstract not provided.

Aerosol jet printing offers a versatile, high-resolution prototyping capability for flexible and hybrid electronics. Despite its rapid growth in recent years, persistent problems such as process drift hinder the adoption of this technology in production environments. Here we explore underlying causes of process drift during aerosol jet printing and introduce an engineered solution to improve deposition stability. It is shown that the ink level within the cartridge is a critical factor in determining atomization efficiency, such that the reduction in ink volume resulting from printing itself can induce significant and systematic process drift. By integrating a custom 3D-printed cartridge with an ink recirculation system, ink composition and level within the cartridge are better maintained. This strategy allows extended duration printing with improved stability, as evidenced by 30 h of printing over 5 production runs. This provides an important tool for extending the duration and improving reliability for aerosol jet printing, a key factor for integration in practical manufacturing operations.

Following the ASME codes, the design of pipelines and pressure vessels for transportation or storage of high-pressure hydrogen gas requires measurements of fatigue crack growth rates at design pressure. However, performing tests in high pressure hydrogen gas can be very costly as only a few laboratories have the unique capabilities. Recently, Code Case 2938 was accepted in ASME Boiler and Pressure Vessel Code (BPVC) VIII-3 allowing for design curves to be used in lieu of performing fatigue crack growth rate (da/dN vs. ?K) and fracture threshold (KIH) testing in hydrogen gas. The design curves were based on data generated at 100 MPa H2 on SA-372 and SA-723 grade steels; however, the data used to generate the design curves are limited to measurements of ?K values greater than 6 MPa m1/2. The design curves can be extrapolated to lower ?K (<6 MPa m1/2), but the threshold stress intensity factor (?Kth) has not been measured in hydrogen gas. In this work, decreasing ?K tests were performed at select hydrogen pressures to explore threshold (?Kth) for ferritic-based structural steels (e.g. pipelines and pressure vessels). The results were compared to decreasing ?K tests in air, showing that the fatigue crack growth rates in hydrogen gas appear to yield similar or even slightly lower da/dN values compared to the curves in air at low ?K values when tests were performed at stress ratios of 0.5 and 0.7. Correction for crack closure was implemented, which resulted in better agreement with the design curves and provide an upper bound throughout the entire ?K range, even as the crack growth rates approach ?Kth. This work gives further evidence of the utility of the design curves described in Code Case 2938 of the ASME BPVC VIII-3 for construction of high pressure hydrogen vessels.

The light output, time resolution, pulse shape discrimination (PSD), neutron light output, and interaction position reconstruction of melt-cast small-molecule organic glass bar scintillators were measured. The trans-stilbene organic scintillator detects fast neutrons and gamma rays with high efficiency and exhibits excellent PSD, but the manufacturing process is slow and expensive and its light output in response to neutrons is anisotropic. Small-molecule organic glass bars offer an easy-to-implement and cost-effective solution to these problems. These properties were characterized to evaluate the efficacy of constructing a compact, low-voltage neutron and gamma-ray imaging system using organic glass bars coupled to silicon photomultiplier arrays. A complete facility for melt-casting organic glass scintillators was setup at the University of Michigan. 6×6×50 mm3 glass bars were produced and the properties listed above were characterized. The first neutron image using organic glass was produced in simple backprojection.

Validation of the extent of water removal in a dry storage system using an industrial vacuum drying procedure is needed. Water remaining in casks upon completion of vacuum drying can lead to cladding corrosion, embrittlement, and breaching, as well as fuel degradation. In order to address the lack of time-dependent industrial drying data, this study employs a vacuum drying procedure to evaluate the efficiency of water removal over time in a scaled system. Isothermal conditions are imposed to generate baseline pressure and moisture data for comparison to future tests under heated conditions. A pressure vessel was constructed to allow for the emplacement of controlled quantities of water and connections to a pumping system and instrumentation. Measurements of pressure and moisture content were obtained over time during sequential vacuum hold points, where the vacuum flow rate was throttled to draw pressures from 100 torr down to 0.7 torr. The pressure rebound, dew point, and water content were observed to eventually diminish with increasingly lower hold points, indicating a reduction in retained water.

Laser vibrometry has become a mature technology for structural dynamics testing, enabling many measurements to be obtained in a short amount of time without mass-loading the part. Recently multi-point laser vibrometers consisting of 48 or more measurement channels have been introduced to overcome some of the limitations of scanning systems, namely the inability to measure multiple data points simultaneously. However, measuring or estimating the alignment (Euler angles) of many laser beams for a given test setup remains tedious and can require a significant amount of time to complete and adds an unquantified source of uncertainty to the measurement. This paper introduces an alignment technique for the multipoint vibrometer system that utilizes photogrammetry to triangulate laser spots from which the Euler angles of each laser head relative to the test coordinate system can be determined. The generated laser beam vectors can be used to automatically create a test geometry and channel table. While the approach described was performed manually for proof of concept, it could be automated using the scripting tools within the vibrometer system.

Estimation of radionuclide aerosol release to the environment, from fire accident scenarios, are one of the most dominant accident evaluations at the U.S. Department of Energy's (DOE's) nuclear facilities. Of particular interest to safety analysts, is estimating the radionuclide aerosol release, the Source Term (ST), based on aerosol transport from a fire room to a corridor and from the corridor to the environment. However, no existing literature has been found on estimating ST from this multi-room facility configuration. This paper contributes the following to aerosol transport modeling body of work: a validation study on a multiroom fire experiment (this includes a code-to-code comparison between MELCOR and Consolidated Fire and Smoke Transport, a specialized fire code without radionuclide transport capabilities), a sensitivity study to provide insight on the effect of smoke on ST, and a sensitivity study on the effect of aerosol entrainment in the atmosphere (puff and continuous rate) on ST.

The Sodium Chemistry (NAC) package in MELCOR has been developed to enhance application to sodium cooled fast reactors. The models in the NAC package have been assessed through benchmark analyses. The F7-1 pool fire experimental analysis is conducted within the framework of the U.S.-Japan collaboration; Civil Nuclear Energy Research and Development Working Group. This study assesses the capability of the pool fire model in MELCOR and provides recommendations for future model improvements because the physics of sodium pool fire are complex. Based on the preliminary results, analytical conditions, such as heat transfer on the floor catch pan are modified. The current MELCOR analysis yields lower values than the experimental data in pool combustion rate and pool, catch pan, and gas temperature during early time. The current treatment of heat transfer for the catch pan is the primary cause of the difference in the results from the experimental data. After sodium discharge stopping, the pool combustion rate and temperature become higher than experimental data. This is caused by absence of a model for pool fire suppression due to the oxide layer buildup on the pool surface. Based on these results, recommendations for future works are needed, such as heat transfer modification in terms of the catch pan and consideration of the effects of the oxide layer for both the MELCOR input model and pool physic.

To increase understanding of damage associated with underground explosions, a field test program was developed jointly by Sandia and Pacific Northwest National Laboratories at the EMRTC test range in Socorro, NM. The Blue Canyon Dome test site is underlain by a rhyolite that is fractured in places. The test system included deployment of a defined array of 64 probes in eight monitoring boreholes. The monitoring boreholes radially surround a central near vertical shot hole at horizontal distances of 4.6m and 7.6m in cardinal and 45 degrees offset to cardinal directions, respectively. The probes are potted in coarse sand which touches/accesses the rhyolite and are individually accessed via nylon tubing and isolated from each other by epoxy and grout sequences. Pre and post chemical explosion air flow rate measurements, conducted for ~30-45 minutes from each probe, were observed for potential change. The gas flow measurement is a function of the rock mass permeability near a probe. Much of the flow rate change is at depth station 8 (59.4m) and is in the SE quadrant. Flow rate changes are inferred to be caused by the chemical explosion which may have opened pre-existing fractures, fractured the rock and/or caused block displacements by rotations and translations. The air flow rate data acquired here may enable a relationship and/or calibration to rock damage to be developed.

We develop a methodology for comparing two or more agent-based models that are developed for the same domain, but may differ in the particular data sets (e.g., geographical regions) to which they are applied, and in the structure of the model. Our approach is to learn a response surface in the common parameter space of the models and compare the regions corresponding to qualitatively different behaviors in the models. As an example, we develop an active learning algorithm to learn phase transition boundaries in contagion processes in order to compare two agent-based models of rooftop solar panel adoption.

PIC MCC simulation results on the breakdown in the pulse discharge in helium at pressure of 100 Torr and voltage of U=3.25 kV are presented. The delay of the breakdown development is studied with different initial densities of plasma and excited helium atoms, which corresponds to various discharge operation frequencies. It is shown that for high concentration of excited atoms the photoemission determines the breakdown delay time. In opposite case of low excited atoms density, the ion-electron emission plays a key role in the breakdown development. The photoemission from the cathode is set with a flux of the photons with Doppler shift over the frequency. These photons are generated in reactions between exited atoms and fast atoms. A wide distribution of breakdown delay time was observed in different runs and analyzed.

In many online social networking platforms, the participation of an individual is motivated by the participation of others. If an individual chooses to leave a platform, this may produce a cascade in which that person’s friends then choose to leave, causing their friends to leave, and so on. In some cases, it may be possible to incentivize key individuals to stay active within the network, thus preventing such a cascade. This problem is modeled using the anchored k-core of a network, which, for a network G and set of anchor nodes A, is the maximal subgraph of G in which every node has a total of at least k neighbors between the subgraph and anchors. In this work, we propose Residual Core Maximization (RCM), a novel algorithm for finding b anchor nodes so that the size of the anchored k-core is maximized. We perform a comprehensive experimental evaluation on numerous real-world networks and compare RCM to various baselines. We observe that RCM is more effective and efficient than the state-of-the-art methods: on average, RCM produces anchored k-cores that are 1.65 times larger than those produced by the baseline algorithm, and is approximately 500 times faster on average.

Light carries a great deal of information in the form of amplitude, phase, and polarization, any or most powerfully all, of which may be exploited for the characterization of materials or development of novel technologies. However, extracting the full set of information carried by light becomes increasingly difficult as sample feature sizes shrink and the footprint and cost of detection schemes must decrease as well. Here, a fiber-based interferometric scheme is deployed to extract this information from optical systems which may be assessed three dimensionally down to the nanoscale and/or temporally up to the bandwidth of electronic data acquisition available. The setup utilizes a homemade fiber stretcher to achieve phase-locking of the reference arm and is compatible with heterodyning. More interestingly, a simplified and less expensive approach is demonstrated which employs the fiber stretcher for arbitrarily frequency up-converted (with respect to driving voltage frequency) phase modulation in addition to locking. This improves the detection system's size, weight, power, and cost requirements, eliminating the need for an acousto-optic modulator and reducing the drive power required by orders of magnitude. High performance is maintained as evidenced by imaging amplitude and phase (and inherently polarization state) in micro and nano optical systems such as lensed fibers and focusing waveguide grating couplers previously imaged only for intensity distribution.

Tensor decomposition is a well-known tool for multiway data analysis. This work proposes using stochastic gradients for efficient generalized canonical polyadic (GCP) tensor decomposition of large-scale tensors. GCP tensor decomposition is a recently proposed version of tensor decomposition that allows for a variety of loss functions such as Bernoulli loss for binary data or Huber loss for robust estimation. The stochastic gradient is formed from randomly sampled elements of the tensor and is efficient because it can be computed using the sparse matricized-tensor times Khatri-Rao product tensor kernel. For dense tensors, we simply use uniform sampling. For sparse tensors, we propose two types of stratified sampling that give precedence to sampling nonzeros. Numerical results demonstrate the advantages of the proposed approach and its scalability to large-scale problems.

Integration of renewable power sources into grids remains an active research and development area,particularly for less developed renewable energy technologies such as wave energy converters (WECs).WECs are projected to have strong early market penetration for remote communities, which serve as naturalmicrogrids. Hence, accurate wave predictions to manage the interactions of a WEC array with microgridsis especially important. Recently developed, low-cost wave measurement buoys allow for operationalassimilation of wave data at remote locations where real-time data have previously been unavailable. This work includes the development and assessment of a wave modeling framework with real-time dataassimilation capabilities for WEC power prediction. The availability of real-time wave spectral componentsfrom low-cost wave measurement buoys allows for operational data assimilation with the Ensemble Kalmanfilter technique, whereby measured wave conditions within the numerical wave forecast model domain areassimilated onto the combined set of internal and boundary grid points while taking into account model andobservation error covariances. The updated model state and boundary conditions allow for more accuratewave characteristic predictions at the locations of interest. Initial deployment data indicated that measured wave data from one buoy that were assimilated intothe wave modeling framework resulted in improved forecast skill for a case where a traditional numericalforecast model (e.g., Simulating WAves Nearshore; SWAN) did not well represent the measured conditions.On average, the wave power forecast error was reduced from 73% to 43% using the data assimilationmodeling with real-time wave observations.

Room ceilings and walls at the Waste Isolation Pilot Plant tend to collapse over time, causing rubble piles on floors of empty rooms. The surrounding rock formation will gradually compact these rubble piles until they eventually become solid salt, but the length of time for a rubble pile to reach a certain porosity and permeability is unknown. This paper details the initial model development to predict the porosity and fluid flow network of a closing empty room. Conventional geomechanical numerical methods would struggle to model empty room collapse and rubble pile consolidation, so three different meshless methods, the Immersed Isogeometric Analysis (IGA) Meshfree Method, Reproducing Kernel Particle Method (RKPM), and Conformal Reproducing Kernel (CRK) method, were assessed. First, each meshless method simulated gradual room closure, without ceiling or wall collapse. All methods produced equivalent predictions to a finite element method reference solution, with comparable computational speed. Second, the Immersed IGA Meshfree method and RKPM simulated two-dimensional empty room collapse and rubble pile consolidation. Both methods successfully simulated large viscoplastic deformations, fracture, and rubble pile rearrangement to produce qualitatively realistic results. Finally, the meshless simulation results helped identify a mechanism for empty room closure that had been previously overlooked.

Ray-tracing and heat-transfer simulations of discrete particles in a representative elementary volume were performed to determine the effective particle-cloud absorptance and temperature profiles as a function of intrinsic particle absorptance values (0 - 1) for dilute solids volume fractions (1 - 3%) representative of falling particle receivers used in concentrating solar power applications. Results showed that the average particle-cloud absorptance is increased above intrinsic particle absorptance values as a result of reflections and subsequent reabsorption (light trapping). The relative increase in effective particle-cloud absorptance was greater for lower values of intrinsic particle absorptance and could be as high as a factor of two. Higher values of intrinsic particle absorptance led to higher simulated steady-state particle temperatures. Significant temperature gradients within the particle cloud and within the particles themselves were also observed in the simulations. Findings indicate that dilute particle-cloud configurations within falling particle receivers can significantly enhance the apparent effective absorptance of the particle curtain, and materials with higher values of intrinsic particle absorptance will yield greater radiative absorptance and temperatures.

The interface between the steel casing and cemented annulus of a typical wellbore may de-bond and become permeable; this flow path is commonly referred to as a microannulus. Because there are often multiple fluids associated with wellbores, understanding two-phase flow behavior in the microannulus is important when evaluating the risks and hazards associated with leaky wellbores. A microannulus was created in a mock wellbore specimen by thermal debonding, which is one of the possible mechanisms for microannulus creation in the field. The specimen was saturated with silicone oil, and the intrinsic permeability through the microannulus was measured. Nitrogen was then injected at progressively increasing pressures, first to find the breakthrough pressure, and secondly, to obtain the relation between capillary pressure and gas relative permeability. The nitrogen was injected through the bottom of the specimen, to simulate the field condition where the gas migrates upwards along the casing. The measured data was successfully fit to common functional forms, such as the models of Brooks-Corey and Van Genuchten, which relate capillary pressure, saturation, and relative permeability of the two phases. The results can be used in computational models of flow along a wellbore microannulus.

Realizing cost-effective, dispatchable, renewable energy production using concentrated solar power (CSP) relies on reaching high process temperatures to increase the thermal-to-electrical efficiency. Ceramic based particles used as both the energy storage medium and heat transfer fluid is a promising approach to increasing the operating temperature of next generation CSP plants. The particle-to-supercritical CO2 (sCO2) heat exchanger is a critical component in the development of this technology for transferring thermal energy from the heated ceramic particles to the sCO2 working fluid of the power cycle. The leading design for the particle-to-sCO2 heat exchanger is a shell-and-plate configuration. Currently, design work is focused on optimizing the performance of the heat exchanger through reducing the plate spacing. However, the particle channel geometry is limited by uniformity and reliability of particle flow in narrow vertical channels. Results of high temperature experimental particle flow testing are presented in this paper.

Distribution system modelling and analysis with growing penetration of distributed energy resources (DERs) requires more detailed and accurate distribution load modelling. Smart meters, DER monitors, and other distribution system sensors provide a new level of visibility to distribution system loads and DERs. However, there is a limited understanding of how to efficiently leverage the new information in distribution system load modelling. This study presents the assessment of 11 methods to leverage the emerging information for improved distribution system active and reactive power load modelling. The accuracy of these load modelling methods is assessed both at the primary and the secondary distribution levels by analysing over 2.7 billion data points of results of feeder node voltages and element phase currents obtained by performing annual quasi-static time series simulations on EPRI's Ckt5 feeder model.

There are several factors that should be considered for robust terrain classification. We address the issue of high pixel-wise variability within terrain classes from remote sensing modalities, when the spatial resolution is less than one meter. Our proposed method segments an image into superpixels, makes terrain classification decisions on the pixels within each superpixel using the probabilistic feature fusion (PFF) classifier, then makes a superpixel-level terrain classification decision by the majority vote of the pixels within the superpixel. We show that this method leads to improved terrain classification decisions. We demonstrate our method on optical, hyperspectral, and polarimetric synthetic aperture radar data.

Permeability prediction of porous media system is very important in many engineering and science domains including earth materials, bio-, solid-materials, and energy applications. In this work we evaluated how machine learning can be used to predict the permeability of porous media with physical properties. An emerging challenge for machine learning/deep learning in engineering and scientific research is the ability to incorporate physics into machine learning process. We used convolutional neural networks (CNNs) to train a set of image data of bead packing and additional physical properties such as porosity and surface area of porous media are used as training data either by feeding them to the fully connected network directly or through the multilayer perception network. Our results clearly show that the optimal neural network architecture and implementation of physics-informed constraints are important to properly improve the model prediction of permeability. A comprehensive analysis of hyperparameters with different CNN architectures and the data implementation scheme of the physical properties need to be performed to optimize our learning system for various porous media system.

Data fields sampled on irregularly spaced points arise in many science and engineering applications. For regular grids, Convolutional Neural Networks (CNNs) gain benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds using Generalized Moving Least Squares (GMLS). GMLS is a nonparametric meshfree technique for estimating linear bounded functionals from scattered data, and has emerged as an effective technique for solving partial differential equations (PDEs). By parameterizing the GMLS estimator, we obtain learning methods for linear and non-linear operators with unstructured stencils. The requisite calculations are local, embarrassingly parallelizable, and supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform operator regression, develop predictive dynamical models, and obtain feature extractors for engineering quantities of interest. The results show the promise of these architectures as foundations for data-driven model development in scientific machine learning applications.

Interpretation of field data from shock tests and subsequent assessment of product safety margins via laboratory testing are based on the shock response spectra (SRS). The SRS capture how a single degree of freedom (SDOF) structure responds to the shock at differing frequencies and, therefore, no longer contain the duration or other temporal parameters pertaining to the shock. A single duration can often be included in the technical specification or in the recreation of acceleration vs. time history from the specified SRS; however, there is little basis for that beyond technical judgment. The loss of such temporal information can result in the recreated SRS being the same while its effect on a system or component can be different. This paper attempts to quantify this deficiency as well as propose a simple method of capturing damping from shock waves that can allow the original waveform to be more accurately reconstructed from the SRS. In this study the decay rate associated with various frequencies that comprise the overall shock was varied. This variation in the decay rate leads to a variation in the acceleration vs. time history, which can be correlated to a “Damage Index” that captures the fatigue damage imparted to the object under shock. Several waveforms that have the same SRS but varying rates of decay for either high- or low-frequency components of the shock were investigated. The resulting variation in stress cycles and Damage Index is discussed in the context of the lognormal distribution of fatigue failure data. It is proposed that, along with the SRS, the decay rate is also captured to minimize the discrepancy between field data and representative laboratory tests.

An InGaAs/GaAsSb Type-II superlattice is explored as an absorber material for extended short-wave infrared detection. A 10.5 nm period was grown with an InGaAs/GaAsSb thickness ratio of 2 with a target In composition of 46% and target Sb composition of 62%. Cutoff wavelengths near 2.8 μm were achieved with responsivity beyond 3 μm. Demonstrated dark current densities were as low as 1.4 mA/cm2 at 295K and 13 μA/cm2 at 235K at -1V bias. A significant barrier to hole extraction was identified in the detector design that severely limited the external quantum efficiency (EQE) of the detectors. A redesign of the detector that removes that barrier could make InGaAs/GaAsSb very competitive with current commercial HgCdTe and extended InGaAs technology.

The goal of this paper is to present, for the first time, calculations of the magnetic penetration case of a first principles multipole-based cable braid electromagnetic penetration model. As a first test case, a one-dimensional array of perfect electrically conducting wires, for which an analytical solution is known, is investigated: We compare both the self-inductance and the transfer inductance results from our first principles cable braid electromagnetic penetration model to those obtained using the analytical solution. These results are found in good agreement up to a radius to half spacing ratio of about 0.78, demonstrating a robustness needed for many commercial and non-commercial cables. We then analyze a second set of test cases of a square array of wires whose solution is the same as the one-dimensional array result and of a rhomboidal array whose solution can be estimated from Kley’s model. As a final test case, we consider two layers of one-dimensional arrays of wires to investigate porpoising effects analytically. We find good agreement with analytical and Kley’s results for these geometries, verifying our proposed multipole model. Note that only our multipole model accounts for the full dependence on the actual cable geometry which enables us to model more complicated cable geometries.

High-speed aerospace engineering applications rely heavily on computational fluid dynamics (CFD) models for design and analysis due to the expense and difficulty of flight tests and experiments. This reliance on CFD models necessitates performing accurate and reliable uncertainty quantification (UQ) of the CFD models. However, it is very computationally expensive to run CFD for hypersonic flows due to the fine grid resolution required to capture the strong shocks and large gradients that are typically present. Additionally, UQ approaches are “many-query” problems requiring many runs with a wide range of input parameters. One way to enable computationally expensive models to be used in such many-query problems is to employ projection-based reduced-order models (ROMs) in lieu of the (high-fidelity) full-order model. In particular, the least-squares Petrov–Galerkin (LSPG) ROM (equipped with hyper-reduction) has demonstrated the ability to significantly reduce simulation costs while retaining high levels of accuracy on a range of problems including subsonic CFD applications [1, 2]. This allows computationally inexpensive LSPG ROM simulations to replace the full-order model simulations in UQ studies, which makes this many-query task tractable, even for large-scale CFD models. This work presents the first application of LSPG to a hypersonic CFD application. In particular, we present results for LSPG ROMs of the HIFiRE-1 in a three-dimensional, turbulent Mach 7.1 flow, showcasing the ability of the ROM to significantly reduce computational costs while maintaining high levels of accuracy in computed quantities of interest.

The performance of the Reactor Core Isolation Cooling (RCIC) system under beyond design basis event (BDBE) conditions is not well-characterized. The operating band of the RCIC system is currently specified utilizing conservative assumptions, with restrictive operational guidelines not allowing for an adequate credit of the true capability of the system. For example, it is assumed that battery power is needed for RCIC operation to maintain the reactor pressure vessel (RPV) water level—a loss of battery power is conservatively assumed to result in failure of the RCIC turbopump system in a range of safety and risk assessments. However, the accidents at Fukushima Daiichi Nuclear Power Station (FDNPS) showed that the Unit 2 RCIC did not cease to operate following loss of battery power. In fact, it continued to inject water into the RPV for nearly 3 days following the earthquake. Improved understanding of Terry turbopump operations under BDBE conditions can support enhancement of accident management procedures and guidelines, promoting more robust severe accident prevention. Therefore, the U.S. Department of Energy (DOE), U.S. nuclear industry, and international stakeholders have funded the Terry Turbine Expanded Operating Band (TTEXOB) program. This program aims to better understand RCIC operations during BDBE conditions through combined experimental and modeling efforts. As part of the TTEXOB, airflow testing was performed at Texas A&M University (TAMU) of a small-scale ZS-1 and a full-scale GS-2 Terry turbine. This paper presents the corresponding efforts to model operation of the TAMU ZS-1 and GS-2 Terry turbines with Sandia National Laboratories’ (SNL) MELCOR code. The current MELCOR modeling approach represents the Terry turbine with a system of equations expressing the conservation of angular momentum. The joint analysis and experimental program identified that a) it is possible for the Terry turbine to develop the same power at different speeds, and b) turbine losses appear to be insensitive to the size of the turbine. As part of this program, further study of Terry turbine modeling unknowns and uncertainties is planned to support more extensive application of modeling and simulation to the enhancement of plant-specific operational and accident procedures.

The light output, time resolution, pulse shape discrimination (PSD), neutron light output, and interaction position reconstruction of melt-cast small-molecule organic glass bar scintillators were measured. The trans-stilbene organic scintillator detects fast neutrons and gamma rays with high efficiency and exhibits excellent PSD, but the manufacturing process is slow and expensive and its light output in response to neutrons is anisotropic. Small-molecule organic glass bars offer an easy-to-implement and cost-effective solution to these problems. These properties were characterized to evaluate the efficacy of constructing a compact, low-voltage neutron and gamma-ray imaging system using organic glass bars coupled to silicon photomultiplier arrays. A complete facility for melt-casting organic glass scintillators was setup at the University of Michigan. 6×6×50 mm³ glass bars were produced and the properties listed above were characterized. The first neutron image using organic glass was produced in simple backprojection.

Fire suppression systems for transuranic (TRU) waste facilities are designed to minimize radioactive material release to the public and to facility employees in the event of a fire. Currently, facilities with Department of Transportation (DOT) 7A drums filled with TRU waste follow guidelines that assume a fraction of the drums experience lid ejection in case of a fire. This lid loss is assumed to result in significant TRU waste material from the drum experiencing an unconfined burn during the fire, and fire suppression systems are thus designed to respond and mitigate potential radioactive material release. However, recent preliminary tests where the standard lid filters of 7A drums were replaced with a UT-9424S filter suggest that the drums could retain their lid if equipped with this filter. The retention of the drum lid could thus result in a very different airborne release fraction (ARF) of a 7A drum's contents when exposed to a pool fire than what is assumed in current safety basis documents. This potentially different ARF is currently unknown because, while studies have been performed in the past to quantify ARF for 7A drums in a fire, no comprehensive measurements have been performed for drums equipped with a UT-9424S filter. If the ARF is lower than what is currently assumed, it could change the way TRU waste facilities operate. Sandia National Laboratories has thus developed a set of tests and techniques to help determine an ARF value for 7A drums filled with TRU waste and equipped with a UT-9424S filter when exposed to the hypothetical accident conditions (HAC) of a 30-minute hydrocarbon pool fire. In this multi-phase test series, SNL has accomplished the following: (1) performed a thermogravimetric analysis (TGA) on various combustible materials typically found in 7A drums in order to identify a conservative load for 7A drums in a pool fire; (2) performed a 30-minute pool fire test to (a) determine if lid ejection is possible under extreme conditions despite the UT-9424S filter, and (b) to measure key parameters in order to replicate the fire environment using a radiant heat setup; and (3) designed a radiant heat setup to demonstrate capability of reproducing the fire environment with a system that would facilitate measurements of ARF. This manuscript thus discusses the techniques, approach, and unique capabilities SNL has developed to help determine an ARF value for DOT 7A drums exposed to a 30-minute fully engulfing pool fire while equipped with a UT-9424S filter on the drum lid.

Empirically-based correlations are commonly used in modeling and simulation but rarely have rigorous uncertainty quantification that captures the nature of the underlying data. In many applications, a mathematical description for a parameter response to some input stimulus is often either unknown, unable to be measured, or both. Likewise, the data used to observe a parameter response is often noisy, and correlations are derived to approximate the bulk response. Practitioners frequently treat the chosen correlation-sometimes referred to as the "surrogate"or "reduced-order"model of the response-as a constant mathematical description of the relationship between input and output. This assumption, as with any model, is incorrect to some degree, and the uncertainty in the correlation can potentially have significant impacts on system responses. Thus, proper treatment of correlation uncertainty is necessary. In this paper, a method is proposed for high-level abstract sampling of uncertain data correlations. Whereas uncertainty characterization is often assigned to scalar values for direct sampling, functional uncertainty is not always straightforward. A systematic approach for sampling univariable uncertain correlations was developed to perform more rigorous uncertainty analyses and more reliably sample the correlation space. This procedure implements pseudo-random sampling of a correlation with a bounded input range to maintain the correlation form, to respect variable uncertainty across the range, and to ensure function continuity with respect to the input variable.

Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not all of which is representative of the original, limited training data. However, updating these deployed models can be difficult due to logistical, bandwidth, time, hardware, and/or data sensitivity constraints. We propose a framework, Self-Updating Models with Error Remediation (SUMER), in which a deployed model updates itself as new data becomes available. SUMER uses techniques from semi-supervised learning and noise remediation to iteratively retrain a deployed model using intelligently-chosen predictions from the model as the labels for new training iterations. A key component of SUMER is the notion of error remediation as self-labeled data can be susceptible to the propagation of errors. We investigate the use of SUMER across various data sets and iterations. We find that self-updating models (SUMs) generally perform better than models that do not attempt to self-update when presented with additional previously-unseen data. This performance gap is accentuated in cases where there is only limited amounts of initial training data. We also find that the performance of SUMER is generally better than the performance of SUMs, demonstrating a benefit in applying error remediation. Consequently, SUMER can autonomously enhance the operational capabilities of existing data processing systems by intelligently updating models in dynamic environments.

We derive a formulation of the nonhydrostatic equations in spherical geometry with a Lorenz staggered vertical discretization. The combination conserves a discrete energy in exact time integration when coupled with a mimetic horizontal discretization. The formulation is a version of Dubos and Tort (2014, https://doi.org/10.1175/MWR-D-14-00069.1) rewritten in terms of primitive variables. It is valid for terrain following mass or height coordinates and for both Eulerian or vertically Lagrangian discretizations. The discretization relies on an extension to Simmons and Burridge (1981, https://doi.org/10.1175/1520-0493(1981)109<0758:AEAAMC>2.0.CO;2) vertical differencing, which we show obeys a discrete derivative product rule. This product rule allows us to simplify the treatment of the vertical transport terms. Energy conservation is obtained via a term-by-term balance in the kinetic, internal, and potential energy budgets, ensuring an energy-consistent discretization up to time truncation error with no spurious sources of energy. We demonstrate convergence with respect to time truncation error in a spectral element code with a horizontal explicit vertically implicit implicit-explicit time stepping algorithm.

There is a wealth of psychological theory regarding the drive for individuals to congregate and form social groups, positing that people may organize out of fear, social pressure, or even to manage their self-esteem. We evaluate three such theories for multi-scale validity by studying them not only at the individual scale for which they were originally developed, but also for applicability to group interactions and behavior. We implement this multi-scale analysis using a dataset of communications and group membership derived from a long-running online game, matching the intent behind the theories to quantitative measures that describe players’ behavior. Once we establish that the theories hold for the dataset, we increase the scope to test the theories at the higher scale of group interactions. Despite being formulated to describe individual cognition and motivation, we show that some group dynamics theories hold at the higher level of group cognition and can effectively describe the behavior of joint decision making and higher-level interactions.

A focus in the development of the next generation of concentrating solar power (CSP) plants is the integration of high temperature particle receivers with improved efficiency supercritical carbon dioxide (sCO2) power cycles. The feasibility of this type of system depends on the design of a particle-to-sCO2 heat exchanger. This work presents a finite element analysis (FEA) model to analyze the thermal performance of a particle-to-sCO2 heat exchanger for potential use in a CSP plant. The heat exchanger design utilizes a moving packed bed of particles in crossflow with sCO2 which flows in a serpentine pattern through banks of microchannel plates. The model contains a thermal analysis to determine the heat exchanger's performance in transferring thermal energy from the particle bed to the sCO2. Test data from a prototype heat exchanger was used to verify the performance predictions of the model. The verification of the model required a multitude of sensitivity tests to identify where fidelity needed to be added to reach agreement between the experimental and simulated results. For each sensitivity test in the model, the effect on the performance is discussed. The model was shown to be in good agreement on the overall heat transfer coefficient of the heat exchanger with the experimental results for a low temperature set of conditions with a combination of added sensitives. A set of key factors with a major impact on the performance of the heat exchanger are discussed.

A focus in the development of the next generation of concentrating solar power (CSP) plants is the integration of high temperature particle receivers with improved efficiency supercritical carbon dioxide (sCO2) power cycles. The feasibility of this type of system depends on the design of a particle-to-sCO2 heat exchanger. This work presents a finite element analysis (FEA) model to analyze the thermal performance of a particle-to-sCO2 heat exchanger for potential use in a CSP plant. The heat exchanger design utilizes a moving packed bed of particles in crossflow with sCO2 which flows in a serpentine pattern through banks of microchannel plates. The model contains a thermal analysis to determine the heat exchanger's performance in transferring thermal energy from the particle bed to the sCO2. Test data from a prototype heat exchanger was used to verify the performance predictions of the model. The verification of the model required a multitude of sensitivity tests to identify where fidelity needed to be added to reach agreement between the experimental and simulated results. For each sensitivity test in the model, the effect on the performance is discussed. The model was shown to be in good agreement on the overall heat transfer coefficient of the heat exchanger with the experimental results for a low temperature set of conditions with a combination of added sensitives. A set of key factors with a major impact on the performance of the heat exchanger are discussed.

The National Solar Thermal Test Facility (NSTTF) at Sandia National Laboratories is conducting research on a Generation 3 Particle Pilot Plant (G3P3) that uses falling sand-like particles as the heat transfer medium. The system will include a thermal energy storage (TES) bin with a capacity of 6 MWht¬ requiring ~120,000 kg of flowing particles. Testing and modeling were conducted to develop a validated modeling tool to understand temporal and spatial temperature distributions within the storage bin as it charges and discharges. Flow and energy transport in funnel-flow was modeled using volume averaged conservation equations coupled with level set interface tracking equations that prescribe the dynamic geometry of particle flow within the storage bin. A thin layer of particles on top of the particle bed was allowed to flow toward the center and into the flow channel above the outlet. Model results were validated using particle discharge temperatures taken from thermocouples mounted throughout a small steel bin. The model was then used to predict heat loss during charging, storing, and discharging operational modes at the G3P3 scale. Comparative results from the modeling and testing of the small bin indicate that the model captures many of the salient features of the transient particle outlet temperature over time.

We present a nonlocal variational image completion technique which admits simultaneous inpainting of multiple structures and textures in a unified framework. The recovery of geometric structures is achieved by using general convolution operators as a measure of behavior within an image. These are combined with a nonlocal exemplar-based approach to exploit the self-similarity of an image in the selected feature domains and to ensure the inpainting of textures. We also introduce an anisotropic patch distance metric to allow for better control of the feature selection within an image and present a nonlocal energy functional based on this metric. Finally, we derive an optimization algorithm for the proposed variational model and examine its validity experimentally with various test images.

A self-tuning proportional-integral control law prescribing motor torques was tested in experiment on a three degree-of-freedom wave energy converter. The control objective was to maximize electrical power. The control law relied upon an identified model of device intrinsic impedance to generate a frequency-domain estimate of the wave-induced excitation force and measurements of device velocities. The control law was tested in irregular sea-states that evolved over hours (a rapid, but realistic time-scale) and that changed instantly (an unrealistic scenario to evaluate controller response). For both cases, the controller converges to gains that closely approximate the post-calculated optimal gains for all degrees of freedom. Convergence to near-optimal gains occurred reliably over a sufficiently short time for realistic sea states. In addition, electrical power was found to be relatively insensitive to gain tuning over a broad range of gains, implying that an imperfectly tuned controller does not result in a large penalty to electrical power capture. An extension of this control law that allows for adaptation to a changing device impedance model over time is proposed for long-term deployments, as well as an approach to explicitly handle constraints within this architecture.

This paper discusses how to design an inter-area oscillations damping controller using a frequency-shaped optimal output feedback control approach. This control approach was chosen because inter-area oscillations occur at a particular frequency range, from 0.2 to 1 Hz, which is the interval the control action must be prioritized. This paper shows that using only the filter for the system states can sufficiently damp the system modes. In addition, the paper shows that the filter for the input can be adjusted to provide primary frequency regulation to the system with no effect to the desired damping control action. Time domain simulations of a power system with a set of controllable power injection devices are presented to show the effectiveness of the designed controller.

Falling particle receivers are an emerging technology for use in concentrating solar power systems. In this work, a staggered angle iron receiver concept is investigated, with the goals of increasing particle curtain stability and opacity in a receiver. The concept consists of angle iron-shaped troughs placed in line with a falling particle curtain in order to collect particles and re-release them, decreasing the downward velocity of the particles and the curtain spread. A particle flow test apparatus has been fabricated. The effect of staggered angle iron trough geometry, orientation, and position on the opacity and uniformity of a falling particle curtain for different particle linear mass flow rates is investigated using the particle flow test apparatus. For the baseline free falling curtain and for different trough configurations, particle curtain transmissivity is measured, and profile images of the particle curtain are taken. Particle mass flow rate and trough position affect curtain transmissivity more than trough orientation and geometry. Optimal trough position for a given particle mass flow rate can result in improved curtain stability and decreased transmissivity. The case with a slot depth of 1/4”, hybrid trough geometry at 36” below the slot resulted in the largest improvement over the baseline curtain: 0.40 transmissivity for the baseline and 0.14 transmissivity with the trough. However, some trough configurations have a detrimental effect on curtain stability and result in increased curtain transmissivity and/or substantial particle bouncing.

This paper discusses how to design an inter-area oscillations damping controller using a frequency-shaped optimal output feedback control approach. This control approach was chosen because inter-area oscillations occur at a particular frequency range, from 0.2 to 1 Hz, which is the interval the control action must be prioritized. This paper shows that using only the filter for the system states can sufficiently damp the system modes. In addition, the paper shows that the filter for the input can be adjusted to provide primary frequency regulation to the system with no effect to the desired damping control action. Time domain simulations of a power system with a set of controllable power injection devices are presented to show the effectiveness of the designed controller.

In the presence of model discrepancy, the calibration of physics-based models for physical parameter inference is a challenging problem. Lack of identifiability between calibration parameters and model discrepancy requires additional identifiability constraints to be placed on the model discrepancy to obtain unique physical parameter estimates. If these assumptions are violated, the inference for the calibration parameters can be systematically biased. In many applications, such as in dynamic material property experiments, many of the calibration inputs refer to measurement uncertainties. In this setting, we develop a metric for identifying overfitting of these measurement uncertainties, propose a prior capable of reducing this overfitting, and show how this leads to a diagnostic tool for validation of physical parameter inference. The approach is demonstrated for a benchmark example and applied for a material property application to perform inference on the equation of state parameters of tantalum.

This paper considers preconditioners for the linear systems that arise from optimal control and inverse problems involving the Helmholtz equation. Specifically, we explore an all-at-once approach. The main contribution centers on the analysis of two block preconditioners. Variations of these preconditioners have been proposed and analyzed in prior works for optimal control problems where the underlying partial differential equation is a Laplace-like operator. In this paper, we extend some of the prior convergence results to Helmholtz-based optimization applications. Our analysis examines situations where control variables and observations are restricted to subregions of the computational domain. We prove that solver convergence rates do not deteriorate as the mesh is refined or as the wavenumber increases. More specifically, for one of the preconditioners we prove accelerated convergence as the wavenumber increases. Additionally, in situations where the control and observation subregions are disjoint, we observe that solver convergence rates have a weak dependence on the regularization parameter. We give a partial analysis of this behavior. We illustrate the performance of the preconditioners on control problems motivated by acoustic testing.

The work presented in this paper applies the MELCOR code developed at Sandia National Laboratories to evaluate the source terms from potential accidents in non-reactor nuclear facilities. The present approach provides an integrated source term approach that would be well-suited for uncertainty analysis and probabilistic risk assessments. MELCOR is used to predict the thermal-hydraulic conditions during fires or explosions that includes a release of radionuclides. The radionuclides are tracked throughout the facility from the initiating event to predict the time-dependent source term to the environment for subsequent dose or consequence evaluations. In this paper, we discuss the MELCOR input model development and the evaluation of the potential source terms from the dominated fire and explosion scenarios for a spent fuel nuclear reprocessing plant.

Bayesian optimization (BO) is an efficient and flexible global optimization framework that is applicable to a very wide range of engineering applications. To leverage the capability of the classical BO, many extensions, including multi-objective, multi-fidelity, parallelization, and latent-variable modeling, have been proposed to address the limitations of the classical BO framework. In this work, we propose a novel multi-objective (MO) extension, called srMOBO-3GP, to solve the MO optimization problems in a sequential setting. Three different Gaussian processes (GPs) are stacked together, where each of the GP is assigned with a different task: the first GP is used to approximate a single-objective computed from the MO definition, the second GP is used to learn the unknown constraints, and the third GP is used to learn the uncertain Pareto frontier. At each iteration, a MO augmented Tchebycheff function converting MO to single-objective is adopted and extended with a regularized ridge term, where the regularization is introduced to smooth the single-objective function. Finally, we couple the third GP along with the classical BO framework to explore the richness and diversity of the Pareto frontier by the exploitation and exploration acquisition function. The proposed framework is demonstrated using several numerical benchmark functions, as well as a thermomechanical finite element model for flip-chip package design optimization.

Wear prediction is important in designing reliable machinery for slurry industry. It usually relies on multi-phase computational fluid dynamics, which is accurate but computationally expensive. Each run of the simulations can take hours or days even on a high-performance computing platform. The high computational cost prohibits a large number of simulations in the process of design optimization. In contrast to physics-based simulations, data-driven approaches such as machine learning are capable of providing accurate wear predictions at a small fraction of computational costs, if the models are trained properly. In this paper, a recently developed WearGP framework [1] is extended to predict the global wear quantities of interest by constructing Gaussian process surrogates. The effects of different operating conditions are investigated. The advantages of the WearGP framework are demonstrated by its high accuracy and low computational cost in predicting wear rates.

Tensor decomposition is a fundamental unsupervised machine learning method in data science, with applications including network analysis and sensor data processing. This work develops a generalized canonical polyadic (GCP) low-rank tensor decomposition that allows other loss functions besides squared error. For instance, we can use logistic loss or Kullback{Leibler divergence, enabling tensor decomposition for binary or count data. We present a variety of statistically motivated loss functions for various scenarios. We provide a generalized framework for computing gradients and handling missing data that enables the use of standard optimization methods for fitting the model. We demonstrate the exibility of the GCP decomposition on several real-world examples including interactions in a social network, neural activity in a mouse, and monthly rainfall measurements in India.

Recent advances in horizontal cask designs for commercial spent nuclear fuel have significantly increased maximum thermal loading. This is due in part to greater efficiency in internal conduction pathways. Carefully measured data sets generated from testing of full-sized casks or smaller cask analogs are widely recognized as vital for validating thermal-hydraulic models of these storage cask designs. While several testing programs have been previously conducted, these earlier validation studies did not integrate all the physics or components important in a modern, horizontal dry cask system. The purpose of this investigation is to produce data sets that can be used to benchmark the codes and best practices presently used to calculate cladding temperatures and induced cooling air flows in modern, horizontal dry storage systems. The horizontal dry cask simulator (HDCS) has been designed to generate this benchmark data and complement the existing knowledge base. Transverse and axial temperature profiles along with induced-cooling air flow are measured using various backfills of gases for a wide range of decay powers and canister pressures. The data from the HDCS tests will be used to host a blind model validation effort.

Estimation of the uncertainty in a critical experiment attributable to uncertainties in the measured experiment temperature is done by calculating the variation of the eigenvalue of a benchmark configuration as a function of temperature. In the low-enriched water-moderated critical experiments performed at Sandia, this is done by 1) estimating the effects of changing the water temperature while holding the UO₂ fuel temperature constant, 2) estimating the effects of changing the UO₂ temperature while holding the water temperature constant, and 3) combining the two results. This assumes that the two effects are separable. The results of such an analysis are nonintuitive and need experimental verification. Critical experiments are being planned at Sandia National Laboratories (Sandia) to measure the effect of temperature on critical systems and will serve to test the methods used in estimating the temperature effects in critical experiments.

The Natural Energy Laboratory of Hawaii Authority's (NELHA) campus on The Island of Hawai'i supplies resources for a number of renewable energy and aquaculture research projects. There is a growing interest at NELHA to convert the research campus to a 100% renewable, islanded microgrid to improve the resiliency of the campus for critical ocean water pumping loads and to limit the increase in the long-term cost of operations. Currently, the campus has solar array to cover some electricity needs but scaling up this system to fully meet the needs of the entire research campus will require significant changes and careful planning to minimize costs. This study will investigate least-cost solar and energy storage system sizes capable of meeting the needs of the campus. The campus is split into two major load centers that are electrically isolated and have different amounts of available land for solar installations. The value of adding an electrical transmission line if NELHA converts to a self-contained microgrid is explored by estimating the cost of resources for each load center individually and combined. Energy storage using lithium-ion and hydrogen-based technologies is investigated. For the hydrogen-based storage system, a variable efficiency and fixed efficiency representation of the electrolysis and fuel cell systems are used. Results using these two models show the importance of considering the changing performance of hydrogen systems for sizing algorithms.

In the decade since support for task parallelism was incorporated into OpenMP, its use has remained limited in part due to concerns about its performance and scalability. This paper revisits a study from the early days of OpenMP tasking that used the Unbalanced Tree Search (UTS) benchmark as a stress test to gauge implementation efficiency. The present UTS study includes both Clang/LLVM and vendor OpenMP implementations on four different architectures. We measure parallel efficiency to examine each implementation’s performance in response to varying task granularity. We find that most implementations achieve over 90% efficiency using all available cores for tasks of O(100k) instructions, and the best even manage tasks of O(10k) instructions well.

Given the increasing ubiquity of online embedded devices, analyzing their firmware is important to security, privacy, and safety. The tight coupling between hardware and firmware and the diversity found in embedded systems makes it hard to perform dynamic analysis on firmware. However, firmware developers regularly develop code using abstractions, such as Hardware Abstraction Layers (HALs), to simplify their job. We leverage such abstractions as the basis for the re-hosting and analysis of firmware. By providing high-level replacements for HAL functions (a process termed High-Level Emulation - HLE), we decouple the hardware from the firmware. This approach works by first locating the library functions in a firmware sample, through binary analysis, and then providing generic implementations of these functions in a full-system emulator. We present these ideas in a prototype system, HALucinator, able to re-host firmware, and allow the virtual device to be used normally. First, we introduce extensions to existing library matching techniques that are needed to identify library functions in binary firmware, to reduce collisions, and for inferring additional function names. Next, we demonstrate the re-hosting process, through the use of simplified handlers and peripheral models, which make the process fast, flexible, and portable between firmware samples and chip vendors. Finally, we demonstrate the practicality of HLE for security analysis, by supplementing HALucinator with the American Fuzzy Lop fuzzer, to locate multiple previously-unknown vulnerabilities in firmware middleware libraries.

A passive yaw implementation is developed, validated, and explored for the WEC-Sim, an open-source wave energy converter modeling tool that works within MATLAB/Simulink. The Reference Model 5 (RM5) is selected for this investigation, and a WEC-Sim model of the device is modified to allow yaw motion. A boundary element method (BEM) code was used to calculate the excitation force coefficients for a range of wave headings. An algorithm was implemented in WEC-Sim to determine the equivalent wave heading from a body's instantaneous yaw angle and interpolate the appropriate excitation coefficients to ensure the correct time-domain excitation force. This approach is able to determine excitation force for a body undergoing large yaw displacement. For the mathematically simple case of regular wave excitation, the dynamic equation was integrated numerically and found to closely approximate the results from this implementation in WEC-Sim. A case study is presented for the same device in irregular waves. In this case, computation time is increased by 32x when this interpolation is performed at every time step. To reduce this expense, a threshold yaw displacement can be set to reduce the number of interpolations performed. A threshold of 0.01o was found to increase computation time by only 22x without significantly affecting time domain results. Similar amplitude spectra for yaw force and displacements are observed for all threshold values less than 1o, for which computation time is only increased by 2.2x.

Compact semiconductor device models are essential for efficiently designing and analyzing large circuits. However, traditional compact model development requires a large amount of manual effort and can span many years. Moreover, inclusion of new physics (e.g., radiation effects) into an existing model is not trivial and may require redevelopment from scratch. Machine Learning (ML) techniques have the potential to automate and significantly speed up the development of compact models. In addition, ML provides a range of modeling options that can be used to develop hierarchies of compact models tailored to specific circuit design stages. In this paper, we explore three such options: (1) table-based interpolation, (2) Generalized Moving Least-Squares, and (3) feed-forward Deep Neural Networks, to develop compact models for a p-n junction diode. We evaluate the performance of these “data-driven” compact models by (1) comparing their voltage-current characteristics against laboratory data, and (2) building a bridge rectifier circuit using these devices, predicting the circuit's behavior using SPICE-like circuit simulations, and then comparing these predictions against laboratory measurements of the same circuit.

Motivated by the gap between theoretical optimal approximation rates of deep neural networks (DNNs) and the accuracy realized in practice, we seek to improve the training of DNNs. The adoption of an adaptive basis viewpoint of DNNs leads to novel initializations and a hybrid least squares/gradient descent optimizer. We provide analysis of these techniques and illustrate via numerical examples dramatic increases in accuracy and convergence rate for benchmarks characterizing scientific applications where DNNs are currently used, including regression problems and physics-informed neural networks for the solution of partial differential equations.

The multi-institution Single-Volume Scatter Camera (SVSC) collaboration led by Sandia National Laboratories (SNL) is developing a compact, high-efficiency double-scatter neutron imaging system. Kinematic emission imaging of fission-energy neutrons can be used to detect, locate, and spatially characterize special nuclear material. Neutron-scatter cameras, analogous to Compton imagers for gamma ray detection, have a wide field of view, good event-by-event angular resolution, and spectral sensitivity. Existing systems, however, suffer from large size and/or poor efficiency. We are developing high-efficiency scatter cameras with small form factors by detecting both neutron scatters in a compact active volume. This effort requires development and characterization of individual system components, namely fast organic scintillators, photodetectors, electronics, and reconstruction algorithms. In this presentation, we will focus on characterization measurements of several SVSC candidate scintillators. The SVSC collaboration is investigating two system concepts: the monolithic design in which isotropically emitted photons are detected on the sides of the volume, and the optically segmented design in which scintillation light is channeled along scintillator bars to segmented photodetector readout. For each of these approaches, we will describe the construction and performance of prototype systems. We will conclude by summarizing lessons learned, comparing and contrasting the two system designs, and outlining plans for the next iteration of prototype design and construction.

Although popular in industry, state-chart notations with ‘run to completion’ semantics lack formal refinement and rigorous verification methods. State-chart models are typically used to design complex control systems that respond to environmental triggers with a sequential process. The model is usually constructed at a concrete level and verified and validated using animation techniques relying on human judgement. Event-B, on the other hand, is based on refinement from an initial abstraction and is designed to make formal verification by automatic theorem provers feasible. We introduce a notion of refinement into a ‘run to completion’ statechart modelling notation, and leverage Event-B ’s tool support for theorem proving. We describe the difficulties in translating ‘run to completion’ semantics into Event-B refinements and suggest a solution. We illustrate our approach and show how critical (e.g. safety) invariant properties can be verified by proof despite the reactive nature of the system. We also show how behavioural aspects of the system can be verified by testing the expected reactions using a temporal logic model checking approach.

Automatic detection of defects in as-built parts is a challenging task due to the large number of potential manufacturing flaws that can occur. X-Ray computed tomography (CT) can produce high-quality images of the parts in a non-destructive manner. The images, however, are grayscale valued, often have artifacts and noise, and require expert interpretation to spot flaws. In order for anomaly detection to be reproducible and cost effective, an automated method is needed to find potential defects. Traditional supervised machine learning techniques fail in the high reliability parts regime due to large class imbalance: there are often many more examples of well-built parts than there are defective parts. This, coupled with the time expense of obtaining labeled data, motivates research into unsupervised techniques. In particular, we build upon the AnoGAN and f-AnoGAN work by T. Schlegl et al. and created a new architecture called PandaNet. PandaNet learns an encoding function to a latent space of defect-free components and a decoding function to reconstruct the original image. We restrict the training data to defect-free components so that the encode-decode operation cannot learn to reproduce defects well. The difference between the reconstruction and the original image highlights anomalies that can be used for defect detection. In our work with CT images, PandaNet successfully identifies cracks, voids, and high z inclusions. Beyond CT, we demonstrate PandaNet working successfully with little to no modifications on a variety of common 2-D defect datasets both in color and grayscale.

Structural dynamic finite element models typically use multipoint constraints (MPC) to condense the degrees of freedom (DOF) near bolted joints down to a single node, which can then be joined to neighboring structures with linear springs or nonlinear elements. Scalability becomes an issue when multiple joints are present in a system, because each requires its own model to capture the nonlinear behavior. While this increases the computational cost, the larger problem is that the parameters of the joint models are not known, and so one must solve a nonlinear model updating problem with potentially hundreds of unknown variables to fit the model to measurements. Furthermore, traditional MPC approaches are limited in how the flexibility of the interface is treated (i.e. with rigid bar elements the interface has no flexibility). To resolve this shortcoming, this work presents an alternative approach where the contact interface is reduced to a set of modal DOF which retain the flexibility of the interface and are capable of modeling multiple joints simultaneously. Specifically, system-level characteristic constraint (S-CC) reduction is used to reduce the motion at the contact interface to a small number of shapes. To capture the hysteresis and energy dissipation that is present during microslip of joints, a hysteretic element is applied to a small number of the S-CC Shapes. This method is compared against a traditional MPC method (using rigid bar elements) on a two-dimensional finite element model of a cantilever beam with a single joint near the free end. For all methods, a four-parameter Iwan element is applied to the interface DOF to capture how the amplitude dependent modal frequency and damping change with vibration amplitude.

Ceramic to metal brazing is a common bonding process usedin many advanced systems such as automotive engines, aircraftengines, and electronics. In this study, we use optimizationtechniques and finite element analysis utilizing viscoplastic andthermo-elastic material models to find an optimum thermalprofile for a Kovar® washer bonded to an alumina button that istypical of a tension pull test. Several active braze filler materialsare included in this work. Cooling rates, annealing times, aging,and thermal profile shapes are related to specific materialbehaviors. Viscoplastic material models are used to represent thecreep and plasticity behavior in the Kovar® and braze materialswhile a thermo-elastic material model is used on the alumina.The Kovar® is particularly interesting because it has a Curiepoint at 435°C that creates a nonlinearity in its thermal strain andstiffness profiles. This complex behavior incentivizes theoptimizer to maximize the stress above the Curie point with afast cooling rate and then favors slow cooling rates below theCurie point to anneal the material. It is assumed that if failureoccurs in these joints, it will occur in the ceramic material.Consequently, the maximum principle stress of the ceramic isminimized in the objective function. Specific details of the stressstate are considered and discussed.

Functional data are fast becoming a preeminent source of information across a wide range of industries. A particularly challenging aspect of functional data is bounding uncertainty. In this unique case study, we present our attempts at creating bounding functions for selected applications at Sandia National Laboratories (SNL). The first attempt involved a simple extension of functional principal component analysis (fPCA) to incorporate covariates. Though this method was straightforward, the extension was plagued by poor coverage accuracy for the bounding curve. This led to a second attempt utilizing elastic methodology which yielded more accurate coverage at the cost of more complexity.

Heterogeneous Integration (HI) may enable optoelectronic transceivers for short-range and long-range radio frequency (RF) photonic interconnect using wavelength-division multiplexing (WDM) to aggregate signals, provide galvanic isolation, and reduce crosstalk and interference. Integration of silicon Complementary Metal-Oxide-Semiconductor (CMOS) electronics with InGaAsP compound semiconductor photonics provides the potential for high-performance microsystems that combine complex electronic functions with optoelectronic capabilities from rich bandgap engineering opportunities, and intimate integration allows short interconnects for lower power and latency. The dominant pure-play foundry model plus the differences in materials and processes between these technologies dictate separate fabrication of the devices followed by integration of individual die, presenting unique challenges in die preparation, metallization, and bumping, especially as interconnect densities increase. In this paper, we describe progress towards realizing an S-band WDM RF photonic link combining 180 nm silicon CMOS electronics with InGaAsP integrated optoelectronics, using HI processes and approaches that scale into microwave and millimeter-wave frequencies.

Rank-revealing matrix decompositions provide an essential tool in spectral analysis of matrices, including the Singular Value Decomposition (SVD) and related low-rank approximation techniques. QR with Column Pivoting (QRCP) is usually suitable for these purposes, but it can be much slower than the unpivoted QR algorithm. For large matrices, the difference in performance is due to increased communication between the processor and slow memory, which QRCP needs in order to choose pivots during decomposition. Our main algorithm, Randomized QR with Column Pivoting (RQRCP), uses randomized projection to make pivot decisions from a much smaller sample matrix, which we can construct to reside in a faster level of memory than the original matrix. This technique may be understood as trading vastly reduced communication for a controlled increase in uncertainty during the decision process. For rank-revealing purposes, the selection mechanism in RQRCP produces results that are the same quality as the standard algorithm, but with performance near that of unpivoted QR (often an order of magnitude faster for large matrices). We also propose two formulas that facilitate further performance improvements. The first efficiently updates sample matrices to avoid computing new randomized projections. The second avoids large trailing updates during the decomposition in truncated low-rank approximations. Our truncated version of RQRCP also provides a key initial step in our truncated SVD approximation, TUXV. These advances open up a new performance domain for large matrix factorizations that will support efficient problem-solving techniques for challenging applications in science, engineering, and data analysis.

We describe the development and benchtop prototype performance characterization of a mechatronic system for automatically drilling small diameter holes of arbitrary depth, to enable monitoring the integrity of oil and gas wells in situ. The precise drilling of very small diameter, high aspect ratio holes, particularly in dimensionally constrained spaces, presents several challenges including bit buckling, limited torsional stiffness, chip clearing, and limited space for the bit and mechanism. We describe a compact mechanism that overcomes these issues by minimizing the unsupported drill bit length throughout the process, enabling the bit to be progressively fed from a chuck as depth increases. When used with flexible drill bits, holes of arbitrary depth and aspect ratio may be drilled orthogonal to the wellbore. The mechanism and a conventional drilling system are tested in deep hole drilling operation. The experimental results show that the system operates as intended and achieves holes with substantially greater aspect ratios than conventional methods with very long drill bits. The mechanism enabled successful drilling of a 1/16" diameter hole to a depth of 9", a ratio of 144:1. Dysfunctions prevented drilling of the same hole using conventional methods.

We present a method of determining the background doping type in semiconductors using capacitance-voltage measurements on overetched double mesa p-i-n or n-i-p structures. Unlike Hall measurements, this method is not limited by the conductivity of the substrate. By measuring the capacitance of devices with varying top and bottom mesa sizes, we were able to conclusively determine which mesa contained the p-n junction, revealing the polarity of the intrinsic layer. This method, when demonstrated on GaSb p-i-n and n-i-p structures, determined that the material is residually doped p-type, which is well established by other sources. The method was then applied on a 10 monolayer InAs/10 monolayer AlSb superlattice, for which the doping polarity was unknown, and indicated that this material is also p-type.

Neuromorphic computing captures the quintessential neural behaviors of the brain and is a promising candidate for the beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. The neuronal lateral inhibition feature, closely associated with the biological receptive field, is crucial to neuronal competition in the nervous system as well as its neuromorphic hardware counterpart. The domain wall - magnetic tunnel junction (DW-MTJ) neuron is an emerging spintronic artificial neuron device exhibiting intrinsic lateral inhibition. This work discusses lateral inhibition mechanism of the DW-MTJ neuron and shows by micromagnetic simulation that lateral inhibition is efficiently enhanced by the Dzyaloshinskii-Moriya interaction (DMI).

Sinuous antennas are capable of producing ultra-wideband radiation with polarization diversity. This capability makes the sinuous antenna an attractive candidate for UWB polarimetric radar applications. Additionally, the ability of the sinuous antenna to be implemented as a planar structure makes it a good fit for close-in sensing applications such as ground-penetrating radar (GPR). In this work, each arm of a four-port sinuous antenna is operated independently to achieve a quasi-monostatic antenna system capable of polarimetry while separating transmit and receive channels-which is often desirable in GPR systems. The quasi-monostatic configuration of the sinuous antenna reduces system size as well as prevents extreme bistatic angles, which may significantly reduce sensitivity when attempting to detect near-surface targets. A prototype four-port sinuous antenna is fabricated and integrated into a GPR testbed. The polarimetric data obtained with the antenna is then used to distinguish between buried target symmetries.

We propose a technique for reconstruction from incomplete compressive measurements. Our approach combines compressive sensing and matrix completion using the consensus equilibrium framework. Consensus equilibrium breaks the reconstruction problem into subproblems to solve for the high-dimensional tensor. This framework allows us to apply two constraints on the statistical inversion problem. First, matrix completion enforces a low rank constraint on the compressed data. Second, the compressed tensor should be consistent with the uncompressed tensor when it is projected onto the low-dimensional subspace. We validate our method on the Indian Pines hyperspectral dataset with varying amounts of missing data. This work opens up new possibilities for data reduction, compression, and reconstruction.

Placing microwave absorbing materials into a high-quality factor resonant cavity may in general reduce the large interior electromagnetic fields excited under external illumination. In this paper, we aim to combine two analytical models we previously developed: 1) an unmatched formulation for frequencies below the slot resonance to model shielding effectiveness versus frequency; and 2) a perturbation model approach to estimate the quality factor of cavities in the presence of absorbers. The resulting model realizes a toolkit with which design guidelines of the absorber’s properties and location can be optimized over a frequency band. Analytic predictions of shielding effectiveness for three transverse magnetic modes for various locations of the absorber placed on the inside cavity wall show good agreement with both full-wave simulations and experiments, and validate the proposed model. This analysis opens new avenues for specialized ways to mitigate harmful fields within cavities.

The attached eddy hypothesis of Townsend (The Structure of Turbulent Shear Flow, 1956, Cambridge University Press) states that the logarithmic mean velocity admits self-similar energy-containing eddies which scale with the distance from the wall. Over the past decade, there has been a significant amount of evidence supporting the hypothesis, placing it to be the central platform for the statistical description of the general organisation of coherent structures in wall-bounded turbulent shear flows. Nevertheless, the most fundamental question, namely why the hypothesis has to be true, has remained unanswered over many decades. Under the assumption that the integral length scale is proportional to the distance from the wall y, in the present study we analytically demonstrate that the mean velocity is a logarithmic function of y if and only if the energy balance at the integral length scale is self-similar with respect to y, providing a theoretical basis for the attached eddy hypothesis. The analysis is subsequently verified with the data from a direct numerical simulation of incompressible channel flow at the friction Reynolds number Reτ ≃ 5200 (Lee & Moser, J. Fluid Mech., vol. 774, 2015, pp. 395-415).

Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system consists of unsupervised (clustering) as well as supervised sub-systems, and generalizes quickly (with few samples). We demonstrate interactions between physical properties of this device and optimal implementation of neuroscience-inspired plasticity learning rules, and highlight performance on a suite of tasks. Our energy analysis confirms the value of the approach, as the learning budget stays below 20µJ even for large tasks used typically in machine learning.

Ceramic to metal brazing is a common bonding process usedin many advanced systems such as automotive engines, aircraftengines, and electronics. In this study, we use optimizationtechniques and finite element analysis utilizing viscoplastic andthermo-elastic material models to find an optimum thermalprofile for a Kovar® washer bonded to an alumina button that istypical of a tension pull test. Several active braze filler materialsare included in this work. Cooling rates, annealing times, aging,and thermal profile shapes are related to specific materialbehaviors. Viscoplastic material models are used to represent thecreep and plasticity behavior in the Kovar® and braze materialswhile a thermo-elastic material model is used on the alumina.The Kovar® is particularly interesting because it has a Curiepoint at 435°C that creates a nonlinearity in its thermal strain andstiffness profiles. This complex behavior incentivizes theoptimizer to maximize the stress above the Curie point with afast cooling rate and then favors slow cooling rates below theCurie point to anneal the material. It is assumed that if failureoccurs in these joints, it will occur in the ceramic material.Consequently, the maximum principle stress of the ceramic isminimized in the objective function. Specific details of the stressstate are considered and discussed.

Following the ASME codes, the design of pipelines and pressure vessels for transportation or storage of high-pressure hydrogen gas requires measurements of fatigue crack growth rates at design pressure. However, performing tests in high pressure hydrogen gas can be very costly as only a few laboratories have the unique capabilities. Recently, Code Case 2938 was accepted in ASME Boiler and Pressure Vessel Code (BPVC) VIII-3 allowing for design curves to be used in lieu of performing fatigue crack growth rate (da/dN vs. ?K) and fracture threshold (KIH) testing in hydrogen gas. The design curves were based on data generated at 100 MPa H2 on SA-372 and SA-723 grade steels; however, the data used to generate the design curves are limited to measurements of ?K values greater than 6 MPa m^1/2. The design curves can be extrapolated to lower ?K (<6 MPa m^1/2), but the threshold stress intensity factor (?Kth) has not been measured in hydrogen gas. In this work, decreasing ?K tests were performed at select hydrogen pressures to explore threshold (?Kth) for ferritic-based structural steels (e.g. pipelines and pressure vessels). The results were compared to decreasing ?K tests in air, showing that the fatigue crack growth rates in hydrogen gas appear to yield similar or even slightly lower da/dN values compared to the curves in air at low ?K values when tests were performed at stress ratios of 0.5 and 0.7. Correction for crack closure was implemented, which resulted in better agreement with the design curves and provide an upper bound throughout the entire ?K range, even as the crack growth rates approach ?Kth. This work gives further evidence of the utility of the design curves described in Code Case 2938 of the ASME BPVC VIII-3 for construction of high pressure hydrogen vessels.

3D Particle-In-Cell Direct Simulation Monte Carlo (PIC-DSMC) simulations of cm-sized devices cannot resolve atomic-scale (nm) surface features and thus one must generate micron-scale models for an effective “local” work function, field enhancement factor, and emission area. Here we report on development of a stochastic effective model based on atomic-scale characterization of as-built electrode surfaces. Representative probability density distributions of the work function and geometric field enhancement factor (beta) for a sputter-deposited Pt surface are generated from atomic-scale surface characterization using Scanning Tunneling Microscopy (STM), Atomic Force Microscopy (AFM), and Photoemission Electron Microscopy (PEEM). In the micron-scale model every simulated PIC-DSMC surface element draws work functions and betas for many independent “atomic emitters”. During the simulation the field emitted current from an element is computed by summing each “atomic emitter's” current. This model has reasonable agreement with measured micron-scale emitted currents across a range of electric field values.

Many in the structural dynamics community are currently researching a range of multiple-input/multiple-output problems and largely rely on commercially-available closed-loop controllers to execute their experiments. Generally, these commercially-available control systems are robust and prove adequate for a wide variety of testing. However, with the development of new techniques in this field, researchers will want to exercise these new techniques in laboratory tests. For example, modifying the control or input estimation method can have benefits to the accuracy of control, or provide higher response for a given input. Modification of the control methods is not typically possible in commercially-available control systems, therefore it is desirable to have some methodology available which allows researchers to synthesize input signals for multiple-input/multiple-output experiments. Here, methods for synthesizing multiply-correlated time histories based on desired cross spectral densities are demonstrated and then explored to understand effects of various parameters on the resulting signals, their statistics, and their relation to the specified cross spectral densities. This paper aims to provide researchers with a simple, step-by-step process which can be implemented to generate input signals for open-loop multiple-input/multiple-output experiments.

A super-resolution optical imaging method is presented that relies on the distinct temporal information associated with each fluorescent optical reporter to determine its spatial position to high precision with measurements of heavily scattered light. This multiple-emitter localization approach uses a diffusion equation forward model in a cost function, and has the potential to achieve micron-scale spatial resolution through centimeters of tissue. Utilizing some degree of temporal separation for the reporter emissions, position and emission strength are determined using a computationally efficient temporal-scanning multiresolution algorithm. The approach circumvents the spatial resolution challenges faced by earlier optical imaging approaches by using a diffusion equation forward model, and is promising for in vivo applications. For example, in principle, the method could be used to localize individual neurons firing throughout a rodent brain, enabling the direct imaging of neural network activity.

The Box Assembly with Removable Component (BARC) structure was developed as a challenge problem for those investigating boundary conditions and their effect on structural dynamic tests. To investigate the effects of boundary conditions on the dynamic response of the Removable Component, it was tested in three configurations, each with a different fixture and thus a different boundary condition. A “truth” configuration test with the component attached to its next-level assembly (the Box) was first performed to provide data that multi-axis tests of the component would aim to replicate. The following two tests aimed to reproduce the component responses of the first test through multi-axis testing. The first of these tests is a more “traditional” vibration test with the removable component attached to a “rigid” plate fixture. A second set of these tests replaces the fixture plate with flexible fixtures designed using topology optimization and created using additive manufacturing. These two test approaches are compared back to the truth test to determine how much improvement can be obtained in a laboratory test by using a fixture that is more representative of the compliance of the component’s assembly.

Historically the qualification process for vehicles carrying vulnerable components has centered around the Shock Response Spectrum (SRS) and qualification consisted of devising a collection of tests whose collective SRS enveloped the qualification SRS. This involves selecting whatever tests are convenient that will envelope the qualification SRS over at least part of its spectrum; this selection is without any consideration of the details of structural response or the nature of anticipated failure of its components. It is asserted that this approach often leads to over-testing, however, as has been pointed out several times in the literature, this approach may not even be conservative. Given the advances in computational and experimental technology in the last several decades, it would be appropriate to seek some strategy of test selection that does account for structural response and failure mechanism and that pushes against the vulnerabilities of that specific structure. A strategy for such a zemblanic (zemblanity is the opposite of serendipity, the faculty of making unhappy, unlucky and expected discoveries by design) approach is presented.

In this study, we present Balancing Domain Decomposition by Constraints (BDDC) preconditioners for three-dimensional scalar elliptic and linear elasticity problems in which the direct solution of the coarse problem is replaced by a preconditioner based on a smaller vertex-based coarse space.

This article describes a parallel implementation of a two-level overlapping Schwarz preconditioner with the GDSW (Generalized Dryja–Smith–Widlund) coarse space described in previous work [12, 10, 15] into the Trilinos framework; cf. [16]. The software is a significant improvement of a previous implementation [12]; see Sec. 4 for results on the improved performance.

Detonation velocity as a function of charge diameter is reported for Alliant Bullseye powder. Results are compared to those of mixtures of ammonium nitrate mixed with aluminum and ammonium nitrate mixed with fuel oil. Additionally, measurements of free surface velocity of flyers in contact with detonating Bullseye are presented and results are compared to those of hydrocode calculations using a Jones–Wilkins–Lee equation of state generated in a thermochemical code. Comparison to the experimental results shows that both the free surface and terminal velocities were under-predicted.

Predictions from numerical hemodynamics are increasingly adopted and trusted in the diagnosis and treatment of cardiovascular disease. However, the predictive abilities of deterministic numerical models are limited due to the large number of possible sources of uncertainty including boundary conditions, vessel wall material properties, and patient specific model anatomy. Stochastic approaches have been proposed as a possible improvement, but are penalized by the large computational cost associated with repeated solutions of the underlying deterministic model. We propose a stochastic framework which leverages three cardiovascular model fidelities, i.e., three-, one- and zero-dimensional representations of cardiovascular blood flow. Specifically, we employ multilevel and multifidelity estimators from Sandia's open-source Dakota toolkit to reduce the variance in our estimated quantities of interest, while maintaining a reasonable computational cost. The performance of these estimators in terms of computational cost reductions is investigated for both global and local hemodynamic indicators.

SNOWPAC (Stochastic Nonlinear Optimization With Path-Augmented Constraints) is a method for stochastic nonlinear constrained derivative-free optimization. For such problems, it extends the path-augmented constraints framework introduced by the deterministic optimization method NOWPAC and uses a noise-adapted trust region approach and Gaussian processes for noise reduction. In recent developments, SNOWPAC is available in the DAKOTA framework which offers a highly flexible interface to couple the optimizer with different sampling strategies or surrogate models. In this paper we discuss details of SNOWPAC and demonstrate the coupling with DAKOTA. We showcase the approach by presenting design optimization results of a shape in a 2D supersonic duct. This simulation is supposed to imitate the behavior of the flow in a SCRAMJET simulation but at a much lower computational cost. Additionally different mesh or model fidelities can be tested. Thus, it serves as a convenient test case before moving to costly SCRAMJET computations. Here, we study deterministic results and results obtained by introducing uncertainty on inflow parameters. As sampling strategies we compare classical Monte Carlo sampling with multilevel Monte Carlo approaches for which we developed new error estimators. All approaches show a reasonable optimization of the design over the objective while maintaining or seeking feasibility. Furthermore, we achieve significant reductions in computational cost by using multilevel approaches that combine solutions from different grid resolutions.

This study explores the use of a Krylov iterative method (GMRES) as a smoother for an algebraic multigrid (AMG) preconditioned Newton–Krylov iterative solution approach for a fully-implicit variational multiscale (VMS) finite element (FE) resistive magnetohydrodynamics (MHD) formulation. The efficiency of this approach is critically dependent on the scalability and performance of the AMG preconditioner for the linear solutions and the performance of the smoothers play an essential role. Krylov smoothers are considered an attempt to reduce the time and memory requirements of existing robust smoothers based on additive Schwarz domain decomposition (DD) with incomplete LU factorization solves on each subdomain. This brief study presents three time dependent resistive MHD test cases to evaluate the method. The results demonstrate that the GMRES smoother can be faster due to a decrease in the preconditioner setup time and a reduction in outer GMRESR solver iterations, and requires less memory (typically 35% less memory for global GMRES smoother) than the DD ILU smoother.