Publications

Surrogate model development is a key resource in the scientific modeling community for providing computational expedience when simulating complex systems without loss of great fidelity. The initial step to development of a surrogate model is identification of the primary governing components of the system. Principal component analysis (PCA) is a widely used data science technique that provides inspection of such driving factors, when the objective for modeling is to capture the greatest sources of variance inherent to a dataset. Although an efficient linear dimension reduction tool, PCA makes the fundamental assumption that the data is continuous and normally distributed. Thus, it provides ideal performance when these conditions are met. In the case for which cyber emulations provide realizations of a port scanning scenario, the data to be modeled follows a discrete time series function comprised of monotonically increasing piece-wise constant steps. The sources of variance are related to the timing and magnitude of these steps. Therefore, we consider using XPCA, an extension to PCA for continuous and discrete random variates. This report provides the documentation of the trade-offs between the PCA and XPCA linear dimension reduction algorithms, for the intended purpose to identify key components of greatest variance in our time series data. These components will ultimately provide the basis for future surrogate models of port scanning cyber emulations.

Neuromorphic devices are a rapidly growing area of interest in industry, with machines in production by IBM and Intel, among others. These devices promise to reduce size, weight and power (SWaP) costs while increasing resilience and facilitating high- performance computing (HPC). Each device will favor some set of algorithms, but this relationship has not been thoroughly studied. The field of neuromorphic computing is so new that existing devices were designed with merely estimated use-cases in mind. To better understand the fit between neuromorphic algorithms and machines, a simulated machine can be configured to any point in the design space. This will identify better choices of devices, and perhaps guide the market in new directions. The design of a generic spiking machine generalizes existing examples while also looking forward to devices that haven't been built yet. Each parameter is specified, along the approach/mechanism by which the relevant component is implemented in the simulator.

Catalytic conversion of methane (CH ₄) into useful products is critical for maximizing the utility of natural gas output and for reducing green house gas release associated with flaring (burning off CH₄ at natural gas extraction sites). One particular useful technique is methane dry reforming (DRM), which involves the chemical reaction of CH₄ with carbon dioxide (CO₂) to generate carbon monoxide (CO), hydrogen gas (H₂), and subsequently other useful products. New and improved catalysts are required to facilitate efficient dry methane reforming. In this report, we apply the Density Functional Theory (DFT) computational technique to investigate a catalyst consisting of small nickel clusters (Ni _n , n < 10) on ceria (Ce0₂ (111) surfaces) support. One main thrust of this project is to study the initial CH₄ and CO₂ reactions with the catalyst. We find that CH₄ exhibits barrierless reactive adsorption on to the catalyst. In order words, this step is likely not the rate-determining step. A second thrust is to perform detailed studies of the catalyst itself and examine the role of oxygen vacancies. Using a specific DFT method and a hypothesis about the absence of the Ce(III) redox state, we obtain predictions about oxygen vacancies in good agreement with experimental observations.

Nuclear quadrupole resonance is a non-destructive detection and inspection technique with potential as a non-destructive test (NDT) tool. Establishment of the capability opens the door to its use in furthering the mission of the labs. There are many possible uses of the capability: explosive detection and stress/strain detection in epoxies are two of the more obvious and are the main results of this work. Enhancement of the signal-to-noise ratio (SNR) and improvements in the acquisition time of the experiment were key focuses of this work. These were achieved by combing special spin-lock pulse sequences with cross-polarization (CP) schemes to improve the signals with shorter acquisition times. A novel rotating magnetic field device was created to facilitate CP in the field. Implementation of these schemes provided a significant reduction in SNR/time.

Experimental validation data is needed to inform simulations of large pulsed power devices which are in development to understand and improve existing accelerators and inform future pulsed power capabilities. Using current spectroscopic techniques on the Z-machine, we have been unable to reliably diagnose plasma conditions and electric and magnetic fields within power flow regions. Laser ablation of a material produces a low density plasma, resulting in narrow spectroscopic line widths. By introducing a laser ablated plasma to the anode cathode gap of the Mykonos accelerator, we can monitor how the line shapes change due the current pulse by comparing these line shapes to spectral measurements taken without power flow. In this report we show several examples of measurements conducted on Mykonos on various dopant materials. We also show a negligible effect on power flow due to the presence of the ablation plasma for a range of parameters.

Plasma physics is an exciting field of study with a wide variety of nonlinear processes that come into play. Examples of such processes include the interaction of small-scale turbulence with large-scale plasma structures and the nonlinear saturation of plasma instabilities, for example those of magneto-hydrodynamical nature. During this Truman LDRD project, I studied a collection of nonlinear problems that are of interest to the field of plasma physics. This LDRD report summarizes four main research accomplishments. First, a new statistical model for describing inhomogeneous drift-wave turbulence inter- acting with zonal flows was developed. This new model includes the effects of nonlinear wave-wave collisions, which are expected to change the spectrum of the underlying DW turbulence and therefore the generation of zonal flows. Second, a new mathematical formalism was proposed to systematically apply the non- linear WKB approximation to general field theories, including those often used in fluid dynamics. This formalism represents an interesting tool for studying physical systems that show an explicit scale separation. Third, a weakly nonlinear model was developed to describe the magneto-Rayleigh-Taylor instability. This instability is of paramount importance to understand as it can reduce the performance of magnetic-inertial-fusion (MIF) platforms. The developed models captures the effects of harmonic generation and saturation of the linear growth of the instability. Finally, a framework was proposed for scaling magneto-inertial fusion (MIF) targets to larger pulsed-power drivers. From this framework, a set of scaling rules were derived that conserve the physical regimes of MIF systems when scaling up in peak current. By doing so, deleterious nonlinear processes that affect MIF performance may be kept at bay.

Monoclonal antibodies (mAbs) is the leading therapy for viral infections because it provides immediate protection and can be administered at higher levels than in a natural immune response. Finding mAbs that neutralize a broad spectrum of viral targets has proven difficult because many species and strains exist and blanket targeting is a slow and laborious process to experimentally screen 108 variants. A new method is needed to rapidly redesign mAbs for homologous targets. This project speeds up redesign using structure-based computational design to reduce the mAbs search space to a manageable level and screen mutants at a much higher rate than in experiments. Computation will also provide critical knowledge about the fundamental interactions. The project will adapt S230, a human antibody that neutralizes SARS-CoV, to neutralize SARS-COV-2.

In this project, we investigate the use of neural networks for the prediction of molecular properties, namely the interatomic potential. We use the machine learning package Tensorflow to build a variety of neural networks and compare performance with a popular Fortran package - Atomic Energy Networks (aenet). There are two primary goals for this work: 1) use the wide availability of different optimization techniques in Tensorflow to outperform aenet and 2) use new descriptors that can outperform Behler descriptors.

This project evaluates natural product molecules with the potential to prevent 2019- nCOV infection. The molecules theoretically work by blocking the ACE2 protein active site in human airways. Previous work focused on modeling candidate natural compounds, but this work examined baicalin, hesperetin, glycyrrhizin, and scutellarin in experimental in vitro studies, which included recombinant protein inhibition assays, cell culture virus inhibition assays, and cytotoxicity assays. The project delivered selectivity indices (ratio that measures the window between cytotoxicity and antiviral activity) of the four natural compounds that will help guide the direction of SARS-CoV-2 therapeutic development.

One challenge of using compartmental SEIR models for public health planning is the difficulty in manually tuning parameters to capture behavior reflected in the real-world data. This team conducted initial, exploratory analysis of a novel technique to use physics-informed machine learning tools to rapidly develop data-driven models for physical systems. This machine learning approach may be used to perform data assimilation of compartment models which account for unknown interactions between geospatial domains (i.e. diffusion processes coupling across neighborhoods/counties/states/etc.). Results presented here are early, proof-of-concept ideas that demonstrate initial success in using a physically informed neural network (PINN) model to assimilate data in a compartmental epidemiology model. The results demonstrate initial success and warrant further research and development.

Penetrating X-rays are one of the most effective tools for diagnosing high energy density experiments, whether through radiographic imaging or X-ray diffraction. To expand the X-ray diagnostic capabilities at the 26-MA Z Pulsed Power Facility, we have developed a new diagnostic X-ray source called the inductively driven X-pinch (IDXP). This X-ray source is powered by a miniature transmission line that is inductively coupled to fringe magnetic fields in the final power feed. The transmission line redirects a small amount of Zs magnetic energy into a secondary cavity where 150+ kA of current is delivered to a hybrid X-pinch. In this report, we describe the multi-stage development of the IDXP concept through experiments both on Z and in a surrogate setup on the 1 MA Mykonos facility. Initial short-circuit experiments to verify power ow on Z are followed by short-circuit and X-ray source development experiments on Mykonos. The creation of a radiography-quality X-pinch hot spot is verified through a combination of X-ray diode traces, laser shadowgraphy, and source radiography. The success of the IDXP experiments on Mykonos has resulted in the design and fabrication of an IDXP for an upcoming Z experiment that will be the first-ever X-pinch fielded on Z. We have also pursued the development of two additional technologies. First, the extended convolute post (XCP) has been developed as an alternate method for powering diagnostic X-pinches on Z. This concept, which directly couples the current owing in one of the twelve Z convolute posts to an X-pinch, greatly increases the amount of available current relative to an IDXP (900 kA versus 150 kA). Initial short-circuit XCP experiments have demonstrated the efficacy of power ow in this geometry. The second technology pursued here is the inductively driven transmission line (IDTL) current monitor. These low-current IDTLs seek to measure the current in the final power feed with high fidelity. After three generations of development, IDTL current monitors frequently return cleaner current measurements than the standard B-dot sensors that are fielded on Z. This is especially true on high-inductance experiments where the harshest conditions are created in the nal power feed.

The CTH multiphysics hydrocode, which is used for a wide range of important calculations, has undertaken in recent years to overhaul its software quality and testing processes. A key part of this effort entailed building a new, robust V&V test suite made up of traditional hydrocode verification problems, such as those listed in the ASC Tri-Lab Test Suite and the Enhanced Tri-Lab Test Suite, as well as validation problems for some of CTHs most frequently used equations of state, materials models, and other key capabilities. Substantial progress towards this goal was made in FY19. In FY20, this test suite has been expanded to include verification and validation tests of the Sesame and JWL equation of state models as well as the Mader verification problem from the Tri-Lab Test Suite and the Blake verification problem - a linear elastic analog to the Hunter problem from the Enhanced Tri-Lab Test Suite. This report documents CTH performance on the new test suite problems. Verification test results are compared to analytic solutions and, for most tests, convergence results are presented. Validation test results are compared to experimental data and mesh refinement studies are included. CTH performs well overall on the new test problems. Convergence rates for the Blake and Mader problems are comparable to those for similar ASC codes. The JWL and Sesame verification tests show good agreement with analytic solutions. Likewise, CTH simulation results show good agreement with experimental validation data for the Sesame and JWL equations of state for the materials tested. Future V&V work will focus on adding tests for other key capabilities like fracture and high explosive models.

In this paper, we introduce a novel experimental platform for the study of the Richtmyer-Meshkov instability in a cylindrically converging geometry using a magnetically driven cylindrical piston. Magnetically driven solid liner implosions are used to launch a shock into a liquid deuterium working fluid and, ultimately, into an on-axis rod with a pre-imposed perturbation. The shock front trajectory is tracked through the working fluid and up to the point of impacting the rod through the use of on axis photonic Doppler velocimetry. This configuration allows for precise characterization of the shock state as it impacts the perturbed rod interface. Monochromatic x-ray radiography is used to measure the post-shock interface evolution and rod density profile. The ALEGRA MHD model is used to simulate the dynamics of the experiment in one dimension. We show that late in time the perturbation growth becomes non-linear as evidenced by the observation of high-order harmonics, up to n = 5. Two dimensional simulations performed using a combination of the GORGON MHD code and the xRAGE radiation hydrodynamics code suggest that the late time non-linear growth is modified by convergence effects as the bubbles and spikes experience differences in the pressure of the background flow.

The present work investigates the complex phenomena associated with pressure/high temperature dodecane injection for the Engine Combustion Network (ECN) Spray-A case, employing more elaborate thermodynamic closures, to avoid well known deficiencies concerning density and speed of sound prediction using traditional cubic models. A tabulated thermodynamic approach is proposed here, based on log10(p)-T tables, providing very high accuracy across a large range of pressures, spanning from 0 to 2500 bar, with only a small number of interpolation points. The tabulation approach is directly extensible to any thermodynamic model, existing or to be developed in the future. Here NIST REFPROP properties are used, combined with PC-SAFT Vapor-Liquid-Equilibrium to identify the liquid in mixtures penetration, hence avoiding the use of an arbitrary threshold for mass fraction. Identified liquid and vapour penetration are compared against experimental data from the ECN database showing a good agreement, within approximately 3–8% for axial penetration of liquid, 2% for vapor axial penetration and within experimental uncertainty for radial distribution of mass fraction. Analysis of the vortex evolution indicates that driving mechanisms behind the jet break-up are vortex tilting/stretching, then baroclinic torque, leading to Rayleigh-Taylor instabilities, closely followed by vortex dilation and finally viscous effects.

A vital part of the licensing process for advanced (non-LWR) nuclear reactor developers in the United States is the assessment of the reactor’s source term, i.e., the potential release of radionuclides from the reactor system to the environment during normal operations and accident sequences. In comparison to source term assessments which follow a bounding approach with conservative assumptions, a mechanistic approach to modeling radionuclide transport, which realistically accounts for transport and retention phenomena, is expected to be used for advanced reactor systems. As the designs of advanced reactors increase in maturity and progress towards licensing, there is a need to advance modeling and simulation capabilities in analyzing the mechanistic source term (MST) of a prospective reactor concept. In the present work, a survey is provided of existing computational capabilities for the modeling of advanced reactors MSTs. The following reactors are considered: high temperature gas reactors (HTGR); molten salt reactors (MSR) which include salt-fueled reactors and fluoride salt-cooled high temperature reactors (FHR); and sodium- and lead-cooled fast reactors (SFR, LFR). A review of relevant codes which may be useful in providing information to MST analyses is also completed, including codes that have been used for source term analyses of LWRs, as well as those being developed for other aspects of advanced reactor system modeling such as reactor physics, thermal hydraulics, and chemistry. A discussion of MST modeling capabilities for each reactor type is provided with additional focus on important phenomena and functional requirements. Additionally, a comprehensive survey is provided of tools for consequence modeling such as atmospheric transport and dispersion (ATD).

Abstract not provided.

It is widely accepted that the breakdown of Si0₂ gate dielectrics is caused by the buildup of stress-induced defects over time. Although several physical mechanisms have been proposed for the generation of these defects, very little direct experimental evidence as to the chemical and physical identity of these defects has been generated in the literature thus far. Here, we present electrically detected magnetic resonance (EDMR) measurements obtained via spin-dependent recombination currents at the interface of high-field stressed Si/Si0₂ metal-oxide-semiconductor field effect transistors (MOSFETs).

Abstract not provided.

Graph analysis in large integrated circuit (IC) designs is an essential tool for verifying design logic and timing via dynamic timing analysis (DTA). IC designs resemble graphs with each logic gate as a vertex and the conductive connections between gates as edges. Using DTA digital statistical correlations, graph condensation, and graph partitioning, it is possible to identify high-entropy component centers and paths within an IC design. Identification of high-entropy component centers (HECC) enables focused DTA, effectively lowering the computational complexity of DTA on large integrated circuit graphs. In this paper, a devised methodology termed IC layout subgraph component center identification (CCI) is described. CCI lowers DTA computational complexity by condensing IC graphs into reduced subgraphs in which dominant logic functions are verified.

The understanding of subsurface events that cannot be directly observed is dependent on the ability to relate surface-based observations to subsurface processes. This is particularly important for nuclear explosion monitoring, as any future clandestine tests will likely be underground. We collected ground-based lidar and optical imagery using remote, very-low-altitude unmanned aerial system platforms, before and after several underground high explosive experiments. For the lidar collections, we used a terrestrial lidar scanner to obtain high-resolution point clouds and create digital elevation models (DEMs). For the imagery collections, we used structure-from-motion photogrammetry techniques and a dense grid of surveyed ground control points to create high-resolution DEMs. Comparisons between the pre- and post-experiment DEMs indicate changes in surface topography that vary between explosive experiments with varying yield and depth parameters. Our work shows that the relationship between explosive yield and the extent of observable surface change differs from the standard scaled-depth-of-burial model. This suggests that the surface morphological change from underground high explosive experiments can help constrain the experiments' yield and depth, and may impact how such activities are monitored and verified.

While lattice metamaterials can achieve exceptional energy absorption by tailoring periodically distributed heterogeneous unit cells, relatively little focus has been placed on engineering heterogeneity above the unit-cell level. In this work, the energy-absorption performance of lattice metamaterials with a heterogeneous spatial layout of different unit cell architectures was studied. Such multi-morphology lattices can harness the distinct mechanical properties of different unit cells while being composed out of a single base material. A rational design approach was developed to explore the design space of these lattices, inspiring a non-intuitive design which was evaluated alongside designs based on mixture rules. Fabrication was carried out using two different base materials: 316L stainless steel and Vero White photopolymer. Results show that multi-morphology lattices can be used to achieve higher specific energy absorption than homogeneous lattice metamaterials. Additionally, it is shown that a rational design approach can inspire multi-morphology lattices which exceed rule-of-mixtures expectations.

Controlling the formation of nanosized branched nanoparticles with high uniformity is one of the major challenges in synthesizing nanocatalysts with improved activity and stability. Using a cubic-core hexagonal-branch mechanism to form highly monodisperse branched nanoparticles, we vary the length of the nickel branches. Lengthening the nickel branches, with their high coverage of active facets, is shown to improve activity for electrocatalytic oxidation of 5-hydroxymethylfurfural (HMF), as an example for biomass conversion.

Integration of nanoscale photonic and plasmonic components on Si substrates is a critical step toward Si-based integrated nanophotonic devices. In this work, a set of unique complex 3D metamaterials with intercalated nanolayered and nanopillar structures with tunable plasmonic and optical properties on Si substrates is designed. More specifically, the 3D metamaterials combine metal (Au) nanopillars and alternating metal-nitride (Au-TiN and Au-TaN) nanolayers, epitaxially grown on Si substrates. The ultrafine Au nanopillars (d ≈ 3 nm) continuously grow throughout all the nanolayers with high epitaxial quality. Novel optical properties, such as highly anisotropic optical property, high absorbance covering the entire visible spectrum regime, and hyperbolic property in the visible regime, are demonstrated. Furthermore, a waveguide based on a silicon nitride (Si3N4) ridge with a multilayer structure is successfully fabricated. The demonstration of 3D nanoscale metamaterial design integrated on Si opens up a new route toward tunable metamaterials nanostructure designs with versatile material selection for various optical components in Si integrated photonics.

In a previous work, embedded ensemble propagation was proposed to improve the efficiency of sampling-based uncertainty quantification methods of computational models on emerging computational architectures. It consists of simultaneously evaluating the model for a subset of samples together, instead of evaluating them individually. A first approach introduced to solve parametric linear systems with ensemble propagation is ensemble reduction. In Krylov methods for example, this reduction consists in coupling the samples together using an inner product that sums the sample contributions. Ensemble reduction has the advantages of being able to use optimized implementations of BLAS functions and having a stopping criterion which involves only one scalar. However, the reduction potentially decreases the rate of convergence due to the gathering of the spectra of the samples. In this paper, we investigate a second approach: ensemble propagation without ensemble reduction in the case of GMRES. This second approach solves each sample simultaneously but independently to improve the convergence compared to ensemble reduction. This raises two new issues which are solved in this paper: the fact that optimized implementations of BLAS functions cannot be used anymore and that ensemble divergence, whereby individual samples within an ensemble must follow different code execution paths, can occur. We tackle those issues by implementing a high-performing ensemble GEMV and by using masks. The proposed ensemble GEMV leads to a similar cost per GMRES iteration for both approaches, i.e. with and without reduction. For illustration, we study the performances of the new linear solver in the context of a mesh tying problem. This example demonstrates improved ensemble propagation speed-up without reduction.

Abstract not provided.

Abstract not provided.

The final review for the FY20 Advanced Simulation and Computing (ASC) Integrated Codes (IC) L2 Milestone #7181 was conducted on August 31, 2020 at Sandia National Laboratories in Albuquerque, New Mexico. The review panel unanimously agreed that the milestone has been successfully completed. Roshan Quadros (1543) led the milestone team and various members from the team presented the results. The review panel was comprised of staff from Sandia National Laboratories Albuquerque and California that are involved with computational engineering modeling and analysis. The panel consisted of experts in the fields of solid modeling, discretization, meshing, simulation workflows, and computational analysis including personnel Brett Clark (1543, Chair); Jay Foulk (8363); Jackie Moore (1553); Ron Kensek (1341); Ed Hoffman (8753); Dan Ibanez (1443). The presentation documented the technical approach of the team and summarized the results with sufficient detail to demonstrate both the value and the completion of the milestone. A separate SAND report was also generated with more detail to supplement the presentation. The purpose of the milestone was to advance capabilities for automatically finding, displaying, and resolving geometric overlaps in CAD models.

Characterizing ionization wave propagation in low temperature plasma jets is critical to predicting production of reactive species and plasma-surface interactions for biomedical applications and surface functionalization. In this paper, results from optical emission and laser induced fluorescence measurements of the ionization wave in a He plasma jet operating in a controlled gas environment are discussed and used for comparison with numerical modeling. The ionization wave was observed using ICCD (Intensified Charge Coupled Device) imaging and characterized by time and spatially resolved electron density measurements using laser-collision-induced fluorescence. The plasma jet was initially characterized using pure He (nominally at 200 Torr), while varying pressure and voltage. When operating in pure He, the ionization wave broadly expands exiting the plasma tube. Increasing the operating pressure reduces the speed and isotropic expansion of the ionization wave. The jet operated with a humid He shroud was also studied. The humid He shroud results in the electron density increasing and having an annular profile due to the lower ionization potential of H2O compared to He and localized photoionization in the mixing region. Numerical modeling highlighted the importance of resonance radiation emitted by excited states of He, photoelectron emission from the quartz tube, and the kinetic behavior of the electrons produced by photoionization ahead of the ionization front.

Hydrogen lithography has been used to template phosphine-based surface chemistry to fabricate atomic-scale devices, a process we abbreviate as atomic precision advanced manufacturing (APAM). Here, we use mid-infrared variable angle spectroscopic ellipsometry (IR-VASE) to characterize single-nanometer thickness phosphorus dopant layers (δ-layers) in silicon made using APAM compatible processes. A large Drude response is directly attributable to the δ-layer and can be used for nondestructive monitoring of the condition of the APAM layer when integrating additional processing steps. The carrier density and mobility extracted from our room temperature IR-VASE measurements are consistent with cryogenic magneto-transport measurements, showing that APAM δ-layers function at room temperature. Finally, the permittivity extracted from these measurements shows that the doping in the APAM δ-layers is so large that their low-frequency in-plane response is reminiscent of a silicide. However, there is no indication of a plasma resonance, likely due to reduced dimensionality and/or low scattering lifetime.

The Pāpaku Fault Zone, drilled at International Ocean Discovery Program (IODP) Site U1518, is an active splay fault in the frontal accretionary wedge of the Hikurangi Margin. In logging-while-drilling data, the 33-m-thick fault zone exhibits mixed modes of deformation associated with a trend of downward decreasing density, P-wave velocity, and resistivity. Methane hydrate is observed from ~30 to 585 m below seafloor (mbsf), including within and surrounding the fault zone. Hydrate accumulations are vertically discontinuous and occur throughout the entire logged section at low to moderate saturation in silty and sandy centimeter-thick layers. We argue that the hydrate distribution implies that the methane is not sourced from fluid flow along the fault but instead by local diffusion. This, combined with geophysical observations and geochemical measurements from Site U1518, suggests that the fault is not a focused migration pathway for deeply sourced fluids and that the near-seafloor Pāpaku Fault Zone has little to no active fluid flow.

This study demonstrates novel in situ transmission electron microscopy-based microscale single grain boundary Coble creep experiments used to grow nanowires through a solid-state process in cubic ZrO₂ between ≈ 1200 °C and ≈ 2100 °C. Experiments indicate Coble creep drives the formation of nanowires from asperity contacts during tensile displacement, which is confirmed by phase field simulations. The experiments also facilitate efficient measurement of grain boundary diffusivity and surface diffusivity. 10 mol% Sc₂O₃ doped ZrO₂ is found to have a cation grain boundary diffusivity of $D_{gb} = (0.056 ± 0.05)exp (\frac{-380,000±41,000}{RT})m^2 s^{-1}$, and $D_s = (0.10 ± 0.27)exp(\frac{-380,000 ± 28,000}{RT}) m^2 s^{-1}$.

This work uses a combination of stress dependent single grain boundary Coble creep and zero-creep experiments to measure interfacial energies, along with grain boundary point defect formation and migration volumes in cubic ZrO₂. These data, along with interfacial diffusivities measured in a companion paper are then applied to analyzing two-particle sintering. The analysis presented here indicates that the large activation volume, $v*=v^f+v^m$primarily derives from a large migration volume and suggests that the grain boundary rate limiting defects are delocalized, possibly due to electrostatic interactions between charge compensating defects. The discrete nature of the sintering and creep process observed in the small-scale experiments supports the hypothesis that grain boundary dislocations serve as sources and sinks for grain boundary point defects and facilitate strain during sintering and Coble creep. Model two-particle sintering experiments demonstrate that initial-stage densification follows interface reaction rate-limited kinetics.

Polymer-tethered nanoparticles (NPs) are commonly added to a polymer matrix to improve the material properties. Critical to the fabrication and processing of such composites is the mobility of the tethered NPs. Here, we study the motion of tethered NPs in unentangled polymer melts using molecular dynamics simulations, which offer a precise control of the grafted chain length Ng and the number z of grafted chains per particle. As Ng increases, there is a crossover from particle-dominated to tethered-chain-dominated terminal diffusion of NPs with the same z. The mean squared displacement of loosely tethered NPs in the case of tethered-chain-dominated terminal diffusion exhibits two subdiffusive regimes at intermediate time scales for small z. The first one at shorter time scales arises from the dynamical coupling of the particle and matrix chains, while the one at longer time scales is due to the participation of the particle in the dynamics of the tethered chains. The friction of loosely grafted chains in unentangled melts scales linearly with the total number of monomers in the chains, as the friction of individual monomers is additive in the absence of hydrodynamic coupling. As more chains are grafted to a particle, hydrodynamic interactions between grafted chains emerge. As a result, there is a nondraining layer of hydrodynamically coupled chain segments surrounding the bare particle. Outside the nondraining layer is a free-draining layer of grafted chain segments with no hydrodynamic coupling. The boundary of the two layers is the stick surface where the shear stress due to the relative melt flow is balanced by the friction between the grafted and melt chains in the interpenetration layer. The stick surface is located further away from the bare surface of the particle with higher grafting density.

The lower limit of metal hydride nanoconfinement is demonstrated through the coordination of a molecular hydride species to binding sites inside the pores of a metal-organic framework (MOF). Magnesium borohydride, which has a high hydrogen capacity, is incorporated into the pores of UiO-67bpy (Zr6O4(OH)4(bpydc)6 with bpydc2- = 2,2′-bipyridine-5,5′-dicarboxylate) by solvent impregnation. The MOF retained its long-range order, and transmission electron microscopy and elemental mapping confirmed the retention of the crystal morphology and revealed a homogeneous distribution of the hydride within the MOF host. Notably, the B-, N-, and Mg-edge XAS data confirm the coordination of Mg(II) to the N atoms of the chelating bipyridine groups. In situ 11B MAS NMR studies helped elucidate the reaction mechanism and revealed that complete hydrogen release from Mg(BH4)2 occurs as low as 200 °C. Sieverts and thermogravimetric measurements indicate an increase in the rate of hydrogen release, with the onset of hydrogen desorption as low as 120 °C, which is approximately 150 °C lower than that of the bulk material. Furthermore, density functional theory calculations support the improved dehydrogenation properties and confirm the drastically lower activation energy for B-H bond dissociation.

Abstract not provided.

Residual stress measurements using neutron diffraction and the contour method were performed on a valve housing made from 316 L stainless steel powder with intricate three-dimensional internal features using laser powder-bed fusion additive manufacturing. The measurements captured the evolution of the residual stress fields from a state where the valve housing was attached to the base plate to a state where the housing was cut free from the base plate. Making use of this cut, thus making it a non-destructive measurement in this application, the contour method mapped the residual stress component normal to the cut plane (this stress field is completely relieved by cutting) over the whole cut plane, as well as the change in all stresses in the entire housing due to the cut. The non-destructive nature of the neutron diffraction measurements enabled measurements of residual stress at various points in the build prior to cutting and again after cutting. Good agreement was observed between the two measurement techniques, which showed large, tensile build-direction residual stresses in the outer regions of the housing. The contour results showed large changes in multiple stress components upon removal of the build from the base plate in two distinct regions: near the plane where the build was cut free from the base plate and near the internal features that act as stress concentrators. These observations should be useful in understanding the driving mechanisms for builds cracking near the base plate and to identify regions of concern for structural integrity. Neutron diffraction measurements were also used to show the shear stresses near the base plate were significantly lower than normal stresses, an important assumption for the contour method because of the asymmetric cut.

In this paper, the first flamelet analysis is conducted of a highly resolved DNS of a multi-injection flame with both auto-ignition and ignition induced by flame-flame interaction. A novel method is proposed to identify the different combustion modes of ignition processes using generalized flamelet equations. The state-of-the-art DNS database generated by Rieth et al. (US National Combustion Meeting, 2019) for a multi-injection flame in a Diesel engine environment is investigated. Three-dimensional flamelets are extracted from the DNS at different time instants with a focus on auto-ignition and interaction-ignition processes. The influences of mixture field interactions and the scalar dissipation rate on the ignition process are investigated by varying the species composition boundary conditions of the transient flamelet equations. Budget analyses of the generalized flamelet equations show that the transport along the mixture fraction iso-surface is insignificant during the auto-ignition process, but becomes important when interaction-ignition occurs, which is further confirmed through a flamelet regime classification method.

Plasma etching of p-type GaN creates n-type nitrogen vacancy (VN) defects at the etched surface, which can be detrimental to device performance. In mesa isolated diodes, etch damage on the sidewalls degrades the ideality factor and leakage current. A treatment was developed to recover both the ideality factor and leakage current, which uses UV/O3 treatment to oxidize the damaged layers followed by HF etching to remove them. The temperature dependent I-V measurement shows that the reverse leakage transport mechanism is dominated by Poole-Frenkel emission at room temperature through the etch-induced VN defect. Depth resolved cathodoluminescence confirms that the damage is limited to first several nanometers and is consistent with the VN defect.

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including graph mining, recommender systems and natural language processing. Due to the compute-intensive nature of applications that must perform repeated NMF, several parallel implementations have been developed. However, existing parallel NMF algorithms have not addressed data locality optimizations, which are critical for high performance since data movement costs greatly exceed the cost of arithmetic/logic operations on current computer systems. In this paper, we present a novel optimization method for parallel NMF algorithm based on the HALS (Hierarchical Alternating Least Squares) scheme that incorporates algorithmic transformations to enhance data locality. Efficient realizations of the algorithm on multi-core CPUs and GPUs are developed, demonstrating a new Accelerated Locality-Optimized NMF (ALO-NMF) that obtains up to 2.29x lower data movement cost and up to 4.45x speedup over existing state-of-the-art parallel NMF algorithms.

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including graph mining, recommender systems and natural language processing. Due to the compute-intensive nature of applications that must perform repeated NMF, several parallel implementations have been developed. However, existing parallel NMF algorithms have not addressed data locality optimizations, which are critical for high performance since data movement costs greatly exceed the cost of arithmetic/logic operations on current computer systems. In this paper, we present a novel optimization method for parallel NMF algorithm based on the HALS (Hierarchical Alternating Least Squares) scheme that incorporates algorithmic transformations to enhance data locality. Efficient realizations of the algorithm on multi-core CPUs and GPUs are developed, demonstrating a new Accelerated Locality-Optimized NMF (ALO-NMF) that obtains up to 2.29x lower data movement cost and up to 4.45x speedup over existing state-of-the-art parallel NMF algorithms.

Non-negative Matrix Factorization (NMF) is a key kernel for unsupervised dimension reduction used in a wide range of applications, including graph mining, recommender systems and natural language processing. Due to the compute-intensive nature of applications that must perform repeated NMF, several parallel implementations have been developed. However, existing parallel NMF algorithms have not addressed data locality optimizations, which are critical for high performance since data movement costs greatly exceed the cost of arithmetic/logic operations on current computer systems. In this paper, we present a novel optimization method for parallel NMF algorithm based on the HALS (Hierarchical Alternating Least Squares) scheme that incorporates algorithmic transformations to enhance data locality. Efficient realizations of the algorithm on multi-core CPUs and GPUs are developed, demonstrating a new Accelerated Locality-Optimized NMF (ALO-NMF) that obtains up to 2.29x lower data movement cost and up to 4.45x speedup over existing state-of-the-art parallel NMF algorithms.

We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction. Practical materials' design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum-niobium-titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations.

Redox flow batteries are attractive technologies for grid energy storage since they use solutions of redox-active molecules that enable a superior scalability and the decoupling of power and energy density. However, the reaction mechanisms of the redox active components at RFB electrodes are complex, and there is currently a pressing need to understand how interfacial processes impact the kinetics and operational reversibility of RFB systems. Here, we developed a combined electrochemical imaging methodology rooted in scanning electrochemical microscopy (SECM) and atomic force microscopy (AFM) for exploring the impact of electrode structure and conditioning on the electron transfer properties of model redox-active dialkoxybenzene derivatives, 2,5-di-tert-butyl-1,4-bis(2-methoxyethoxy)benzene (C1) and 2,3-dimethyl-1,4-dialkoxybenzene (C7). Using AFM and secondary-ion mass spectrometry (SIMS), we observed the formation of interfacial films with distinct mechanical properties compared to those of cleaved graphitic surfaces, and exclusively during reduction of electrogenerated radical cations. These films had an impact on the median rate and distribution of the electron transfer rate constant at the basal plane of multilayer and single layer graphene electrodes, displaying kinetically-limited values that did not yield the activation expected per the Butler-Volmer model with a transfer coefficient ∼0.5. These changes were dependent on redoxmer structure: SECM showed strong attenuation of C7 kinetics by a surface layer on MLG and SLG, while C1 kinetics were only affected by SLG. SECM and AFM results together show that these limiting films operate exclusively on the basal plane of graphite, with the edge plane showing a relative insensitivity to cycling and operation potential. This integrated electrochemical imaging methodology creates new opportunities to understand the unique role of interfacial processes on the heterogeneous reactivity of redoxmers at electrodes for RFBs, with a future role in elucidating phenomena at high active concentrations and spatiotemporal variations in electrode dynamics. This journal is

Calibrated values of many devices exhibit predictable drift over time. To provide an uncertainty statement valid over the entire calibration interval, one must account for drift. In this article, a method of accounting for drift is proposed based on guidance in the Guide to Expression of Uncertainty in Measurement. An additional uncertainty term is computed using a linear regression of historical measurement data, which is included along with the time-of-test uncertainty. This method is evaluated by analyzing its average out-of-tolerance (OOT) rate using a Monte Carlo simulation, which results in the desired 5% average OOT rate when the total uncertainty is expanded to a 95% confidence interval.

Quantum well intersubband polaritons are traditionally studied in large scale ensembles, over many wavelengths in size.In this presentation, we demonstrate that it is possible to detect and investigate intersubband polaritons in a single sub-wavelength nanoantenna in the IR frequency range. We observe polariton formation using a scattering-type near-fieldmicroscope and nano-FTIR spectroscopy. In this work, we will discuss near-field spectroscopic signatures of plasmonic antennae withand without coupling to the intersubband transition in quantum wells located underneath the antenna. Evanescent fieldamplitude spectra recorded on the antenna surface show a mode anti-crossing behavior in the strong coupling case. Wealso observe a corresponding strong-coupling signature in the phase of the detected field. We anticipate that this near-fieldapproach will enable explorations of strong and ultrastrong light-matter coupling in the single nanoantenna regime,including investigations of the elusive effect of ISB polariton condensation.

MCB11H12 (M: Li, Na) dodecahydro-monocarba-closo-dodecaborate salt compounds are known to have stellar superionic Li+ and Na+ conductivities in their high-temperature disordered phases, making them potentially appealing electrolytes in all-solid-state batteries. Nonetheless, it is of keen interest to search for other related materials with similar conductivities while at the same time exhibiting even lower (more device-relevant) disordering temperatures, a key challenge for this class of materials. With this in mind, the unknown structural and dynamical properties of the heavier KCB11H12 congener were investigated in detail by X-ray powder diffraction, differential scanning calorimetry, neutron vibrational spectroscopy, nuclear magnetic resonance, quasielastic neutron scattering, and AC impedance measurements. This salt indeed undergoes an entropy-driven, reversible, order-disorder transformation and with a lower onset temperature (348 K upon heating and 340 K upon cooling) in comparison to the lighter LiCB11H12 and NaCB11H12 analogues. The K+ cations in both the low-T ordered monoclinic (P21/c) and high-T disordered cubic (Fm3¯ m) structures occupy octahedral interstices formed by CB11H12- anions. In the low-T structure, the anions orient themselves so as to avoid close proximity between their highly electropositive C-H vertices and the neighboring K+ cations. In the high-T structure, the anions are orientationally disordered, although to best avoid the K+ cations, the anions likely orient themselves so that their C-H axes are aligned in one of eight possible directions along the body diagonals of the cubic unit cell. Across the transition, anion reorientational jump rates change from 6.2 × 106 s-1 in the low-T phase (332 K) to 2.6 × 1010 s-1 in the high-T phase (341 K). In tandem, K+ conductivity increases by about 30-fold across the transition, yielding a high-T phase value of 3.2 × 10-4 S cm-1 at 361 K. However, this is still about 1 to 2 orders of magnitude lower than that observed for LiCB11H12 and NaCB11H12, suggesting that the relatively larger K+ cation is much more sterically hindered than Li+ and Na+ from diffusing through the anion lattice via the network of smaller interstitial sites.

Sandia National Laboratories has built and successfully tested a dynamic simulation technoeconomic model of the Palo Verde Generating Station that is now being updated to help other US power plants improve operations. Palo Verde, located west of Phoenix, Arizona, is the largest electricity generator in the US at 4 GW. Palo Verde uses — 60 million gallons per day of treated wastewater from Phoenix to cool reactors, and disposes of blowdown in evaporation ponds. The model built for Palo Verde numerically evaluates the economic impact of changing, for example, alternative cooling technologies, water usage and treatment, and influent water chemistry, and is based on detailed accounting of mass, energy, and cash flows.

Metamaterials are artificial optical structures that allow control of light in ways not found in, or offered by, naturally occurring materials. Sandia's Multiscale Inverse Rapid Group-theory for Engineered-metamaterials (MIRaGE) software, which won an R&D100 award in 2019, allows researchers to deterministically design and produce metamaterials with unique characteristics. MIRaGE also provides powerful autonomous optimization techniques for real-world performance in a rigorous, robust, and accurate manner.

Infrared detectors using monolithically integrated doped semiconductor "designer metals"are proposed and experimentally demonstrated. We leverage the "designer metal"groundplanes to form resonant cavities with enhanced absorption tuned across the long-wave infrared (LWIR). Detectors are designed with two target absorption enhancement wavelengths: 8 and 10 μm. The core of our detectors are quantum-engineered LWIR type-II superlattice p-i-n detectors with total thicknesses of only 1.42 and 1.80 μm for the 8 and 10 μm absorption enhancement devices, respectively. Our 8 and 10 μm structures show peak external quantum efficiencies of 45 and 27%, which are 4.5× and 2.7× enhanced, respectively, compared to control structures. We demonstrate the clear advantages of this detector architecture, both in terms of ease of growth/fabrication and enhanced device performance. The proposed architecture is absorber- A nd device-structure agnostic, much thinner than state-of-the-art LWIR T2SLs, and offers the opportunity for the integration of low dark current LWIR detector architectures for significant enhancement of IR detectivity.

Here, photodissociation of pyruvic acid (PA) was studied in the gas-phase at 193 nm using two complementary techniques. The time-sliced velocity map imaging arrangement was used to determine kinetic energy release distributions of fragments and estimate dissociation timescales. The multiplexed photoionization mass spectrometer setup was used to identify and quantify photoproducts, including isomers and free radicals, by their mass-to-charge ratios, photoionization spectra, and kinetic time profiles. Using these two techniques, it is possible to observe the major dissociation products of PA photodissociation: CO₂, CO, H, OH, HCO, CH₂CO, CH₃CO, and CH₃. Acetaldehyde and vinyl alcohol are minor primary photoproducts at 193 nm, but products that are known to arise from their unimolecular dissociation, such as HCO, H₂CO, and CH₄, are identified and quantified. A multivariate analysis that takes into account the yields of the observed products and assumes a set of feasible primary dissociation reactions provides a reasonable description of the photoinitiated chemistry of PA despite the necessary simplifications caused by the complexity of the dissociation. These experiments offer the first comprehensive description of the dissociation pathways of PA initiated on the S₃ excited state. Most of the observed products and yields are rationalized on the basis of three reaction mechanisms: (i) decarboxylation terminating in CO₂ + other primary products (~50%); (ii) Norrish type I dissociation typical of carbonyls (~30%); and (iii) O—H and C—H bond fission reactions generating the H atom (~10%). The analysis shows that most of the dissociation reactions create more than two products. This observation is not surprising considering the high excitation energy (~51 800 cm^–1) and fairly low energy required for dissociation of PA. We find that two-body fragmentation processes yielding CO₂ are minor, and the expected, unstable primary co-fragment, methylhydroxycarbene, is not observed because it probably undergoes fast secondary dissociation and/or isomerization. Norrish type I dissociation pathways generate OH and only small yields of CH₃CO and HOCO, which have low dissociation energies and further decompose via three-body fragmentation processes. Experiments with d₁-PA (CH₃COCOOD) support the interpretations. The dissociation on S₃ is fast, as indicated by the products’ recoil angular anisotropy, but the roles of internal conversion and intersystem crossing to lower states are yet to be determined.

Sparse triangular solver is an important kernel in many computational applications. However, a fast, parallel, sparse triangular solver on a manycore architecture such as GPU has been an open issue in the field for several years. In this paper, we develop a sparse triangular solver that takes advantage of the supernodal structures of the triangular matrices that come from the direct factorization of a sparse matrix. We implemented our solver using Kokkos and Kokkos Kernels such that our solver is portable to different manycore architectures. This has the additional benefit of allowing our triangular solver to use the team-level kernels and take advantage of the hierarchical parallelism available on the GPU. We compare the effects of different scheduling schemes on the performance and also investigate an algorithmic variant called the partitioned inverse. Our performance results on an NVIDIA V100 or P100 GPU demonstrate that our implementation can be 12.4 × or 19.5 × faster than the vendor optimized implementation in NVIDIA's CuSPARSE library.

Tempered fractional operators provide an improved predictive capability for modeling anomalous effects that cannot be captured by standard partial differential equations. These effects include subdiffusion and superdiffusion (i.e. the mean square displacement in a diffusion process is proportional to a fractional power of the time), that often occur in, e.g., geoscience and hydrology. We analyze tempered fractional operators within the nonlocal vector calculus framework in order to assimilate them to the rigorous mathematical structure developed for nonlocal models. First, we show they are special instances of generalized nonlocal operators by means of a proper choice of the nonlocal kernel. Then, we present a plan for showing tempered fractional operators are equivalent to truncated fractional operators. These truncated operators are useful because they are less computationally intensive than the tempered operators.

This work explores the development of a heterogeneous nanostructured material through leveraging abnormal recrystallization, which is a prominent phenomenon in coarse-grained Ni-based superalloys. Through synthesis of a sputtered Inconel 725 film with a heterogeneous distribution of stored energy and subsequent aging treatments at 730°C, a unique combination of grain sizes and morphologies was observed throughout the thickness of the material. Three distinct domains are formed in the aged microstructure, where abnormally large grains are observed in-between a nanocrystalline and a nanotwinned region. In order to investigate the transitions towards a heterogeneous structure, crystallographic orientation and elemental mapping at interval aging times up to 8 h revealed the microstructural evolution and precipitation behavior. From the experimental observations and the detailed analysis of this study, the current methodology can be utilized to further expand the design space of current heterogeneous nanostructured materials.

The tensile response of superelastic shape memory alloys (SMAs) has been widely studied, but detailed experimental studies under multi-axial loading are relatively rare. In Part I, we present the isothermal responses of commercially-available superelastic NiTi tubes for a series of proportional stretch-twist controlled histories, spanning pure tension to simple torsion to pure compression. These axial-shear responses are used to quantify the onset and saturation during forward (loading) and reverse (unloading) stress-induced transformations for the first time. Each of the four transformation surfaces is well-captured by a smooth (three-parameter) ellipse in both strain and stress space. A simple Gibbs free energy model is presented to show how the driving force for phase transformation is approximately constant across all proportional strain paths and how the stress and strain transformation surfaces are conjugate to one another. In addition, transformation kinetics and surface strain morphologies are characterized by stereo digital image correlation (DIC). Under extension at low amounts of twist, stress-induced transformation involves strain localization in helical bands that evolve into axial propagation of ring-like transformation fronts with fine criss-crossing fingers (similar to those seen by Q. P. Sun and co-workers in pure extension). However, at large amounts of twist, including simple torsion and pure torsion, we report a new transformation morphology, involving strain localization along nearly longitudinal bands in the tube. The sequel (Part II) will address the response to non-proportional stretch-twist paths. Together, these detailed multi-axial results advance the scientific understanding of superelasticity and inform efforts to develop high-fidelity SMA constitutive models and simulation tools.

Gallium nitride substrates grown by the hydride vapor phase epitaxy (HVPE) method using a patterned growth process have been characterized by synchrotron monochromatic beam X-ray topography in the grazing incidence geometry. Images reveal a starkly heterogeneous distribution of dislocations with areas as large as 0.3 mm2 containing threading dislocation densities below 103 cm−2 in between a grid of strain centers with higher threading dislocation densities (>104 cm−2). Basal plane dislocation densities in these areas are as low as 104 cm−2. By comparing the recorded images of dislocations with ray tracing simulations of expected dislocations in GaN, the Burgers vectors of the dislocations have been determined. The distribution of threading screw/mixed dislocations (TSDs/TMDs), threading edge dislocations (TEDs) and basal plane dislocations (BPDs) is discussed with implications for fabrication of power devices.

Two-dimensional electromagnetic (EM) particle-in-cell (PIC) simulations of a radial magnetically-insulated-transmission-line are presented and compared to the model of E. M. Waisman, M. P. Desjarlais, and M. E. Cuneo [Phys. Rev. Accel. Beams 22, 030402 (2019) in the “high-enhancement” (WDC-HE) limit. The simulations use quasi-equilibrium current and voltage values based on the Sandia National Laboratories Z accelerator, with prescribed injection of an electron sheath that gives electron density profiles qualitatively similar to those used in the WDC-HE model. We find that the WDC-HE model accurately predicts the quasiequilibrium ion current losses in the EM PIC simulations for a wide range of current and voltage values. For the case of two ion species where one is magnetically insulated by the ambient magnetic field and the other is not, the charge of the lighter insulated species in the anode-cathode gap can modify the electric field profile, reducing the ion current density enhancement for the heavier ion species. On the other hand, for multiple ion species, when the lighter ions are not magnetically insulated and are a significant fraction of the anode plasma, they dominate the current loss, producing loss currents which are a significant fraction of the lighter ion WDC values. The observation of this effect in the present work is new to the field and may significantly impact the analysis of ion current losses in the Z machine inner MITL and convolute.

The term “in situ processing” has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over 50 experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. Here, they discuss these axes, evaluate existing systems within the axes, and explore how currently used terms relate to the axes.

Efficient algorithms for the calculation of minimum energy paths of magnetic transitions are implemented within the geodesic nudged elastic band (GNEB) approach. While an objective function is not available for GNEB and a traditional line search can, therefore, not be performed, the use of limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) and conjugate gradient algorithms in conjunction with orthogonal spin optimization (OSO) approach is shown to greatly outperform the previously used velocity projection and dissipative Landau-Lifschitz dynamics optimization methods. The implementation makes use of energy weighted springs for the distribution of the discretization points along the path and this is found to improve performance significantly. The various methods are applied to several test problems using a Heisenberg-type Hamiltonian, extended in some cases to include Dzyaloshinskii-Moriya and exchange interactions beyond nearest neighbours. Minimum energy paths are found for magnetization reversals in a nano-island, collapse of skyrmions in two-dimensional layers and annihilation of a chiral bobber near the surface of a three-dimensional magnet. The LBFGS-OSO method is found to outperform the dynamics based approaches by up to a factor of 8 in some cases.

Radiation damage can cause significantly more surface damage in metallic nanostructures than bulk materials. Structural changes from displacement damage compromise the performance of nanostructures in radiation environments such as nuclear reactors and outer space, or used in radiation therapy for biomedical treatments. As such, it is important to develop strategies to prevent this from occurring if nanostructures are to be incorporated into these applications. In this work, in situ transmission electron microscope ion irradiation was used to investigate whether a metallic glass (MG) coating mitigates sputtering and morphological changes in metallic nanostructures. Dislocation-free Au nanocubes and Au nanocubes coated with a Ni–B MG were bombarded with 2.8 MeV Au⁴⁺ ions. The formation of internal defects in bare Au nanocubes was observed at a fluence of 7.5 × 10¹¹ ions/cm² (0.008 dpa), and morphological changes such as surface roughening, rounding of corners, and formation of nanofilaments began at 4 × 10¹² ions/cm² (0.04 dpa). In contrast, the Ni–B MG-coated Au nanocubes (Au@NiB) showed minimal morphological changes at a fluence of 1.9 × 10¹³ ions/cm² (0.2 dpa). Finally, the MG coating maintains its amorphous nature under all irradiation conditions investigated.

Macromolecules is an exceptional resource in the field of polymer science and now publishes more than 1000 original articles a year that set the standard for scientific rigor and creative insights. Over the years, these individual contributions have combined to build the foundation of polymer science, broadly and inclusively defined. In addition to the individual articles, many of which are being celebrated in this series of editorials, Macromolecules has published invaluable reviews and perspectives. These scholarly contributions integrate the insights and results from numerous sources into a unified whole and often recommend future directions for the field. Novices and experts alike benefit from these works that capture topics from emerging discoveries to long-pondered topics and everything in between. To explore the importance of Macromolecules’ reviews and perspectives, we considered their influence on the field and found the 1994 review by Fetters et al. entitled “Connection between Polymer Molecular Weight, Density, Chain Dimensions, and Melt Viscoelastic Properties”1 to be a singularity. This review expertly curates and compiles a trove of data to build robust correlations between molecular characteristics and macroscopic viscoelastic properties of polymer melts, in the context of the tube model of entanglements.

Accurate event locations and replicability of location analyses are essential for assessing the nature of an event, its context, ambient site conditions, and proximity to relevant facilities and infrastructure. Additionally, accurate event locations provide valuable information that reduce uncertainties, improve confidence in event analyses, and inform in-field verification activities. However, event location/relocation and replicability are difficult due to a number of factors, including spatially-sparse network coverage in some areas of the globe and variability in seismic data processing. This team proposed that the incorporation of high-fidelity imagery as a data backbone to the analytical assessment of a suspected underground explosion and/or an advanced seismic event bulletin produced by the International Data Centre (IDC) of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO PrepCom) could reduce uncertainties and improve confidence in analyses. Specifically, temporally-separated images can reduce uncertainty by identifying areas where change has occurred (e.g., building construction or demolition, road or facilities improvements). The primary goal of this project was to develop an automated geospatial processing script for imagery change detection to better reflect needs of the technical community (including the IDC) and to make the use of such a tool accessible in a variety of settings across platforms. Technical experts at Los Alamos National Laboratory successfully built GAIA: the Geospatial Automated Imagery Analysis tool, to fill this need. GAIA combines five tool components to produce orthorectified time-separated imagery and imagery change detection maps. Our toolkit (1) reduces error by providing a standardized workflow for image analyses and (2) significantly reduces processing time from between 7 and 24+ hours to approximately 5 minutes. Technical experts at Sandia National Laboratories supported GAIA via beta-testing and by introducing a web-based system approach for increased applicability. To test the function, performance, broad application, and ease-of-use of GAIA, we applied it to four separate test cases. The results of this preliminary investigation show promise in reducing uncertainty in seismic event locations: if satellite imagery can show regions where operations that produce seismic activity likely occurred, then pursuing imagery to locate epicenters of seismic nuclear events could reduce the time needed to find the true epicenter location.

Surface water quality along river corridors can be modulated by hyporheic zones (HZs) that are ubiquitous and biogeochemically active. Watershed management practices often ignore the potentially important role of HZs as a natural reactor. To investigate the effect of hydrological exchange and biogeochemical processes on the fate of nutrients in surface water and HZs, a novel model, SWAT-MRMT-R, was developed coupling the Soil and Water Assessment Tool (SWAT) watershed model and the reaction module from a flow and reactive transport code (PFLOTRAN). SWAT-MRMT-R simulates concurrent nonlinear multicomponent biogeochemical reactions in both the channel water and its surrounding HZs, connecting the channel water and HZs through hyporheic exchanges using multirate mass transfer (MRMT) representation. Within the model, HZs are conceptualized as transient storage zones with distinguished exchange rates and residence times. The biogeochemical processes within HZs are different from those in the channel water. Hyporheic exchanges are modeled as multiple first-order mass transfers between the channel water and HZs. As a numerical example, SWAT-MRMT-R is applied to the Hanford Reach of the Columbia River, a large river in the United States, focusing on nitrate dynamics in the channel water. Major nitrate contaminants entering the Hanford Reach include those from the legacy waste, irrigation return flows (irrigation water that is not consumed by crops and runs off as point sources to the stream), and groundwater seepage resulting from irrigated agriculture. A two-step reaction sequence for denitrification and an aerobic respiration reaction is assumed to represent the biogeochemical transformations taking place within the HZs. The spatially variable hyporheic exchange rates and residence times in this example are estimated with the basin-scale Networks with EXchange and Subsurface Storage (NEXSS) model. Our simulation results show that (1), given a residence time distribution, how the exchange fluxes to HZs are approximated when using MRMT can significantly change the amount of nitrate consumption in HZs through denitrification and (2) source locations of nitrate have a different impact on surface water quality due to the spatially variable hyporheic exchanges.

Hyperdynamics (HD) is a method for accelerating the timescale of standard molecular dynamics (MD). It can be used for simulations of systems with an energy potential landscape that is a collection of basins, separated by barriers, where transitions between basins are infrequent. HD enables the system to escape from a basin more quickly while enabling a statistically accurate renormalization of the simulation time, thus effectively boosting the timescale of the simulation. In the work of Kim et al. [J. Chem. Phys. 139, 144110 (2013)], a local version of HD was formulated, which exploits the intrinsic locality characteristic typical of most systems to mitigate the poor scaling properties of standard HD as the system size is increased. Here, we discuss how both HD and local HD can be formulated to run efficiently in parallel. We have implemented these ideas in the LAMMPS MD code, which means HD can be used with any interatomic potential LAMMPS supports. Together, these parallel methods allow simulations of any size to achieve the time acceleration offered by HD (which can be orders of magnitude), at a cost of 2-4× that of standard MD. As examples, we performed two simulations of a million-atom system to model the diffusion and clustering of Pt adatoms on a large patch of the Pt(100) surface for 80 μs and 160 μs.

The Corrected Rigid Spheres (CRIS) equation of state (EOS) model [Kerley, J. Chem. Phys. 73, 469 (1980); 73, 478 (1980); 73, 487 (1980)], developed from fluid perturbation theory using a hard sphere reference system, has been successfully used to calculate the EOS of many materials, including gases and metals. The radial distribution function (RDF) plays a pivotal role in choosing the sphere diameter, through a variational principle, as well as the thermodynamic response. Despite its success, the CRIS model has some shortcomings in that it predicts too large a temperature for liquid-vapor critical points, can break down at large compression, and is computationally expensive. We first demonstrate that an improved analytic representation of the hard sphere RDF does not alleviate these issues. Relaxing the strict adherence of the RDF to hard spheres allows an accurate fit to the isotherms and vapor dome of the Lennard-Jones fluid using an arbitrary reference system. The second order correction is eliminated, limiting the breakdown at large compression and significantly reducing the computation cost. The transferability of the new model to real systems is demonstrated on argon, with an improved vapor dome compared to the original CRIS model.

Controlling sub-microsecond desorption of water and other impurities from electrode surfaces at high heating rates is crucial for pulsed power applications. Despite the short time scales involved, quasi-equilibrium ideas based on transition state theory (TST) and Arrhenius temperature dependence have been widely applied to fit desorption activation free energies. In this work, we apply molecular dynamics (MD) simulations in conjunction with equilibrium potential-of-mean-force (PMF) techniques to directly compute the activation free energies (ΔG∗) associated with desorption of intact water molecules from Fe2O3 and Cr2O3 (0001) surfaces. The desorption free energy profiles are diffuse, without maxima, and have substantial dependences on temperature and surface water coverage. Incorporating the predicted ΔG∗ into an analytical form gives rate equations that are in reasonable agreement with non-equilibrium molecular dynamics desorption simulations. We also show that different ΔG∗ analytical functional forms which give similar predictions at a particular heating rate can yield desorption times that differ by up to a factor of four or more when the ramp rate is extrapolated by 8 orders of magnitude. This highlights the importance of constructing a physically-motivated ΔG∗ functional form to predict fast desorption kinetics.

Abstract not provided.

A sidewall activation process was optimized for buried magnesium-doped p-GaN layers yielding a significant reduction in tunnel junction-enabled light emitting diode (LED) forward voltage. This buried activation enabled the realization of cascaded blue LEDs with fully transparent GaN homojunction tunnel junctions. The initial optimization of buried p-GaN activation was performed on PN junctions grown by metal organic chemical vapor deposition (MOCVD) buried under hybrid tunnel junctions grown by MOCVD and molecular beam epitaxy. Next the activation process was implemented in cascaded blue LEDs emitting at 450 nm, which were enabled by fully transparent GaN homojunction tunnel junctions. The tunnel junction-enabled multi-active region blue LEDs were grown monolithically by MOCVD. This work demonstrates a state-of-the-art tunnel junction-enabled cascaded LED utilizing homojunction tunnel junctions which do not contain any heterojunction interface.

Activation of liquids with atmospheric pressure plasmas is being investigated for environmental and biomedical applications. When activating the liquid using gas plasma produced species (as opposed to plasmas sustained in the liquid), a rate limiting step is transport of these species into the liquid. To first order, the efficiency of activating the liquid is improved by increasing the ratio of the surface area of the water in contact with the plasma compared to its volume—often called the surface-to-volume ratio (SVR). Maximizing the SVR then motivates the plasma treatment of thin films of liquids. In this paper, results are discussed from a computational investigation using a global model of atmospheric pressure plasma treatment of thin water films by a dielectric barrier discharge (DBD) sustained in different gases (Ar, He, air, N₂, O₂). The densities of reactive species in the plasma activated water (PAW) are evaluated. The residence time of the water in contact with the plasma is increased by recirculating the PAW in plasma reactor. Longer lived species such as H₂O_2aq and NO₃^-_aq accumulate over time (aq denotes an aqueous species). DBDs sustained in Ar and He are the most efficient at producing H₂O_2aq, DBDs sustained in argon produces the largest density of NO₃^-_aq with the lowest pH, and discharges sustained in O₂ and air produce the highest densities of O_3aq. Finally, comparisons to experiments by others show agreement in the trends in densities in PAW including O_3aq, OH_aq, H₂O_2aq and NO₃^-_aq, and highlight the importance of controlling desolvation of species from the activated water.

Distribution system models play a critical role in the modern grid, driving distributed energy resource integration through hosting capacity analysis and providing insight into critical areas of interest such as grid resilience and stability. Thus, the ability to validate and improve existing distribution system models is also critical. This work presents a method for identifying service transformers which contain errors in specifying the customers connected to the low-voltage side of that transformer. Pairwise correlation coefficients of the smart meter voltage time series are used to detect when a customer is not in the transformer grouping that is specified in the model. The proposed method is demonstrated both on synthetic data as well as a real utility feeder, and it successfully identifies errors in the transformer labeling in both datasets.

The state of California is leading the nation with respect to solar energy and storage. The California Energy Commission has mandated that starting in 2020 all new homes must be solar powered. In 2010 the California state legislature adopted an energy storage mandate AB 2514. This required California's three largest utilities to contract for an additiona11.3 GW of energy storage by 2020, coming online by 2024. Therefore, there is keen interest in the potential advantages of deploying solar combined with energy storage. This paper formulates the optimization problem to identify the maximum potential revenue from pairing storage with solar and participating in the California Independent System Operator (CAISO) day ahead market for energy. Using the optimization formulation, five years of historical market data (2014-2018) for 2, 172 price nodes were analyzed to identify trends and opportunities for the deployment of solar plus storage.

Distributed photovoltaic (PV) systems equipped with advanced inverters can control real and reactive power output based on grid and atmospheric conditions. The Volt-Var control method allows inverters to regulate local grid voltages by producing or consuming reactive power. Based on their power ratings, the inverters may need to curtail real power to meet the reactive power requirements, which decreases their total energy production. To evaluate the expected curtailment associated with Volt-Var control, yearlong quasi-static time-series (QSTS) simulations were conducted on a realistic distribution feeder under a variety of PV system design considerations. Overall, this paper found that the amount of curtailed energy is low (< 0.55%) compared to the total PV energy production in a year but is affected by several PV system design considerations.

An overall capacity assessment and an analysis of the system's X/R ratios for six actual distribution feeders was conducted to characterize the voltage response to various levels of distributed Electric Vehicle Supply Equipment (EVSE). The evaluation identified the capacity of the system at which a voltage violation occurred. This included a review of the uncontrolled and controlled cases to quantify the value of injecting reactive power as the grid voltage decreases. The evaluation found that the implementation of a Volt-Var curve with a global voltage reference provided a notable increase in capacity. A local reference voltage, measured at the point of common coupling, did not increase the capacity of every feeder in the experiment. The review of the X/R line properties using a Principal Component Analysis (PCA) identified groups within the six feeders that corresponded with each system's voltage response rate. This suggests the X/R ratios provide a direct prediction of the feeder's ability to avoid voltage violations while charging EVs.

Aluminum alloys are being explored as lightweight structural materials for use in hydrogen-containing environments.To understand hydrogen effects on deformation, we perform molecular statics studies of the hydrogen Cottrell atmosphere around edge dislocations in aluminum. First, we calculate the hydrogen binding energies at all interstitial sites in a periodic aluminum crystal containing an edge dislocation dipole. This allows us to use the Boltzmann equation to quantify the hydrogen Cottrell atmosphere. Based on these binding energies, we then construct a continuum model to study the kinetics of the hydrogen Cottrell atmosphere formation. Finally, we compare our results with existing theories and discuss the effects of hydrogen on deformation of aluminum-based alloys.

Traditional techniques to derive dynamic specification for components have a great deal of uncertainty. One of the major sources of uncertainty is that the number of response measurements in the operational system environment is insufficient to determine the component motion. This inadequacy is due to logistical limitations for data recording in field testing and space limitations for accelerometers, strain gages and associated wiring. Available measurements are often some distance from the component and therefore do not represent component motion. Typical straight-line envelopes of these unrepresentative measurements guarantee an increase in the uncertainty. In this paper multiple methods are attempted to expand a sparse set of field test measurements on a system to responses of interest that cannot be measured in the field due to the limitations. Proof of concept is demonstrated on the Modal Analysis Test Vehicle (MATV). The responses of interest, known as “truth responses”, are measured in a system vibration environment along with an optimized sparse set of 30 field responses. Methods to expand the field responses to the truth responses are demonstrated by comparing the acceleration spectral density of the expanded response to the measured response. Two methods utilize a validated finite element model of the MATV. One is developed from purely experiment based frequency response functions of a laboratory pre-test. These approaches are designed to drastically reduce the uncertainty of the component in-service motion as a basis for developing specifications that are guaranteed to be conservative with a known (instead of unknown) conservatism.

Data-consistent inversion is a recently developed measure-theoretic framework for solving a stochastic inverse problem involving models of physical systems. The goal is to construct a probability measure on model inputs (i.e., parameters of interest) whose associated push-forward measure matches (i.e., is consistent with) a probability measure on the observable outputs of the model (i.e., quantities of interest). Previous implementations required the map from parameters of interest to quantities of interest to be deterministic. This work generalizes this framework for maps that are stochastic, i.e., contain uncertainties and variation not explainable by variations in uncertain parameters of interest. Generalizations of previous theorems of existence, uniqueness, and stability of the data-consistent solution are provided while new theoretical results address the stability of marginals on parameters of interest. A notable aspect of the algorithmic generalization is the ability to query the solution to generate independent identically distributed samples of the parameters of interest without requiring knowledge of the so-called stochastic parameters. This work therefore extends the applicability of the data-consistent inversion framework to a much wider class of problems. This includes those based on purely experimental and field data where only a subset of conditions are either controllable or can be documented between experiments while the underlying physics, measurement errors, and any additional covariates are either uncertain or not accounted for by the researcher. Numerical examples demonstrate application of this approach to systems with stochastic sources of uncertainties embedded within the modeling of a system and a numerical diagnostic is summarized that is useful for determining if a key assumption is verified among competing choices of stochastic maps.

Full-field data from digital image correlation (DIC) provide rich information for finite-element analysis (FEA) validation. However, there are several inherent inconsistencies between FEA and DIC data that must be rectified before meaningful, quantitative comparisons can be made, including strain formulations, coordinate systems, data locations, strain calculation algorithms, spatial resolutions and data filtering. In this paper, we investigate two full-field validation approaches: (1) the direct interpolation approach, which addresses the first three inconsistencies by interpolating the quantity of interest from one mesh to the other, and (2) the proposed DIC-levelling approach, which addresses all six inconsistencies simultaneously by processing the FEA data through a stereo-DIC simulator to ‘level' the FEA data to the DIC data in a regularisation sense. Synthetic ‘experimental' DIC data were generated based on a reference FEA of an exemplar test specimen. The direct interpolation approach was applied, and significant strain errors were computed, even though there was no model form error, because the filtering effect of the DIC engine was neglected. In contrast, the levelling approach provided accurate validation results, with no strain error when no model form error was present. Next, model form error was purposefully introduced via a mismatch of boundary conditions. With the direct interpolation approach, the mismatch in boundary conditions was completely obfuscated, while with the levelling approach, it was clearly observed. Finally, the ‘experimental' DIC data were purposefully misaligned slightly from the FEA data. Both validation techniques suffered from the misalignment, thus motivating continued efforts to develop a robust alignment process. In summary, direct interpolation is insufficient, and the proposed levelling approach is required to ensure that the FEA and the DIC data have the same spatial resolution and data filtering. Only after the FEA data have been ‘levelled' to the DIC data can meaningful, quantitative error maps be computed.

Dynamic strain aging (DSA) is the process of solute atoms segregating around dislocations on the timescale of loading. Continuum theories of DSA derived from elasticity theory have been shown to severely overpredict both the timescale and strengthening of DSA. Recently, cross-core theory was developed to reconcile this gap, invoking a special single-atomic-hop diffusion mechanism across the core of an extended dislocation. In this work, we show that the classical continuum theory expression for the rate of solute segregation is in error. After correcting this error, we show that continuum theory predictions match cross-core theory when the elevated diffusivity near the dislocation core is accounted for. Our findings indicate that continuum theory is still a useful tool for studying dislocation-solute interactions.

Determining a process–structure–property relationship is the holy grail of materials science, where both computational prediction in the forward direction and materials design in the inverse direction are essential. Problems in materials design are often considered in the context of process–property linkage by bypassing the materials structure, or in the context of structure–property linkage as in microstructure-sensitive design problems. However, there is a lack of research effort in studying materials design problems in the context of process–structure linkage, which has a great implication in reverse engineering. In this work, given a target microstructure, we propose an active learning high-throughput microstructure calibration framework to derive a set of processing parameters, which can produce an optimal microstructure that is statistically equivalent to the target microstructure. The proposed framework is formulated as a noisy multi-objective optimization problem, where each objective function measures a deterministic or statistical difference of the same microstructure descriptor between a candidate microstructure and a target microstructure. Furthermore, to significantly reduce the physical waiting wall-time, we enable the high-throughput feature of the microstructure calibration framework by adopting an asynchronously parallel Bayesian optimization by exploiting high-performance computing resources. Case studies in additive manufacturing and grain growth are used to demonstrate the applicability of the proposed framework, where kinetic Monte Carlo (kMC) simulation is used as a forward predictive model, such that for a given target microstructure, the target processing parameters that produced this microstructure are successfully recovered.

Additive Manufacturing (AM) presents unprecedented opportunities to enable design freedom in parts that are unachievable via conventional manufacturing. However, AM-processed components generally lack the necessary performance metrics for widespread commercial adoption. We present a novel AM processing and design approach using removable heat sink artifacts to tailor the mechanical properties of traditionally low strength and low ductility alloys. The design approach is demonstrated with the Fe-50 at.% Co alloy, as a model material of interest for electromagnetic applications. AM-processed components exhibited unprecedented performance, with a 300 % increase in strength and an order-of-magnitude improvement in ductility relative to conventional wrought material. These results are discussed in the context of product performance, production yield, and manufacturing implications toward enabling the design and processing of high-performance, next-generation components, and alloys.

Room-temperature ferromagnetic materials with perpendicular magnetic anisotropy are widely sought after for spintronics, magnetic data storage devices, and stochastic computing. To address this need, a new Fe-BaTiO3 vertically aligned nanocomposite (VAN) has been fabricated—combining both the strong room-temperature ferromagnetic properties of Fe nanopillars and the strong room-temperature ferroelectric properties of the BaTiO3 matrix. Furthermore, the Fe-BaTiO3 VAN allows for highly anisotropic magnetic properties with tunable magnetization and coercivity. In addition, to demonstrate the multiferroic properties of the Fe-BaTiO3 system, the new metal-oxide hybrid material system has been incorporated in a multilayer stack. This new multiferroic VAN system possesses great potential in magnetic anisotropy and property tuning and demonstrates a new material family of oxide-metal hybrid systems for room-temperature multiferroic material designs.

This paper presents a multi-Time period two-stage stochastic mixed-integer linear optimization model which determines the optimal hardening investments to improve power system resilience to natural disaster threat scenarios. The input to the optimization model is a set of scenarios for specific natural disaster events, that is based on historical data. The objective of the optimization model is to minimize the expected weighted load shed from the initial impact and the restoration process over all scenarios. The optimization model considers the initial impact of the severe event by using electromechanical transient dynamic simulations. The initial impact weighted load shed is determined by the transient simulation, which allows for secondary transients from protection devices and cascading failures. The rest of the event, after the initial shock, is modeled in the optimization with a multi-Time period dc optimal power flow (DCOPF) which is initialized with the solution from the dynamic simulation. The first stage of the optimization model determines the optimal investments. The second stage, given the investments, determines the optimal unit commitment, generator dispatch, and transmission line switching during the multi-Time period restoration process to minimize the weighted load shed over all scenarios. Note, an investment will change the transient simulation result, and therefore change the initialization to the DCOPF restoration model. The investment optimization model encompasses both the initial impact (dynamic transient simulation results) and the restoration period (DCOPF) of the event, as components come back online. The model is tested on the IEEE RTS-96 system.

Calibrating distribution system models to aid in the accuracy of simulations such as hosting capacity analysis is increasingly important in the pursuit of the goal of integrating more distributed energy resources. The recent availability of smart meter data is enabling the use of machine learning tools to automatically achieve model calibration tasks. This research focuses on applying machine learning to the phase identification task, using a co-association matrix-based, ensemble spectral clustering approach. The proposed method leverages voltage time series from smart meters and does not require existing or accurate phase labels. This work demonstrates the success of the proposed method on both synthetic and real data, surpassing the accuracy of other phase identification research.

This paper presents the subtleties of obtaining robust experimental scaling laws for the core resonant error field threshold that leads to field penetration, locked modes, and disruptions. Recent progress in attempts to project this threshold to new machines has focused on advances in the metric used to quantify the dangerous error fields, incorporating the ideal MHD plasma response in a metric referred to as the 'dominant mode overlap'. However, the scaling of this or any quantity with experimental parameters known to be important for the complicated tearing layer physics requires regressions performed for databases that, for historical reasons, unevenly sample the available parametric space. This paper presents the distribution of the existing international n = 1 database and details biases in the available sampling and details the sensitivity of ITER projections to simple least-squares regressions. Downsampling and a simple kernel density estimation weighted regression are used here to demonstrate the difference in projections that acknowledging the machine sampling bias can make. This results in more robust projection to parameters far from the 'usual' devices built thus far. Two multi-device and multi-parameter scalings of the EF threshold in Ohmic and powered plasmas are presented, projecting the threshold to ITER and investigating the impact of sampling biases.

NMR spectroscopy continues to provide important molecular level details of dynamics in different polymer materials, ranging from rubbers to highly crosslinked composites. It has been argued that thermoset polymers containing dynamic and chemical heterogeneities can be fully cured at temperatures well below the final glass transition temperature (Tg). In this paper, we described the use of static solid-state 1H NMR spectroscopy to measure the activation of different chain dynamics as a function of temperature. Near Tg, increasing polymer segmental chain fluctuations lead to dynamic averaging of the local homonuclear proton-proton (1H-1H) dipolar couplings, as reflected in the reduction of the NMR line shape second moment (M2) when motions are faster than the magnitude of the dipolar coupling. In general, for polymer systems, distributions in the dynamic correlation times are commonly expected. To help identify the limitations and pitfalls of M2 analyses, the impact of activation energy or, equivalently, correlation time distributions, on the analysis of 1H NMR M2 temperature variations is explored. It is shown by using normalized reference curves that the distributions in dynamic activation energies can be measured from the M2 temperature behavior. An example of the M2 analysis for a series of thermosetting polymers with systematically varied dynamic heterogeneity is presented and discussed.

We report the atomic- and nanosecond-scale quantification of kinetics of a shock-driven phase transition in Zr metal. We uniquely make use of a multiple shock-and-release loading pathway to shock Zr into the β phase and to create a quasisteady pressure and temperature state shortly after. Coupling shock loading with in situ time-resolved synchrotron x-ray diffraction, we probe the structural transformation of Zr in the steady state. Our results provide a quantified expression of kinetics of formation of β-Zr phase under shock loading: transition incubation time, completion time, and crystallization rate.

The study of hypersonic flows and their underlying aerothermochemical reactions is particularly important in the design and analysis of vehicles exiting and reentering Earth's atmosphere. Computational physics codes can be employed to simulate these phenomena; however, code verification of these codes is necessary to certify their credibility. To date, few approaches have been presented for verifying codes that simulate hypersonic flows, especially flows reacting in thermochemical nonequilibrium. In this work, we present our code-verification techniques for verifying the spatial accuracy and thermochemical source term in hypersonic reacting flows in thermochemical nonequilibrium. Additionally, we demonstrate the effectiveness of these techniques on the Sandia Parallel Aerodynamics and Reentry Code (SPARC).

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