Publications

We develop methods that could be used to qualify a training dataset and a data-driven turbulence closure trained on it. By qualify, we mean identify the kind of turbulent physics that could be simulated by the data-driven closure. We limit ourselves to closures for the Reynolds-Averaged Navier Stokes (RANS) equations. We build on our previous work on assembling feature-spaces, clustering and characterizing Direct Numerical Simulation datasets that are typically pooled to constitute training datasets. In this paper, we develop an alternative way to assemble feature-spaces and thus check the correctness and completeness of our previous method. We then use the characterization of our training dataset to identify if a data-driven turbulence closure learned on it would generalize to an unseen flow configuration – an impinging jet in our case. Finally, we train a RANS closure architected as a neural network, and develop an explanation i.e., an interpretable approximation, using generalized linear mixed-effects models and check whether the explanation resembles a contemporary closure from turbulence modeling.

A high-throughput experimental setup was used to characterize initiation threshold and growth to detonation in the explosives hexanitrostilbene (HNS) and pentaerythritol tetranitrate (PETN). The experiment sequentially launched an array of laser-driven flyers to shock samples arranged in a 96-well microplate geometry, with photonic Doppler velocimetry diagnostics to characterize flyer velocity and particle velocity at the explosive-substrate interface. Vapor-deposited films of HNS and PETN were used to provide numerous samples with various thicknesses, enabling characterization of the evolution of growth to detonation. One-dimensional hydrocode simulations were performed with reactions disabled to illustrate where the experimental data deviate from the predicted inert response. Prompt initiation was observed in 144 μm thick HNS films at flyer velocities near 3000 m/s and in 125 μm thick PETN films at flyer velocities near 2400 m/s. This experimental setup enables rapid quantification of the growth of reactions in explosive materials that can reach detonation at sub-millimeter length scales. These data can subsequently be used for parameterizing reactive burn models in hydrocode simulations, as discussed in Paper II [D. E. Kittell, R. Knepper, and A. S. Tappan, J. Appl. Phys. 131, 154902 (2022)].

Software is ubiquitous in society, but understanding it, especially without access to source code, is both non-trivial and critical to security. A specialized group of cyber defenders conducts reverse engineering (RE) to analyze software. The expertise-driven process of software RE is not well understood, especially from the perspective of workflows and automated tools. We conducted a task analysis to explore the cognitive processes that analysts follow when using static techniques on binary code. Experienced analysts were asked to statically find a vulnerability in a small binary that could allow for unverified access to root privileges. Results show a highly iterative process with commonly used cognitive states across participants of varying expertise, but little standardization in process order and structure. A goal-centered analysis offers a different perspective about dominant RE states. We discuss implications about the nature of RE expertise and opportunities for new automation to assist analysts using static techniques.

Quantum diamond microscope (QDM) magnetic field imaging is an emerging interrogation and diagnostic technique for integrated circuits (ICs). To date, the ICs measured with a QDM have been either too complex for us to predict the expected magnetic fields and benchmark the QDM performance or too simple to be relevant to the IC community. In this paper, we establish a 555 timer IC as a "model system"to optimize QDM measurement implementation, benchmark performance, and assess IC device functionality. To validate the magnetic field images taken with a QDM, we use a spice electronic circuit simulator and finite-element analysis (FEA) to model the magnetic fields from the 555 die for two functional states. We compare the advantages and the results of three IC-diamond measurement methods, confirm that the measured and simulated magnetic images are consistent, identify the magnetic signatures of current paths within the device, and discuss using this model system to advance QDM magnetic imaging as an IC diagnostic tool.

Time-resolved, absolute number densities of metastable N2(A3ς u +, v = 0, 1) molecules, ground state N2 and H atoms, and rotational-translational temperature have been measured by tunable diode laser absorption spectroscopy and two-photon absorption laser-induced fluorescence in diffuse N2 and N2-H2 plasmas during and after a nanosecond pulse discharge burst. Comparison of the measurement results with the kinetic modeling predictions, specifically the significant reduction of the N2(A3ς u +) populations and the rate of N atom generation during the burst, suggests that these two trends are related. The slow N atom decay in the afterglow, on a time scale longer than the discharge burst, demonstrates that the latter trend is not affected by N atom recombination, diffusion to the walls, or convection with the flow. This leads to the conclusion that the energy pooling in collisions of N2(A3ς u +) molecules is a major channel of N2 dissociation in electric discharges where a significant fraction of the input energy goes to electronic excitation of N2. Additional measurements in a 1% H2-N2 mixture demonstrate a further significant reduction of N2(A3ς u +, v = 0, 1) populations, due to the rapid quenching by H atoms accumulating in the plasma. Comparison with the modeling predictions suggests that the N2(A3ς u +) molecules may be initially formed in the highly vibrationally excited states. The reduction of the N2(A3ς u +) number density also diminishes the contribution of the energy pooling process into N2 dissociation, thus reducing the N atom number density. The rate of N atom generation during the burst also decreases, due to its strong coupling to N2(A3ς u +, v) populations. On the other hand, the rate of H atom generation, produced predominantly by the dissociative quenching of the excited electronic states of N2 by H2, remains about the same during the burst, resulting in a nearly linear rise in the H atom number density. Comparison of the kinetic model predictions with the experimental results suggests that the yield of H atoms during the quenching of the excited electronic state of N2 by molecular H2 is significantly less than 100%. The present results quantify the yield of N and H atoms in high-pressure H2-N2 plasmas, which have significant potential for ammonia generation using plasma-assisted catalysis.

We demonstrate an optical waveguide device capable of supporting the optical power necessary for trapping a single atom or a cold-atom ensemble with evanescent fields. Our photonic integrated platform successfully manages optical powers of ~30mW.

The emerging use of the physics-based athermal recombination-corrected displacement per atom (arc-dpa) model for the displacement damage efficiency has motivated a re-evaluation of the historical empirically-derived GaAs damage response function with the purpose of highlighting needs for future analytical and experimental work. The 1-MeV neutron damage equivalence methodology used in the ASTM E-722 standard for GaAs has been re-evaluated using updated nuclear data. This yielded a higher fidelity representation of the GaAs displacement kerma and, through the use of the refined PKA recoil energy-dependent damage efficiency model, an updated 1-MeV(GaAs) displacement damage function. This re-evaluation included use of the Norgett-Robinson-Torrens (NRT) model for an updated threshold treatment, rather than the sharp-threshold Kinchin-Pease model used in the current ASTM standard. The underlying nuclear data evaluations have been updated to use the ENDF/VIII.0 ⁷⁵As and TENDL-2019 ⁷¹Ga/⁶⁹Ga evaluations. The displacement kerma and 1-MeV-equivalent damage responses were calculated using a modified NJOY-2016 code which allowed for refinements in some of the damage models. This paper shows that an updated displacement damage function, based upon the latest nuclear data, is consistent with the experimental data used to develop the current ASTM E-722 GaAs standard. Using a double ratio approach to compare the available experimental data with the calculated response, the average legacy double ratio was found to be 0.97±0.05 and the average updated double ratio was found to be 0.94 ±0.05.

For the model-based control of low-voltage microgrids, state and parameter information are required. Different optimal estimation techniques can be employed for this purpose. However, these estimation techniques require knowledge of noise covariances (process and measurement noise). Incorrect values of noise covariances can deteriorate the estimator performance, which in turn can reduce the overall controller performance. This paper presents a method to identify noise covariances for voltage dynamics estimation in a microgrid. The method is based on the autocovariance least squares technique. A simulation study of a simplified 100 kVA, 208 V microgrid system in MATLAB/Simulink validates the method. Results show that estimation accuracy is close to the actual value for Gaussian noise, and non-Gaussian noise has a slightly larger error.

This paper demonstrates that a faster Automatic Generation Control (AGC) response provided by Inverter-Based Resources (IBRs) can improve a performance-based regulation (PBR) metric. The improvement in performance has a direct effect on operational income. The PBR metric used in this work was obtained from a California ISO (CAISO) example and is fully described herein. A single generator in a modified three area IEEE 39 bus system was replaced with a group of co-located IBRs to present possible responses using different plant controls and variable resource conditions. We show how a group of IBRs that rely on variable resources may negatively affect the described PBR metric of all connected areas if adequate plant control is not employed. However, increasing the dispatch rate of internal plant controls may positively affect the PBR metric of all connected areas despite variable resource conditions.

The Multi-Fidelity Toolkit (MFTK) is a simulation tool being developed at Sandia National Laboratories for aerodynamic predictions of compressible flows over a range of physics fidelities and computational speeds. These models include the Reynolds-Averaged Navier–Stokes (RANS) equations, the Euler equations, and modified Newtonian aerodynamics (MNA) equations, and they can be invoked independently or coupled with hierarchical Kriging to interpolate between high-fidelity simulations using lower-fidelity data. However, as with any new simulation capability, verification and validation are necessary to gather credibility evidence. This work describes formal code-and solution-verification activities. Code verification is performed on the MNA model by comparing with an analytical solution for flat-plate and inclined-plate geometries. Solution-verification activities include grid-refinement studies of HIFiRE-1 wind tunnel measurements, which are used for validation, for all model fidelities.

Kokkos is a C++ library and ecosystem for writing parallel programs on heterogeneous systems. One of the primary goals of Kokkos is portability: programs in Kokkos are expressed through general parallel constructs which can enable the same code to compile and execute on different parallel architectures. However, there is no known formal model of Kokkos's semantics, which must be generic enough to support current and future CPU and accelerator architectures. As a first step of formalizing Kokkos, We introduce MiniKokkos: a small language capturing the main features of Kokkos, and then prove that MiniKokkos ensures portability across all possible parallel executions. We also provide a case study of how MiniKokkos can help reason about Kokkos programs and help find a bug in the Kokkos implementation.

Abstract not provided.

Advances in wind-plant control have often focused on more effectively balancing power between neighboring turbines. Wake steering is one such method that provides control-based improvements in a quasi-static way, but this does little to fundamentally change the wake recovery process, and thus, it has limited potential. This study investigates use of another control paradigm known as dynamic wake control (DWC) to excite the mutual inductance instability between adjacent tip-vortex structures, thereby accelerating the breakdown of the structures. The current work carries this approach beyond the hypothetical by applying the excitation via turbine control vectors that already exist on all modern wind turbines: blade pitch and rotor speed control. The investigation leverages a free-vortex wake method (FVWM) that allows a thorough exploration of relevant frequencies and amplitudes of harmonic forcing for each control vector (as well as the phase difference between the vectors for a tandem configuration) while still capturing the essential tip-vortex dynamics. The FVWM output feeds into a Fourier stability analysis working to pinpoint candidate DWC strategies suggesting fastest wake recovery. Near-wake length reductions of >80% are demonstrated, although without considering inflow turbulence. Analysis is provided to interpret these predictions considering the presence of turbulence in a real atmospheric inflow.

Measurements of gas-phase pressure and temperature in hypersonic flows are important to understanding fluid–structure interactions on vehicle surfaces, and to develop compressible flow turbulence models. To achieve this measurement capability, femtosecond coherent anti-Stokes Raman scattering (fs CARS) is applied at Sandia National Laboratories’ hypersonic wind tunnel. After excitation of rotational Raman transitions by a broadband femtosecond laser pulse, two probe pulses are used: one at an early time where the collisional environment has largely not affected the Raman coherence, and another at a later time after the collisional environment has led to significant J-dependent dephasing of the Raman coherence. CARS spectra from the early probe are fit for temperature, while the later CARS spectra are fit for pressure. Challenges related to implementing fs CARS in cold-flow hypersonic facilities are discussed. Excessive fs pump energy can lead to flow perturbations. The output of a second-harmonic bandwidth compressor (SHBC) is spectrally filtered using a volume Bragg grating to provide the narrowband ps probe pulses and enable single-shot CARS measurements at 1 kHz. Measurements are demonstrated at temperatures and pressures relevant to cold-flow hypersonic wind tunnels in a low-pressure cryostat with an initial demonstration in the hypersonic wind tunnel.

Development of job scheduling algorithms, which directly influence High-Performance Computing (HPC) clusters performance, is hindered because popular scheduling quality metrics, such as Bounded Slowdown, poorly correlate with global scheduling objectives that include job packing efficiency and fairness. This report proposes Area Weighted Response Time, a metric that offers an unbiased representation of job packing efficiency, and presents a class of new metrics, Priority Weighted Specific Response Time, that assess both packing efficiency and fairness of schedules. The provided examples of simulation of scheduling of real workload traces and analysis of the resulting schedules with the help of these metrics and conventional metrics, demonstrate that although Bounded Slowdown can be readily improved by modifying the standard First Come First Served backfilling algorithm and by using existing techniques of estimating job runtime, these improvements are accompanied by significant degradation of job packing efficiency and fairness. In contrast, improving job packing efficiency and fairness over the standard backfilling algorithm, which is designed to target those objectives, is difficult. It requires further algorithm development and more accurate runtime estimation techniques that reduce frequency of underpredictions.

Applications such as counterfeit identification, quality control, and non-destructive material identification benefit from improved spatial and compositional analysis. X-ray Computed Tomography is used in these applications but is limited by the X-ray focal spot size and the lack of energy-resolved data. Recently developed hyperspectral X-ray detectors estimate photon energy, which enables composition analysis but lacks spatial resolution. Moving beyond bulk homogeneous transmission anodes toward multi-metal patterned anodes enables improvements in spatial resolution and signal-to-noise ratios in these hyperspectral X-ray imaging systems. We aim to design and fabricate transmission anodes that facilitate confirmation of previous simulation results. These anodes are fabricated on diamond substrates with conventional photolithography and metal deposition processes. The final transmission anode design consists of a cluster of three disjoint metal bumps selected from molybdenum, silver, samarium, tungsten, and gold. These metals are chosen for their k-lines, which are positioned within distinct energy intervals of interest and are readily available in standard clean rooms. The diamond substrate is chosen for its high thermal conductivity and high transmittance of X-rays. The feature size of the metal bumps is chosen such that the cluster is smaller than the 100 m diameter of the impinging electron beam in the X-ray tube. This effectively shrinks the X-ray focal spot in the selected energy bands. Once fabricated, our transmission anode is packaged in a stainless-steel holder that can be retrofitted into our existing X-ray tube. Innovations in anode design enable an inexpensive and simple method to improve existing X-ray imaging systems.

We develop a framework for Gaussian processes regression constrained by boundary value problems. The framework may be applied to infer the solution of a well-posed boundary value problem with a known second-order differential operator and boundary conditions, but for which only scattered observations of the source term are available. Scattered observations of the solution may also be used in the regression. The framework combines co-kriging with the linear transformation of a Gaussian process together with the use of kernels given by spectral expansions in eigenfunctions of the boundary value problem. Thus, it benefits from a reduced-rank property of covariance matrices. We demonstrate that the resulting framework yields more accurate and stable solution inference as compared to physics-informed Gaussian process regression without boundary condition constraints.

Traditional deep learning (DL) models are powerful classifiers, but many approaches do not provide uncertainties for their estimates. Uncertainty quantification (UQ) methods for DL models have received increased attention in the literature due to their usefulness in decision making, particularly for high-consequence decisions. However, there has been little research done on how to evaluate the quality of such methods. We use statistical methods of frequentist interval coverage and interval width to evaluate the quality of credible intervals, and expected calibration error to evaluate classification predicted confidence. These metrics are evaluated on Bayesian neural networks (BNN) fit using Markov Chain Monte Carlo (MCMC) and variational inference (VI), bootstrapped neural networks (NN), Deep Ensembles (DE), and Monte Carlo (MC) dropout. We apply these different UQ for DL methods to a hyperspectral image target detection problem and show the inconsistency of the different methods' results and the necessity of a UQ quality metric. To reconcile these differences and choose a UQ method that appropriately quantifies the uncertainty, we create a simulated data set with fully parameterized probability distribution for a two-class classification problem. The gold standard MCMC performs the best overall, and the bootstrapped NN is a close second, requiring the same computational expense as DE. Through this comparison, we demonstrate that, for a given data set, different models can produce uncertainty estimates of markedly different quality. This in turn points to a great need for principled assessment methods of UQ quality in DL applications.

Software sustainability is critical for Computational Science and Engineering (CSE) software. Measuring sustainability is challenging because sustainability consists of many attributes. One factor that impacts software sustainability is the complexity of the source code. This paper introduces an approach for utilizing complexity data, with a focus on hotspots of and changes in complexity, to assist developers in performing code reviews and inform project teams about longer-term changes in sustainability and maintainability from the perspective of cyclomatic complexity. We present an analysis of data associated with four real-world pull requests to demonstrate how the metrics may help guide and inform the code review process and how the data can be used to measure changes in complexity over time.

Multi-model Monte Carlo methods have been illustrated to be an efficient and accurate alternative to standard Monte Carlo (MC) in the model-based propagation of uncertainty in entry, descent, and landing (EDL) applications. These multi-model MC methods fuse predictions from low-fidelity models with the high-fidelity EDL model of interest to produce unbiased statistics with a fraction of the computational cost. The accuracy and efficiency of the multi-model MC methods are dependent upon the magnitude of correlations of the low-fidelity models with the high-fidelity model, but also upon the correlation amongst the low-fidelity models, and their relative computational cost. Because of this layer of complexity, the question of how to optimally select the set of low-fidelity models has remained open. In this work, methods for optimal model construction and tuning are investigated as a means to increase the speed and precision of trajectory simulation for EDL. Specifically, the focus is on the inclusion of low-fidelity model tuning within the sample allocation optimization that accompanies multi-model MC methods. Results indicate that low-fidelity model tuning can significantly improve efficiency and precision of trajectory simulations and provide an increased edge to multi-model MC methods when compared to standard MC.

A crucial component of field testing is the utilization of numerical models to better understand the system and the experimental data being collected. Meshing and modeling field tests is a complex and computationally demanding problem. Hexahedral elements cannot always reproduce experimental dimensions leading to grid orientation or geometric errors. Voronoi meshes can match complex geometries without sacrificing orthogonality. As a result, here we present a high-resolution 3D numerical study for the BATS heater test at the WIPP that compares both a standard non-deformed cartesian mesh along with a Voronoi mesh to match field data collected during a salt heater experiment.

This chapter deals with experimental dynamic substructures which are reduced order models that can be coupled with each other or with finite element derived substructures to estimate the system response of the coupled substructures. A unifying theoretical framework in the physical, modal or frequency domain is reviewed with examples. The major issues that have hindered experimental based substructures are addressed. An example is demonstrated with the transmission simulator method that overcomes the major historical difficulties. Guidelines for the transmission simulator design are presented.

Energy storage is an extremely flexible grid asset than can provide a wide range of services. Unfortunately, energy storage is often relatively expensive compared to other options. With the emphasis on decarbonization, energy storage is required to buffer the intermittency associated with variable renewable generation. This paper calculates the maximum potential revenue from an energy storage system engaged in day-ahead market arbitrage in the California Independent System Operator (CAISO) region and uses these results to estimate the distribution of break-even capital costs. Break-even cost data is extremely useful as it provides insight into expected market penetration given a target capital cost. This information is also valuable for setting policy related to energy storage incentives as well as for setting price targets for research and development initiatives. The potential annual revenue of a generic battery energy storage system (BESS) participating in the CAISO day-ahead energy market was analyzed for 2,145 nodes over a seven year period (2014-2020). This data was used to estimate the break-even capital cost for each node as well as the cost requirements for several internal rate of return scenarios. Based on the analysis, the capital costs of lithium-ion systems must be reduced by approximately 80% from current levels to enable arbitrage applications to have a reasonable rate of return.

Abstract not provided.

Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficient operation in this system especially for larger workloads. Our work leverages the underlying physics of emerging devices to develop probabilistic neural circuits for RNGs from a given distribution. However, codesign for novel circuits and systems that leverage inherent device stochasticity is a hard problem. This is mostly due to the large design space and complexity of doing so. It requires concurrent input from multiple areas in the design stack from algorithms, architectures, circuits, to devices. In this paper, we present examples of optimal circuits developed leveraging AI-enhanced codesign techniques using constraints from emerging devices and algorithms. Our AI-enhanced codesign approach accelerated design and enabled interactions between experts from different areas of the micro-electronics design stack including theory, algorithms, circuits, and devices. We demonstrate optimal probabilistic neural circuits using magnetic tunnel junction and tunnel diode devices that generate an RNG from a given distribution.

We present a new strategy for automatically exploring the design space of key CUDA + MPI programs and providing design rules that discriminate slow from fast implementations. In such programs, the order of operations (e.g., G PU kernels, MPI communication) and assignment of operations to resources (e.g., G PU streams) makes the space of possible designs enormous. Systems experts have the task of redesigning and reoptimizing these programs to effectively utilize each new platform. This work provides a prototype tool to reduce that burden. In our approach, a directed acyclic graph of CUDA and MPI operations defines the design space for the program. Monte-Carlo tree search discovers regions of the design space that have large impact on the program's performance. A sequence-to-vector transformation defines features for each explored im-plementation, and each implementation is assigned a class label according to its relative performance. A decision tree is trained on the features and labels to produce design rules for each class; these rules can be used by systems experts to guide their implementations. We demonstrate our strategy using a key kernel from scientific computing - sparse-matrix vector multiplication - on a platform with multiple MPI ranks and GPU streams.

Abstract not provided.

Iterative methods for solving linear systems serve as a basic building block for computational science. The computational cost of these methods can be significantly influenced by the round-off errors that accumulate as a result of their implementation in finite precision. In the extreme case, round-off errors that occur in practice can completely prevent an implementation from satisfying the accuracy and convergence behavior prescribed by its underlying algorithm. In the exascale era where cost is paramount, a thorough and rigorous analysis of the delay of convergence due to round-off should not be ignored. In this paper, we use a small model problem and the Jacobi iterative method to demonstrate how the Coq proof assistant can be used to formally specify the floating-point behavior of iterative methods, and to rigorously prove the accuracy of these methods.

Calibrating a finite element model to test data is often required to accurately characterize a joint, predict its dynamic behavior, and determine fastener fatigue life. In this work, modal testing, model calibration, and fatigue analysis are performed for a bolted structure, and various joint modeling techniques are compared. The structure is designed to test a single bolt to fatigue failure by utilizing an electrodynamic modal shaker to axially force the bolted joint at resonance. Modal testing is done to obtain the dynamic properties, evaluate finite element joint modeling techniques, and assess the effectiveness of a vibration approach to fatigue testing of bolts. Results show that common joint models can be inaccurate in predicting bolt loads, and even when updated using modal test data, linear structural models alone may be insufficient in evaluating fastener fatigue.

Detailed analysis of both the line-intensity ratios and line shapes of the K-lines of elements of different abundances (Fe, Cr, Ni, and Mn) emitted from the stagnation of a steel wire-array implosion on Z, were used to determine the line opacities. While the opacities at the early time of stagnation appear to be consistent with a nearly uniform hot-plasma cylinder on-axis surrounded by a colder annulus, the opacities during the peak K-emission strongly suggest that the main K-emission is due to small hot regions (spots) spread over the stagnating column. The spots are shown to be at least 4× denser than expected based on a uniform-cylinder emission (namely, ni > 3 ×1020 cm-3 ), are of diameters of about 200 μ or less (where the smaller the spots the higher are the densities), and are thousands in number. The total mass of the spots was determined to be 3-10 % of the load mass, and their total volume 3-15 % of the O 1.2-mm stagnation-column volume, both are less than the respective values for the earlier period of lower K power.

This work evaluates the benefits of using a 'smart' network interface card (SmartNIC) as a compute accelerator for the example of the MiniMD molecular dynamics proxy application. The accelerator is NVIDIA's BlueField-2 card, which includes an 8-core Arm processor along with a small amount of DRAM and storage. We test the networking and data movement performance of these cards compared to a standard Intel server host using microbenchmarks and MiniMD. In MiniMD, we identify two distinct classes of computation, namely core computation and maintenance computation, which are executed in sequence. We restructure the algorithm and code to weaken this dependence and increase task parallelism, thereby making it possible to increase utilization of the BlueField-2 concurrently with the host. We evaluate our implementation on a cluster consisting of 16 dual-socket Intel Broadwell host nodes with one BlueField-2 per host-node. Our results show that while the overall compute performance of BlueField-2 is limited, using them with a modified MiniMD algorithm allows for up to 20% speedup over the host CPU baseline with no loss in simulation accuracy.

High-performing teams learn intelligent and efficient communication and coordination strategies to maximize their joint utility. These teams implicitly understand the different roles of heterogeneous team members and adapt their communication protocols accordingly. Multi-Agent Reinforcement Learning (MARL) seeks to develop computational methods for synthesizing such coordination strategies, but formulating models for heterogeneous teams with different state, action, and observation spaces has remained an open problem. Without properly modeling agent heterogeneity, as in prior MARL work that leverages homogeneous graph networks, communication becomes less helpful and can even deteriorate the cooperativity and team performance. We propose Heterogeneous Policy Networks (HetNet) to learn efficient and diverse communication models for coordinating cooperative heterogeneous teams. Building on heterogeneous graph-attention networks, we show that HetNet not only facilitates learning heterogeneous collaborative policies per existing agent-class but also enables end-to-end training for learning highly efficient binarized messaging. Our empirical evaluation shows that HetNet sets a new state of the art in learning coordination and communication strategies for heterogeneous multi-agent teams by achieving an 8.1% to 434.7% performance improvement over the next-best baseline across multiple domains while simultaneously achieving a 200× reduction in the required communication bandwidth.

The development of the High-Resolution Wavelet Transform (HRWT) is driven by the need of increasing the high-frequency resolution of widely used discrete Wavelet Transforms (WTs). Based on the Stationary Wavelet Transform (SWT), which is a modification of the Discrete Wavelet Transform (DWT), a novel WT that increases the number of decomposition levels (therefore increasing the previously mentioned frequency resolution) is proposed. In order to show the validity of the HRWT, this paper encompasses a theoretical comparison with other discrete WT methods. First, a summary of the DWT and the SWT, along with a brief explanation of the WT theory, is provided. Then, the concept of the HRWT is presented, followed by a discussion of the adherence of this new method to the WT's common properties. Finally, an example of the application is performed on a transient waveform analysis from a power system fault event, outlining the benefits that can be obtained from its usage compared to the SWT.

With the increase in penetration of inverter-based resources (IBRs) in the electrical power system, the ability of these devices to provide grid support to the system has become a necessity. With standards previously developed for the interconnection requirements of grid-following inverters (GFLI) (most commonly photovoltaic inverters), it has been well documented how these inverters 'should' respond to changes in voltage and frequency. However, with other IBRs such as grid-forming inverters (GFMIs) (used for energy storage systems, standalone systems, and as uninterruptable power supplies) these requirements are either: not yet documented, or require a more in deep analysis. With the increased interest in microgrids, GFMIs that can be paralleled onto a distribution system have become desired. With the proper control schemes, a GFMI can help maintain grid stability through fast response compared to rotating machines. This paper will present an experimental comparison of commercially available GFMIand GFLI ' responses to voltage and frequency deviation, as well as the GFMIoperating as a standalone system and subjected to various changes in loads.

Metal additive manufacturing allows for the fabrication of parts at the point of use as well as the manufacture of parts with complex geometries that would be difficult to manufacture via conventional methods (milling, casting, etc.). Additively manufactured parts are likely to contain internal defects due to the melt pool, powder material, and laser velocity conditions when printing. Two different types of defects were present in the CT scans of printed AlSi10Mg dogbones: spherical porosity and irregular porosity. Identification of these pores via a machine learning approach (i.e., support vector machines, convolutional neural networks, k-nearest neighbors’ classifiers) could be helpful with part qualification and inspections. The machine learning approach will aim to label the regions of porosity and label the type of porosity present. The results showed that a combination approach of Canny edge detection and a classification-based machine learning model (k-nearest neighbors or support vector machine) outperformed the convolutional neural network in segmenting and labeling different types of porosity.

In the summer of 2020, the National Aeronautics and Space Administration (NASA) launched a spacecraft as part of the Mars 2020 mission. The rover on the spacecraft uses a Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) to provide continuous electrical and thermal power for the mission. The MMRTG uses radioactive plutonium dioxide. NASA prepared a Supplemental Environmental Impact Statement (SEIS) for the mission in accordance with the National Environmental Policy Act. The SEIS provides information related to updates to the potential environmental impacts associated with the Mars 2020 mission as outlined in the Final Environmental Impact Statement (FEIS) for the Mars 2020 Mission issued in 2014 and associated Record of Decision (ROD) issued in January 2015. The Nuclear Risk Assessment (NRA) 2019 Update includes new and updated Mars 2020 mission information since the publication of the 2014 FEIS and the updates to the Launch Approval Process with the issuance of Presidential Memorandum on Launch of Spacecraft Containing Space Nuclear Systems, National Security Presidential Memorandum 20 (NSPM-20). The NRA 2019 Update addresses the responses of the MMRTG to potential accident and abort conditions during the launch opportunity for the Mars 2020 mission and the associated consequences. This information provides the technical basis for the radiological risks discussed in the SEIS. This paper provides a summary of the methods and results used in the NRA 2019 Update.

A survey of cadmium plated field return hardware showed ubiquitous cadmium whisker growth. The most worn and debris-covered hardware showed the densest whisker growth. Whiskers were often found growing in agglomerates of nodules and whiskers. The hardware was rinsed with alcohol to transfer whiskers and debris from the hardware to a flat stub. Fifty whiskers were studied individually by scanning electron microscopy (SEM), including energy dispersive spectroscopy (EDS) and electron backscatter diffraction (EBSD). Most of the whiskers were single crystal, though three were found to contain grain boundaries at kinks. The whiskers ranged from 5 to 600 μm in length and 80 pct showed a <1 ¯ 2 1 ¯ 0> type growth direction. This growth direction facilitates the development of low energy side faces of the whisker, (0001) and {1010}.

A high-speed, two-color pyrometer was developed and employed to characterize the temperature of the ejecta from pyrotechnic igniters. The pyrometer used a single objective lens, beamsplitter, and two high-speed cameras to maximize the spatial and temporal resolutions. The pyrometer used the integrated intensity of under-resolved particles to maintain a large region of interest to capture more particles. The spectral response of the pyrometer was determined based on the response of each optical component and the total system was calibrated using a black body source to ensure accurate intensity ratios over the range of interest.

Periodic arrays of nanoholes perforated in metallic thin films interact strongly with light and produce large electromagnetic near-field enhancements in their vicinity. As a result, the optical response of these systems is very sensitive to changes in their dielectric environment, thus making them an exceptional platform for the development of compact optical sensors. Given that these systems already operate at the shot-noise limit when used as optical sensors, their sensing capabilities can be enhanced beyond this limit by probing them with quantum light, such as squeezed or entangled states. Motivated by this goal, here, we present a comparative theoretical analysis of the quantum enhanced sensing capabilities of metallic nanohole arrays with one and two holes per unit cell. Through a detailed investigation of their optical response, we find that the two-hole array supports resonances that are narrower and stronger than its one-hole counterpart, and therefore have a higher fundamental sensitivity limit as defined by the quantum Cramér-Rao bound. We validate the optical response of the analyzed arrays with experimental measurements of the reflectance of representative samples. The results of this work advance our understanding of the optical response of these systems and pave the way for developing sensing platforms capable of taking full advantage of the resources offered by quantum states of light.

Despite their susceptibility to hydrogen-assisted fracture, ferritic steels make up a large portion of the hydrogen infrastructure. It is impractical and too costly to build large scale components such as pipelines and pressure vessels out of more hydrogen-resistant materials such as austenitic stainless steels. Therefore, it is necessary to understand the fracture behavior of ferritic steels in high-pressure hydrogen environments to manage design margins and reduce costs. Quenched and tempered (Q&T) martensite is the predominant microstructure of high-pressure hydrogen pressure vessels, and higher strength grades of this steel type are more susceptible to hydrogen degradation than lower strength grades. In this study, a single heat of 4340 alloy was heat treated to develop alternative microstructures for evaluation of fracture resistance in hydrogen gas. Fracture tests of several microstructures, such as lower bainite and upper bainite with similar strength to the baseline Q&T martensite, were tested at 21 and 105 MPa H2. Despite a higher MnS inclusion content in the tested 4340 alloy which reduced the fracture toughness in air, the fracture behavior in hydrogen gas fit a similar trend to other previously tested Q&T martensitic steels. The lower bainite microstructure performed similar to the Q&T martensite, whereas the upper bainite microstructure performed slightly worse. In this paper, we extend the range of high-strength microstructures evaluated for hydrogen-assisted fracture beyond conventional Q&T martensitic steels.

Abstract not provided.

A new small-scale pressure vessel with a 5×5 fuel assembly and axially truncated PWR hardware was created to simulate commercial vacuum drying processes. This test assembly, known as the Dashpot Drying Apparatus, was built to focus on the drying of a single PWR dashpot and surrounding fuel. Drying operations were simulated for three tests with the DDA based on the pressure and temperature histories observed in the HBDP. All three tests were conducted with an empty guide tube. One test was performed with deionized water as the fill fluid. The other two tests used 0.2 M boric acid as the fill fluid to accurately simulate spent fuel pool conditions. These tests proved the capability of the DDA to mimic commercial drying processes on a limited scale and detect the presence of bulk and residual water. Furthermore, for all tests, pressure remained below the 0.4 kPa (3 Torr) rebound threshold for the final evacuation step in the drying procedure. Results indicate that after bulk fluid is removed from the pressure vessel, residual water is verifiably measured through confirmatory measurements of pressure and water content using a mass spectrometer. The final pressure rebound behaviors for the three tests conducted were well below the established regulatory limit of less than 0.4 kPa (3 Torr) within 30 minutes of isolation. The water content measurements across all tests showed that despite observing high water content within the DDA vessel at the beginning of the vacuum isolations, the water content drastically drops to below 1,200 ppmv after the isolations were conducted. The data and operational experience from these tests will guide the next evolution of experiments on a prototypic-length scale with multiple surrogate rods in a full 17×17 PWR assembly. The insight gained through these investigations is expected to support the technical basis for the continued safe storage of spent nuclear fuel into long term operations.

Firmware emulation is useful for finding vulnerabil-ities, performing debugging, and testing functionalities. However, the process of enabling firmware to execute in an emulator (i.e., re-hosting) is difficult. Each piece of the firmware may depend on hardware peripherals outside the microcontroller that are inaccessible during emulation. Current practices involve painstakingly disentangling these dependencies or replacing them with developed models that emulate functions interacting with hardware. Unfortunately, both are highly manual and error-prone. In this paper, we introduce a systematic graph-based approach to analyze firmware binaries and determine which functions need to be replaced. Our approach is customizable to balance the fidelity of the emulation and the amount of effort it would take to achieve the emulation by modeling functions. We run our algorithm across a number of firmware binaries and show its ability to capture and remove a large majority of hardware dependencies.

Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficient operation in this system especially for larger workloads. Our work leverages the underlying physics of emerging devices to develop probabilistic neural circuits for RNGs from a given distribution. However, codesign for novel circuits and systems that leverage inherent device stochasticity is a hard problem. This is mostly due to the large design space and complexity of doing so. It requires concurrent input from multiple areas in the design stack from algorithms, architectures, circuits, to devices. In this paper, we present examples of optimal circuits developed leveraging AI-enhanced codesign techniques using constraints from emerging devices and algorithms. Our AI-enhanced codesign approach accelerated design and enabled interactions between experts from different areas of the micro-electronics design stack including theory, algorithms, circuits, and devices. We demonstrate optimal probabilistic neural circuits using magnetic tunnel junction and tunnel diode devices that generate an RNG from a given distribution.

This paper describes an efficient reverse-mode differentiation algorithm for contraction operations for arbitrary and unconventional tensor network topologies. The approach leverages the tensor contraction tree of Evenbly and Pfeifer (2014), which provides an instruction set for the contraction sequence of a network. We show that this tree can be efficiently leveraged for differentiation of a full tensor network contraction using a recursive scheme that exploits (1) the bilinear property of contraction and (2) the property that trees have a single path from root to leaves. While differentiation of tensor-tensor contraction is already possible in most automatic differentiation packages, we show that exploiting these two additional properties in the specific context of contraction sequences can improve eficiency. Following a description of the algorithm and computational complexity analysis, we investigate its utility for gradient-based supervised learning for low-rank function recovery and for fitting real-world unstructured datasets. We demonstrate improved performance over alternating least-squares optimization approaches and the capability to handle heterogeneous and arbitrary tensor network formats. When compared to alternating minimization algorithms, we find that the gradient-based approach requires a smaller oversampling ratio (number of samples compared to number model parameters) for recovery. This increased efficiency extends to fitting unstructured data of varying dimensionality and when employing a variety of tensor network formats. Here, we show improved learning using the hierarchical Tucker method over the tensor-train in high-dimensional settings on a number of benchmark problems.

Abstract not provided.

The Multi-Fidelity Toolkit (MFTK) is a simulation tool being developed at Sandia National Laboratories for aerodynamic predictions of compressible flows over a range of physics fidelities and computational speeds. These models include the Reynolds-Averaged Navier–Stokes (RANS) equations, the Euler equations, and modified Newtonian aerodynamics (MNA) equations, and they can be invoked independently or coupled with hierarchical Kriging to interpolate between high-fidelity simulations using lower-fidelity data. However, as with any new simulation capability, verification and validation are necessary to gather credibility evidence. This work describes formal model validation with uncertainty considerations that leverages experimental data from the HIFiRE-1 wind tunnel tests. The geometry is a multi-conic shape that produces complex flow phenomena under hypersonic conditions. A thorough treatment of the validation comparison with prediction error and validation uncertainty is also presented.

A first-of-its-kind model calibration was performed using Sandia National Laboratories' high-throughput initiation (HTI) experiment for two types of vapor-deposited explosive films consisting of hexanitrostilbene (HNS) or pentaerythritol tetranitrate (PETN). These films exhibit prompt initiation, and they reach steady detonation at sub-millimeter length scales. Following prior work on HNS, we test the hypothesis of approximating these explosive films as fine-grained homogeneous solids with simple Arrhenius kinetics burn models. The model calibration process is described herein using a single-step as well as a two-step Arrhenius rate law, and it consists of systematic parameter sampling leading to a reduction in the model degrees of freedom. Multiple local minima are observed; results are given for seven different optimized parameter sets. Each model set is further evaluated in a two-dimensional simulation of the critical failure thickness for a sustained detonation. Overall, the two-step Arrhenius kinetics model captures the observed behavior for HNS; however, neither model produces a good fit to the PETN data. We hypothesize that the HTI results for PETN correspond to a heterogeneous response, owing to the smaller reaction zone of PETN compared to HNS (i.e., it does not homogenize the fine-grained hot spots as well). Future work should consider using the ignition and growth model for PETN, as well as other reactive burn models such as xHVRB, AWSD, PiSURF, and CREST.

Carbon sequestration is a growing field that requires subsurface monitoring for potential leakage of the sequestered fluids through the casing annulus. Sandia National Laboratories (SNL) is developing a smart collar system for downhole fluid monitoring during carbon sequestration. This technology is part of a collaboration between SNL, University of Texas at Austin (UT Austin) (project lead), California Institute of Technology (Caltech), and Research Triangle Institute (RTI) to obtain real-time monitoring of the movement of fluids in the subsurface through direct formation measurements. Caltech and RTI are developing millimeter-scale radio frequency identification (RFID) sensors that can sense carbon dioxide, pH, and methane. These sensors will be impervious to cement, and as such, can be mixed with cement and poured into the casing annulus. The sensors are powered and communicate via standard RFID protocol at 902-928 MHz. SNL is developing a smart collar system that wirelessly gathers RFID sensor data from the sensors embedded in the cement annulus and relays that data to the surface via a wired pipe that utilizes inductive coupling at the collar to transfer data through each segment of pipe. This system cannot transfer a direct current signal to power the smart collar, and therefore, both power and communications will be implemented using alternating current and electromagnetic signals at different frequencies. The complete system will be evaluated at UT Austin's Devine Test Site, which is a highly characterized and hydraulically fractured site. This is the second year of the three-year effort, and a review of SNL's progress on the design and implementation of the smart collar system is provided.

This work presents a high-speed laser-absorption-spectroscopy diagnostic capable of measuring temperature, pressure, and nitric oxide (NO) mole fraction in shock-heated air at a measurement rate of 500 kHz. This diagnostic was demonstrated in the High-Temperature Shock Tube (HST) facility at Sandia National Laboratories. The diagnostic utilizes a quantum-cascade laser to measure the absorbance spectra of two rovibrational transitions near 5.06 µm in the fundamental vibration bands (v" = 0 and 1) of NO in its ground electronic state (X2 Π1/2 ). Gas properties were determined using scanned-wavelength direct absorption and a recently established fitting method that utilizes a modified form of the time-domain molecular free-induction-decay signal (m-FID). This diagnostic was applied to acquire measurements in shock-heated air in the HST at temperatures ranging from approximately 2500 to 5500 K and pressures of 3 to 12 atm behind both incident and reflected shocks. The measurements agree well with the temperature predicted by NASA CEA and the pressure measured simultaneously using PCB pressure sensors. The measurements presented demonstrate that this diagnostic is capable of resolving the formation of NO in shock-heated air and the associated temperature change at the conditions studied.