Publications

Dynamic modeling and simulation will be used to provide an understanding of the interactions between various complex systems. This dynamic model is based on an enterprise architecture framework whereby complex, dynamic and non-linear interactions, particularly those involving the human, can be understood and analyzed. Our modeling approach will include a synthesis of top-down and bottom-up strategies. The top-down portion will analyze high-level, mandated guidance and trace its tenants down to individually identifiable activities at the worker-level. We will then model these activities through the provision of a discrete event task model emphasizing research-based human performance and cognitive workload principles (bottom-up). These principles are based on accepted theories of the interaction between cognitive workload and human error. Synthesizing these two approaches will demonstrate both the impact and effect of high-level mandated activities and aid analysts in their understanding of how, why and when these impacts help or possibly hinder humans at the worker level. Benefits of using this model, namely the ability to predict “what if” scenarios in real time will be discussed. The model will be tested across multiple domains to demonstrate the potential modeling approach and its application in future hazard analyses.

Composition of computational science applications into both ad hoc pipelines for analysis of collected or generated data and into well-defined and repeatable workflows is becoming increasingly popular. Meanwhile, dedicated high performance computing storage environments are rapidly becoming more diverse, with both significant amounts of non-volatile memory storage and mature parallel file systems available. At the same time, computational science codes are being coupled to data analysis tools which are not filesystem-oriented. In this paper, we describe how the FAODEL data management service can expose different available data storage options and mediate among them in both application- and FAODEL-directed ways. These capabilities allow applications to exploit their knowledge of the different types of data they may exchange during a workflow execution, and also provide FAODEL with mechanisms to proactively tune data storage behavior when appropriate. We describe the implementation of these capabilities in FAODEL and how they are used by applications, and present preliminary performance results demonstrating the potential benefits of our approach.

The Artificial Intelligence (AI) revolution foretold of during the 1960s is well underway in the second decade of the twenty first century. Its period of phenomenal growth likely lies ahead. AI-operated machines and technologies will extend the reach of Homo sapiens far beyond the biological constraints imposed by evolution: outwards further into deep space, as well as inwards into the nano-world of DNA sequences and relevant medical applications. And yet, we believe, there are crucial lessons that biology can offer that will enable a prosperous future for AI. For machines in general, and for AI's especially, operating over extended periods or in extreme environments will require energy usage orders of magnitudes more efficient than exists today. In many operational environments, energy sources will be constrained. The AI's design and function may be dependent upon the type of energy source, as well as its availability and accessibility. Any plans for AI devices operating in a challenging environment must begin with the question of how they are powered, where fuel is located, how energy is stored and made available to the machine, and how long the machine can operate on specific energy units. While one of the key advantages of AI use is to reduce the dimensionality of a complex problem, the fact remains that some energy is required for functionality. Hence, the materials and technologies that provide the needed energy represent a critical challenge toward future use scenarios of AI and should be integrated into their design. Here we look to the brain and other aspects of biology as inspiration for Biomimetic Research for Energy-efficient AI Designs (BREAD).

Atmospheric modeling with element-based high-order Galerkin methods presents a unique challenge to the conventional physics–dynamics coupling paradigm, due to the highly irregular distribution of nodes within an element and the distinct numerical characteristics of the Galerkin method. The conventional coupling procedure is to evaluate the physical parameterizations (physics) on the dynamical core grid. Evaluating the physics at the nodal points exacerbates numerical noise from the Galerkin method, enabling and amplifying local extrema at element boundaries. Grid imprinting may be substantially reduced through the introduction of an entirely separate, approximately isotropic finite-volume grid for evaluating the physics forcing. Integration of the spectral basis over the control volumes provides an area-average state to the physics, which is more representative of the state in the vicinity of the nodal points rather than the nodal point itself and is more consistent with the notion of a “large-scale state” required by conventional physics packages. This study documents the implementation of a quasi-equal-area physics grid into NCAR’s Community Atmosphere Model Spectral Element and is shown to be effective at mitigating grid imprinting in the solution. The physics grid is also appropriate for coupling to other components within the Community Earth System Model, since the coupler requires component fluxes to be defined on a finite-volume grid, and one can be certain that the fluxes on the physics grid are, indeed, volume averaged.

Communication networks have evolved to a level of sophistication that requires computer models and numerical simulations to understand and predict their behavior. A network simulator is a software that enables the network designer to model several components of a computer network such as nodes, routers, switches and links and events such as data transmissions and packet errors in order to obtain device and network level metrics. Network simulations, as many other numerical approximations that model complex systems, are subject to the specification of parameters and operative conditions of the system. Very often the full characterization of the system and their input is not possible, therefore Uncertainty Quantification (UQ) strategies need to be deployed to evaluate the statistics of its response and behavior. UQ techniques, despite the advancements in the last two decades, still suffer in the presence of a large number of uncertain variables and when the regularity of the systems response cannot be guaranteed. In this context, multifidelity approaches have gained popularity in the UQ community recently due to their flexibility and robustness with respect to these challenges. The main idea behind these techniques is to extract information from a limited number of high-fidelity model realizations and complement them with a much larger number of a set of lower fidelity evaluations. The final result is an estimator with a much lower variance, i.e. a more accurate and reliable estimator can be obtained. In this contribution we investigate the possibility to deploy multifidelity UQ strategies to computer network analysis. Two numerical configurations are studied based on a simplified network with one client and one server. Preliminary results for these tests suggest that multifidelity sampling techniques might be used as effective tools for UQ tools in network applications.

Compact heat exchangers for supercritical CO2 (sCO2) service are often designed with external, semi-circular headers. Their design is governed by the ASME Boiler & Pressure Vessel Code (BPVC) whose equations were typically derived by following Castigliano’s Theorems. However, there are no known validation experiments to support their claims of pressure rating or burst pressure predictions nor is there much information about how and where failures occur. This work includes high pressure bursting of three semicircular header prototypes for the validation of three aspects: (1) burst pressure predictions from the BPVC, (2) strain predictions from Finite Element Analysis (FEA), and (3) deformation from FEA. The header prototypes were designed with geometry and weld specifications from the BPVC Section VIII Division 1, a design pressure typical of sCO2 service of 3,900 psi (26.9 MPa), and were built with 316 SS. Repeating the test in triplicate allows for greater confidence in the experimental results and enables data averaging. Burst pressure predictions are compared with experimental results for accuracy assessment. The prototypes are analyzed to understand their failure mechanism and locations. Experimental strain and deformation measurements were obtained optically with Digital Image Correlation (DIC). This technique allows strain to be measured in two dimensions and even allows for deformation measurements, all without contacting the prototype. Eight cameras are used for full coverage of both headers on the prototypes. The rich data from this technique are an excellent validation source for FEA strain and deformation predictions. Experimental data and simulation predictions are compared to assess simulation accuracy.

A new cell-centered third-order entropy stable Weighted Essentially Non-Oscillatory (SS-WENO) finite difference scheme in multi-block domains is developed for compressible flows. This new scheme overcomes shortcomings of the conventional SSWENO finite difference scheme in multi-domain problems by incorporating non-dissipative Simultaneous Approximation Term (SAT) penalties into the construction of a dual flux. The stencil of the generalized dual flux allows for full stencil biasing across the interface while maintaining the nonlinear stability estimate. We demonstrate the shock capturing improvement across multi-block domain interfaces using the generalized SSWENO in comparison to the conventional entropy stable high-order finite difference with interface penalty in shock problems. Furthermore, we test the new scheme in multi-dimensional turbulent flow problems to assess the accuracy and stability of the multi-block domain formulation.

Two novel and challenging applications of high-frequency pressure-sensitive paint were attempted for ground testing at Sandia National Labs. Blast tube testing, typically used to assess the response of a system to an incident blast wave, was the first application. The paint was tested to show feasibility for supplementing traditional pressure instrumentation in the harsh outdoor environment. The primary challenge was the background illumination from sunlight and time-varying light contamination from the associated explosion. Optimal results were obtained in pre-dawn hours when sunlight contamination was absent; additional corrections must be made for the intensity of the explosive illumination. A separate application of the paint for acoustic testing was also explored to provide the spatial distribution of loading on systems that do not contain pressure instrumentation. In that case, the challenge was the extremely low level of pressure variations that the paint must resolve (120 dB). Initial testing indicated the paint technique merits further development for a larger scale reverberant chamber test with higher loading levels near 140 dB.

We describe new machine-learning-based methods to defeature CAD models for tetrahedral meshing. Using machine learning predictions of mesh quality for geometric features of a CAD model prior to meshing we can identify potential problem areas and improve meshing outcomes by presenting a prioritized list of suggested geometric operations to users. Our machine learning models are trained using a combination of geometric and topological features from the CAD model and local quality metrics for ground truth. We demonstrate a proof-of-concept implementation of the resulting work ow using Sandia's Cubit Geometry and Meshing Toolkit.

Abstract not provided.

The fluid injection into the subsurface perturbs the states of pore pressure and stress on the pre-existing faults, potentially causing earthquakes. In the multiphase flow system, the contrast of fluid and rock properties between different structures produces the changes in pressure gradients and subsequently stress fields. Assuming two-phase fluid flow (gas-water system) and poroelasticity, we simulate the three-layered formation including a basement fault, in which injection-induced pressure encounters the fault directly given injection scenarios. The single-phase poroelasticity model with the same setting is also conducted to evaluate the multiphase flow effects on poroelastic response of the fault to gas injection. Sensitivity tests are performed by varying the fault permeability. The presence of gaseous phase reduces the pressure buildup within the highly gas-saturated region, causing less Coulomb stress changes, whereas capillarity increases the pore pressure within the gas-water mixed region. Even though the gaseous plume does not approach the fault, the poroelastic stressing can affect the fault stability, potentially the earthquake occurrence.

For at least the last 20 years, many have tried to create a general resource management system to support interoperability across various concurrent libraries. The previous strategies all suffered from additional toolchain requirements, and/or a usage of a shared programing model that assumed it owned/controlled access to all resources available to the program. None of these techniques have achieved wide spread adoption. The ubiquity of OpenMP coupled with C++ developing a standard way to describe many different concurrent paradigms (C++23 executors) would allow OpenMP to assume the role of a general resource manager without requiring user code written directly in OpenMP. With a few added features such as the ability to use otherwise idle threads to execute tasks and to specify a task “width”, many interesting concurrent frameworks could be developed in native OpenMP and achieve high performance. Further, one could create concrete C++ OpenMP executors that enable support for general C++ executor based codes, which would allow Fortran, C, and C++ codes to use the same underlying concurrent framework when expressed as native OpenMP or using language specific features. Effectively, OpenMP would become the de facto solution for a problem that has long plagued the HPC community.

As clock speeds have stagnated, the number of cores in a node has been drastically increased to improve processor throughput. Most scalable system software was designed and developed for single-threaded environments. Multithreaded environments become increasingly prominent as application developers optimize their codes to leverage the full performance of the processor; however, these environments are incompatible with a number of assumptions that have driven scalable system software development. This paper will highlight a case study of this mismatch focusing on MPI message matching. MPI message matching has been designed and optimized for traditional serial execution. The reduced determinism in the order of MPI calls can significantly reduce the performance of MPI message matching, potentially overtaking time-per-iteration targets of many applications. Different proposed techniques attempt to address these issues and enable multithreaded MPI usage. These approaches highlight a number of tradeoffs that make adapting MPI message matching complex. This case study and its proposed solutions highlight a number of general concepts that need to be leveraged in the design of next generation scaleable system software.

The overall DOE R&D strategy for DPC disposition includes a significant effort directed toward consequence screening to determine if engineered solutions discussed above are needed. Work to develop injectable filler technology will continue. The disposal criticality control features approach, and zone loading, have not been investigated since the EPRI studies in 2008-2009. The utility of such measures would be maximized by implementing them soon. This ongoing study is motivated by comparative cost analysis [7] that showed the potential cost savings using the control rods/blades approach, compared to repackaging (comparing the two most technically mature options for DPC disposition and retaining the low-probability criticality screening objective) would be approximately $2 million per DPC.

Abstract not provided.

In this paper, we develop software for decomposing sparse tensors that is portable to and performant on a variety of multicore, manycore, and GPU computing architectures. The result is a single code whose performance matches optimized architecture-specific implementations. The key to a portable approach is to determine multiple levels of parallelism that can be mapped in different ways to different architectures, and we explain how to do this for the matricized tensor times Khatri-Rao product (MTTKRP), which is the key kernel in canonical polyadic tensor decomposition. Our implementation leverages the Kokkos framework, which enables a single code to achieve high performance across multiple architectures that differ in how they approach fine-grained parallelism. We also introduce a new construct for portable thread-local arrays, which we call compile-time polymorphic arrays. Not only are the specifics of our approaches and implementation interesting for tuning tensor computations, but they also provide a roadmap for developing other portable high-performance codes. As a last step in optimizing performance, we modify the MTTKRP algorithm itself to do a permuted traversal of tensor nonzeros to reduce atomic-write contention. We test the performance of our implementation on 16- and 68-core Intel CPUs and the K80 and P100 NVIDIA GPUs, showing that we are competitive with state-of-the-art architecture-specific codes while having the advantage of being able to run on a variety of architectures.

Anisotropy in the mechanical properties of rock is often attributed to layering or mineral texture. Here, results from a study on mode I fracturing are presented that examine the effect of layering and mineral orientation fracture toughness and roughness. Additively manufactured gypsum rock was created through 3D printing with bassanite/gypsum. The 3D printing process enabled control of the orientation of the mineral texture within the printed layers. Three-point bending (3PB) experiments were performed on the 3D printed rock with a central notch. Unlike cast gypsum, the 3D-printed gypsum exhibited ductile post-peak behavior in all cases. The experiments also showed that the mode I fracture toughness and surface roughness of the induced fracture depended on both the orientation of the bedding relative to the load and the orientation of the mineral texture relative to the layering. This study found that mineral texture orientation, chemical bond strength and layer orientation play dominant roles in the formation of mode I fractures. The uniqueness of the induced fracture roughness is a potential method for the assessment of bonding strengths in rock.

A long-term goal for superconductors is to increase the superconducting transition temperature, TC. In cuprates, TC depends strongly on the out-of-plane Cu-apical oxygen distance and the in-plane Cu-O distance, but there has been little attention paid to tuning them independently. Here, in simply grown, self-assembled, vertically aligned nanocomposite thin films of La2CuO4+δ + LaCuO3, by strongly increasing out-of-plane distances without reducing in-plane distances (three-dimensional strain engineering), we achieve superconductivity up to 50 K in the vertical interface regions, spaced ∼50 nm apart. No additional process to supply excess oxygen, e.g., by ozone or high-pressure oxygen annealing, was required, as is normally the case for plain La2CuO4+δ films. Our proof-of-concept work represents an entirely new approach to increasing TC in cuprates or other superconductors.

Tunable plasmonic structure at the nanometer scale presents enormous opportunities for various photonic devices. In this work, we present a hybrid plasmonic thin film platform: i.e., a vertically aligned Au nanopillar array grown inside a TiN matrix with controllable Au pillar density. Compared to single phase plasmonic materials, the presented tunable hybrid nanostructures attain optical flexibility including gradual tuning and anisotropic behavior of the complex dielectric function, resonant peak shifting and change of surface plasmon resonances (SPRs) in the UV-visible range, all confirmed by numerical simulations. The tailorable hybrid platform also demonstrates enhanced surface plasmon Raman response for Fourier-transform infrared spectroscopy (FTIR) and photoluminescence (PL) measurements, and presents great potentials as designable hybrid platforms for tunable optical-based chemical sensing applications.

Resilience is an imminent issue for next-generation platforms due to projected increases in soft/transient failures as part of the inherent trade-offs among performance, energy, and costs in system design. In this paper, we introduce a comprehensive approach to enabling application-level resilience in Asynchronous Many-Task (AMT) programming models with a focus on remedying Silent Data Corruption (SDC) that can often go undetected by the hardware and OS. Our approach makes it possible for the application programmer to declaratively express resilience attributes with minimal code changes, and to delegate the complexity of efficiently supporting resilience to our runtime system. We have created a prototype implementation of our approach as an extension to the Habanero C/C++ library (HClib), where different resilience techniques including task replay, task replication, algorithm-based fault tolerance (ABFT), and checkpointing are available. Our experimental results show that task replay incurs lower overhead than task replication when an appropriate error checking function is provided. Further, task replay matches the low overhead of ABFT. Our results also demonstrate the ability to combine different resilience schemes. To evaluate the effectiveness of our resilience mechanisms in the presence of errors, we injected synthetic errors at different error rates (1.0%, and 10.0%) and found modest increase in execution times. In summary, the results show that our approach supports efficient and scalable recovery, and that our approach can be used to influence the design of future AMT programming models and runtime systems that aim to integrate first-class support for user-level resilience.

Multiple fastener reduced-order models and fitting strategies are used on a multiaxial dataset and these models are further evaluated using a high-fidelity analysis model to demonstrate how well these strategies predict load-displacement behavior and failure. Two common reduced-order modeling approaches, the plug and spot weld, are calibrated, assessed, and compared to a more intensive approach – a “two-block” plug calibrated to multiple datasets. An optimization analysis workflow leveraging a genetic algorithm was exercised on a set of quasistatic test data where fasteners were pulled at angles from 0° to 90° in 15° increments to obtain material parameters for a fastener model that best capture the load-displacement behavior of the chosen datasets. The one-block plug is calibrated just to the tension data, the spot weld is calibrated to the tension (0°) and shear (90°), and the two-block plug is calibrated to all data available (0°-90°). These calibrations are further assessed by incorporating these models and modeling approaches into a high-fidelity analysis model of the test setup and comparing the load-displacement predictions to the raw test data.

Structural alloy corrosion is a major concern for the design and operation of supercritical carbon dioxide (sCO2) power cycles. Looking towards the future of sCO2 system development, the ability to measure real-time alloy corrosion would be invaluable to informing operation and maintenance of these systems. Sandia has recently explored methods available for in-situ alloy corrosion monitoring. Electrical resistance (ER) was chosen for initial tests due the operational simplicity and commercial availability. A series of long duration (>1000 hours) experiments have recently been completed at a range of temperatures (400-700oC) using ER probes made from four important structural alloys (C1010 Carbon Steel, 410ss, 304L, 316L) being considered for sCO2 systems. Results from these tests are presented, including correlations between the probe measured corrosion rate to that for witness coupons of the same alloys.

Measures of energy input and spatial energy distribution during laser powder bed fusion additive manufacturing have significant implications for the build quality of parts, specifically relating to formation of internal defects during processing. In this study, scanning electron microscopy was leveraged to investigate the effects of these distributions on the mechanical performance of parts manufactured using laser powder bed fusion as seen through the fracture surfaces resulting from uniaxial tensile testing. Variation in spatial energy density is shown to manifest in differences in defect morphology and mechanical properties. Computed tomography and scanning electron microscopy inspections revealed significant evidence of porosity acting as failure mechanisms in printed parts. These results establish an improved understanding of the effects of spatial energy distributions in laser powder bed fusion on mechanical performance.

Researchers have been extensively studying wide-bandgap (WBG) semiconductor materials such as gallium nitride (GaN) with an aim to accomplish an improvement in size, weight, and power (SWaP) of power electronics beyond current devices based on silicon (Si). However, the increased operating power densities and reduced areal footprints of WBG device technologies result in significant levels of self-heating that can ultimately restrict device operation through performance degradation, reliability issues, and failure. Typically, self-heating in WBG devices is studied using a single measurement technique while operating the device under steady-state direct current (DC) measurement conditions. However, for switching applications, this steady-state thermal characterization may lose significance since high power dissipation occurs during fast transient switching events. Therefore, it can be useful to probe the WBG devices under transient measurement conditions in order to better understand the thermal dynamics of these systems in practical applications. In this work, the transient thermal dynamics of an AlGaN/GaN high electron mobility transistor (HEMT) were studied using thermoreflectance thermal imaging and Raman thermometry. Also, the proper use of iterative pulsed measurement schemes such as thermoreflectance thermal imaging to determine the steady-state operating temperature of devices is discussed. These studies are followed with subsequent transient thermal characterization to accurately probe the self-heating from steady-state down to sub-microsecond pulse conditions using both thermoreflectance thermal imaging and Raman thermometry with temporal resolutions down to 15 ns.

Drinking water utilities use booster stations to maintain chlorine residuals throughout water distribution systems. Booster stations could also be used as part of an emergency response plan to minimize health risks in the event of an unintentional or malicious contamination incident. The benefit of booster stations for emergency response depends on several factors, including the reaction between chlorine and an unknown contaminant species, the fate and transport of the contaminant in the water distribution system, and the time delay between detection and initiation of boosted levels of chlorine. This paper takes these aspects into account and proposes a mixed-integer linear program formulation for optimizing the placement of booster stations for emergency response. A case study is used to explore the ability of optimally placed booster stations to reduce the impact of contamination in water distribution systems.

Sandia National Laboratories was tasked by the United States Army Recovered Chemical Materiel Directorate with evaluating the fitness of the Transportable Detonation Chamber for use in demilitarization of chemical munitions. The chamber was instrumented with strain, pressure, and acceleration sensors to study its behavior during explosive tests ranging from 1.25 to 20 lb of explosive charge weight. The structural response of the chamber and techniques recommended by the manufacturer- use of water bags and sand-filled walls-were assessed. Through this testing, it was found that the two techniques did not significantly affect the chamber's response. It was also discovered that the structural integrity of the chamber (and, therefore, its suitability for use with chemical agents) was compromised, as some welds failed. Sandia does not recommend using this vessel for chemical munition demilitarization. This chamber is suitable, however, for demilitarization of conventional munitions, in which fragments and overpressure are the primary concern.

A Compressive Sensing Snapshot Imaging Spectrometer (CSSIS) and its performance are described. The number of spectral bins recorded in a traditional tiled array spectrometer is limited to the number of filters. By properly designing the filters and leveraging compressive sensing techniques, more spectral bins can be reconstructed. Simulation results indicate that closely-spaced spectral sources that are not resolved with a traditional spectrometer can be resolved with the CSSIS. The nature of the filters used in the CSSIS enable higher signal-to-noise ratios in measured signals. The filters are spectrally broad relative to narrow-line filters used in traditional systems, and hence more light reaches the imaging sensor. This enables the CSSIS to outperform a traditional system in a classification task in the presence of noise. Simulation results on classifying in the compressive domain are shown. This obviates the need for the computationally-intensive spectral reconstruction algorithm.

The ability to estimate a range of plausible parameter values, based on experimental data, is a critical aspect in process model validation and design optimization. In this paper, a Python software package is described that allows for model-based parameter estimation along with characterization of the uncertainty associated with the estimates. The software, called parmest, is available within the Pyomo open-source software project as a third-party contribution. The software includes options to obtain confidence regions that are based on single or multi-variate distributions, compute likelihood ratios, use bootstrap resampling in estimation, and make use of parallel processing capabilities.

There is a need for efficient optimization strategies to efficiently solve large-scale, nonlinear optimization problems. Many problem classes, including design under uncertainty are inherently structured and can be accelerated with decomposition approaches. This paper describes a second-order multiplier update for the alternating direction method of multipliers (ADMM) to solve nonlinear stochastic programming problems. We exploit connections between ADMM and the Schur-complement decomposition to derive an accelerated version of ADMM. Specifically, we study the effectiveness of performing a Newton-Raphson algorithm to compute multiplier estimates for the method of multipliers (MM). We interpret ADMM as a decomposable version of MM and propose modifications to the multiplier update of the standard ADMM scheme based on improvements observed in MM. The modifications to the ADMM algorithm seek to accelerate solutions of optimization problems for design under uncertainty and the numerical effectiveness of the approaches is demonstrated on a set of ten stochastic programming problems. Practical strategies for improving computational performance are discussed along with comparisons between the algorithms. We observe that the second-order update achieves convergence in fewer unconstrained minimizations for MM on general nonlinear problems. In the case of ADMM, the second-order update reduces significantly the number of subproblem solves for convex quadratic programs (QPs).

In the field of semiconductor quantum dot spin qubits, there is growing interest in leveraging the unique properties of hole-carrier systems and their intrinsically strong spin-orbit coupling to engineer novel qubits. Recent advances in semiconductor heterostructure growth have made available high quality, undoped Ge/SiGe quantum wells, consisting of a pure strained Ge layer flanked by Ge-rich SiGe layers above and below. These quantum wells feature heavy hole carriers and a cubic Rashba-type spin-orbit interaction. Here, we describe progress toward realizing spin qubits in this platform, including development of multi-metal-layer gated device architectures, device tuning protocols, and charge-sensing capabilities. Iterative improvement of a three-layer metal gate architecture has significantly enhanced device performance over that achieved using an earlier single-layer gate design. We discuss ongoing, simulation-informed work to fine-tune the device geometry, as well as efforts toward a single-spin qubit demonstration.

Camera-based imaging methods were evaluated to quantify both particle and convective heat losses from the aperture of a high-temperature particle receiver. A bench-scale model of a field-tested on-sun particle receiver was built, and particle velocities and temperatures were recorded using the small-scale model. Particles heated to over 700 °C in a furnace were released from a slot aperture and allowed to fall through a region that was imaged by the cameras. Particle-image, particle-tracking, and image-correlation velocimetry methods were compared against one another to determine the best method to obtain particle velocities. A high-speed infrared camera was used to evaluate particle temperatures, and a model was developed to determine particle and convective heat losses. In addition, particle sampling instruments were deployed during on-sun field tests of the particle receiver to determine if small particles were being generated that can pose an inhalation hazard. Results showed that while there were some recordable emissions during the tests, the measured particle concentrations were much lower than the acceptable health standard of 15 mg/m3. Additional bench-scale tests were performed to quantify the formation of particles during continuous shaking and dropping of the particles. Continuous formation of small particles in two size ranges (< ~1 microns and between ~8 - 10 microns) were observed due to deagglomeration and mechanical fracturing, respectively, during particle collisions.

Solid particle receivers provide an opportunity to run concentrating solar tower receivers at higher temperatures and increased overall system efficiencies. The design of the bins used for storing and managing the flow of particles creates engineering challenges in minimizing thermomechanical stress and heat loss. An optimization study of mechanical stress and heat loss was performed at the National Solar Thermal Test Facility at Sandia National Laboratories to determine the geometry of the hot particle storage hopper for a 1 MWt pilot plant facility. Modeling of heat loss was performed on hopper designs with a range of geometric parameters with the goal of providing uniform mass flow of bulk solids with no clogging, minimizing heat loss, and reducing thermomechanical stresses. The heat loss calculation included an analysis of the particle temperatures using a thermal resistance network that included the insulation and hopper. A plot of the total heat loss as a function of geometry and required thicknesses to accommodate thermomechanical stresses revealed suitable designs. In addition to the geometries related to flow type and mechanical stress, this study characterized flow related properties of CARBO HSP 40/70 and Accucast ID50-K in contact with refractory insulation. This insulation internally lines the hopper to prevent heat loss and allow for low cost structural materials to be used for bin construction. The wall friction angle, effective angle of friction, and cohesive strength of the bulk solid were variables that were determined from empirical analysis of the particles at temperatures up to 600°C.

In modern scientific analyses, physical experiments are often supplemented with computational modeling and simulation. This is especially true in the nuclear power industry, where experiments are prohibitively expensive, or impossible, due to extreme scales, high temperatures, high pressures, and the presence of radiation. To qualify these computational tools, it is necessary to perform software quality assurance, verification, validation, and uncertainty quantification. As part of this broad process, the uncertainty of empirically derived models must be quantified. In this work, three commonly used thermal hydraulic models are calibrated to experimental data. The empirical equations are used to determine single phase friction factor in smooth tubes, single phase heat transfer coefficient for forced convection, and the transfer of mass between two phases. Bayesian calibration methods are used to estimate the posterior distribution of the parameters given the experimental data. In cases where it is appropriate, mixed-effects hierarchical calibration methods are utilized. The analyses presented in this work result in justified and reproducible joint parameter distributions which can be used in future uncertainty analysis of nuclear thermal hydraulic codes. When using these joint distributions, uncertainty in the output will be lower than traditional methods of determining parameter uncertainty. The lower uncertainties are more representative of the state of knowledge for the phenomena analyzed in this work.

This paper presents Proteus, a framework for conducting rapid, emulation-based analysis of distributed ledger technologies (DLTs) using FIREWHEEL, an orchestration tool that assists a user in building, controlling, observing, and analyzing realistic experiments of distributed systems. Proteus is designed to support any DLT that has some form of a “transaction” and which operates on a peer-to-peer network layer. Proteus provides a framework for an investigator to set up a network of nodes, execute rich agent-driven behaviors, and extract run-time observations. Proteus relies on common features of DLTs to define agent-driven scenarios in a DLT-agnostic way allowing for those scenarios to be executed against different DLTs. We demonstrate the utility of using Proteus by executing a 51% attack on an emulated Ethereum network containing 2000 nodes.

The use of solid particles as a heat-transfer fluid and thermal storage media for concentrating solar power is a promising candidate for meeting levelized cost of electricity (LCOE) targets for next-generation CSP concepts. Meeting these cost targets for a given system concept will require optimization of the particle heat-transfer fluid with simultaneous consideration of all system components and operating conditions. This paper explores the trade-offs in system operating conditions and particle thermophysical properties on the levelized cost of electricity through parametric analysis. A steady-state modeling methodology for design point simulations dispatched against typical meteorological year (TMY) data is presented, which includes computationally efficient submodels of a falling particle receiver, moving packed-bed heat exchanger, storage bin, particle lift, and recompression supercritical CO2 (sCO2) cycle. The components selected for the baseline system configuration presents the most near-term realization of a particle-based CSP system that has been developed to date. However, the methodology could be extended to consider alternative particle receiver and heat exchanger concepts. The detailed system-level model coupled to component cost models is capable of propagating component design and performance information directly into the plant performance and economics. The system-level model is used to investigate how the levelized cost of electricity varies with changes in particle absorptivity, hot storage bin temperature, heat exchanger approach temperature, and sCO2 cycle operating parameters. Trade-offs in system capital cost and solar-to-electric efficiency due to changes in the size of the heliostat field, storage bins, primary heat exchanger, and receiver efficiency are observed. Optimal system operating conditions are reported, which approach levelized costs of electricity of $0.06 kWe-1hr-1

One of the core missions of the Department of Energy (DOE) is to move beyond current high performance computing (HPC) capabilities toward a capable exascale computing ecosystem that accelerates scientific discovery and addresses critical challenges in energy and national security. The very nature of this mission has drawn a wide range of talented and successful scientists to work together in new ways to push beyond the status-quo toward this goal. For many scientists, their past success was achieved through efficient and agile collaboration within small trusted teams that rapidly innovate, prototype, and deliver. Thus, a key challenge for the ECP (Exascale Computing Project) is to scale this efficiency and innovation from small teams to aggregate teams of teams. While scaling agile collaboration from small teams to teams of teams may seem like a trivial transition, the path to exascale introduces significant uncertainty in HPC scientific software development for future modeling and simulation, and can cause unforeseen disruptions or inefficiencies that impede organizational productivity and innovation critical to achieving an integrated exascale vision. This paper identifies key challenges in scaling to a team of teams approach and recommends strategies for addressing them. The scientific community will take away lessons learned and recommended best practices from examples for enhancing productivity and innovation at scale for immediate use in modeling and simulation software engineering projects and programs.

International nuclear safeguards inspectors are tasked with verifying that nuclear materials in facilities around the world are not misused or diverted from peaceful purposes. They must conduct detailed inspections in complex, information-rich environments, but there has been relatively little research into the cognitive aspects of their jobs. We posit that the speed and accuracy of the inspectors can be supported and improved by designing the materials they take into the field such that the information is optimized to meet their cognitive needs. Many in-field inspection activities involve comparing inventory or shipping records to other records or to physical items inside of a nuclear facility. The organization and presentation of the records that the inspectors bring into the field with them could have a substantial impact on the ease or difficulty of these comparison tasks. In this paper, we present a series of mock inspection activities in which we manipulated the formatting of the inspectors’ records. We used behavioral and eye tracking metrics to assess the impact of the different types of formatting on the participants’ performance on the inspection tasks. The results of these experiments show that matching the presentation of the records to the cognitive demands of the task led to substantially faster task completion.

Vulnerability analysts protecting software lack adequate tools for understanding data flow in binaries. We present a case study in which we used human factors methods to develop a taxonomy for understanding data flow and the visual representations needed to support decision making for binary vulnerability analysis. Using an iterative process, we refined and evaluated the taxonomy by generating three different data flow visualizations for small binaries, trained an analyst to use these visualizations, and tested the utility of the visualizations for answering data flow questions. Throughout the process and with minimal training, analysts were able to use the visualizations to understand data flow related to security assessment. Our results indicate that the data flow taxonomy is promising as a mechanism for improving analyst understanding of data flow in binaries and for supporting efficient decision making during analysis.

The V26 containment vessel was procured by the Project Manager, Non-Stockpile Chemical Materiel (PMNSCM) for use on the Phase-2 Explosive Destruction Systems. The vessel was fabricated under Code Case 2564 of the ASME Boiler and Pressure Vessel Code, which provides rules for the design of impulsively loaded vessels. The explosive rating for the vessel, based on the Code Case, is nine (9) pounds TNT-equivalent for up to 637 detonations, limited only by fatigue crack growth calculations initiated from a minimum detectable crack depth. The vessel consists of a cylindrical cup, a flat cover or door, and clamps to secure the door. The vessel is sealed with a metal gasket. The body is a deep cylindrical cup machined from a 316 stainless steel forging. The door is also machined from a 316 stainless steel forging. The closure clamps are secured with four 17-4 PH steel threaded rods with 4140 alloy steel threadednuts on one end and hydraulic nuts on the other. A flange with four high-voltage electrical feedthroughs is bolted to the door and sealed with a small metal gasket. These feedthroughs conduct the firing signals for the high-voltage Exploding Bridge-wire detonators. Small blast plates on the inside of the door protect fluidic components and electrical feedthroughs. A large blast plate provides additional protection. Both vessel door and feedthrough flange employ O-ring seals outside the metal seals in order to provide a mechanism for helium leak checks of the volume just outside the metal seal surface before and after detonation. In previous papers (References 2 and 3), the authors describe results from testing of the vessel body and ends under qualification loads, determining the effective TNT equivalency of Composition C4 (EDS Containment Vessel TNT Equivalence Testing) and analyzing the effects of distributed explosive charges versus unitary charges (EDS Containment Vessel Explosive Test and Analysis). In addition to measurements made on the vessel body and ends as reported previously, bulk motion and deformation of the door and clamping system was made. Strain gauges were positioned at various locations on the inner and outer surface of the clamping system and on the vessel door surface. Digital Image Correlation was employed during both hydrostatic testing and dynamic testing under full-load explosive detonation to determine bulk and bending motion of the door relative to the vessel body and clamping system. Some limited hydrocode and finite element code analysis was performed on the clamping system for comparison. The purpose of this analysis was to determine the likelihood of a change in the static sealing efficacy of the metal clamping system and to evaluate the possibility of dynamic burping of vessel contents during detonation. Those results will be reported in this paper.

The V26 containment vessel was procured by the Project Manager, Non-Stockpile Chemical Materiel (PMNSCM) for use on the Phase-2 Explosive Destruction Systems. The vessel was fabricated under Code Case 2564 of the ASME Boiler and Pressure Vessel Code, which provides rules for the design of impulsively loaded vessels. The explosive rating for the vessel, based on the Code Case, is nine (9) pounds TNT-equivalent for up to 637 detonations, limited only by fatigue crack growth calculations initiated from a minimum detectable crack depth. The vessel consists of a cylindrical cup, a flat cover or door, and clamps to secure the door. The vessel is sealed with a metal gasket. The body is a deep cylindrical cup machined from a 316 stainless steel forging. The door is also machined from a 316 stainless steel forging. The closure clamps are secured with four 17-4 PH steel threaded rods with 4140 alloy steel threadednuts on one end and hydraulic nuts on the other. A flange with four high-voltage electrical feedthroughs is bolted to the door and sealed with a small metal gasket. These feedthroughs conduct the firing signals for the high-voltage Exploding Bridge-wire detonators. Small blast plates on the inside of the door protect fluidic components and electrical feedthroughs. A large blast plate provides additional protection. Both vessel door and feedthrough flange employ O-ring seals outside the metal seals in order to provide a mechanism for helium leak checks of the volume just outside the metal seal surface before and after detonation. In previous papers (References 2 and 3), the authors describe results from testing of the vessel body and ends under qualification loads, determining the effective TNT equivalency of Composition C4 (EDS Containment Vessel TNT Equivalence Testing) and analyzing the effects of distributed explosive charges versus unitary charges (EDS Containment Vessel Explosive Test and Analysis). In addition to measurements made on the vessel body and ends as reported previously, bulk motion and deformation of the door and clamping system was made. Strain gauges were positioned at various locations on the inner and outer surface of the clamping system and on the vessel door surface. Digital Image Correlation was employed during both hydrostatic testing and dynamic testing under full-load explosive detonation to determine bulk and bending motion of the door relative to the vessel body and clamping system. Some limited hydrocode and finite element code analysis was performed on the clamping system for comparison. The purpose of this analysis was to determine the likelihood of a change in the static sealing efficacy of the metal clamping system and to evaluate the possibility of dynamic burping of vessel contents during detonation. Those results will be reported in this paper.

In the field of semiconductor quantum dot spin qubits, there is growing interest in leveraging the unique properties of hole-carrier systems and their intrinsically strong spin-orbit coupling to engineer novel qubits. Recent advances in semiconductor heterostructure growth have made available high quality, undoped Ge/SiGe quantum wells, consisting of a pure strained Ge layer flanked by Ge-rich SiGe layers above and below. These quantum wells feature heavy hole carriers and a cubic Rashba-type spin-orbit interaction. Here, we describe progress toward realizing spin qubits in this platform, including development of multi-metal-layer gated device architectures, device tuning protocols, and charge-sensing capabilities. Iterative improvement of a three-layer metal gate architecture has significantly enhanced device performance over that achieved using an earlier single-layer gate design. We discuss ongoing, simulation-informed work to fine-tune the device geometry, as well as efforts toward a single-spin qubit demonstration.

Performance of terahertz (THz) photoconductive devices, including detectors and emitters, has been improved recently by means of plasmonic nanoantennae and gratings. However, plasmonic nanostructures introduce Ohmic losses, which limit gains in device performance. In this presentation, we discuss an alternative approach, which eliminates the problem of Ohmic losses. We use all-dielectric photoconductive metasurfaces as the active region in THz switches to improve their efficiency. In particular, we discuss two approaches to realize perfect optical absorption in a thin photoconductive layer without introducing metallic elements. In addition to providing perfect optical absorption, the photoconductive channel based on all-dielectric metasurface allows us to engineer desired electrical properties, specifically, fast and efficient conductivity switching with very high contrast. This approach thus promises a new generation of sensitive and efficient THz photoconductive detectors. Here we demonstrate and discuss performance of two practical THz photoconductive detectors with integrated all-dielectric metasurfaces.

In this article, we present an approach to streaming collection of application performance data. Practical application performance tuning and troubleshooting in production high-performance computing (HPC) environments requires an understanding of how applications interact with the platform, including (but not limited to) parallel programming libraries such as Message Passing Interface (MPI). Several profiling and tracing tools exist that collect heavy runtime data traces either in memory (released only at application exit) or on a file system (imposing an I/O load that may interfere with the performance being measured). Although these approaches are beneficial in development stages and post-run analysis, a systemwide and low-overhead method is required to monitor deployed applications continuously. This method must be able to collect information at both the application and system levels to yield a complete performance picture. In our approach, an application profiler collects application event counters. A sampler uses an efficient inter-process communication method to periodically extract the application counters and stream them into an infrastructure for performance data collection. We implement a tool-set based on our approach and integrate it with the Lightweight Distributed Metric Service (LDMS) system, a monitoring system used on large-scale computational platforms. LDMS provides the infrastructure to create and gather streams of performance data in a low overhead manner. We demonstrate our approach using applications implemented with MPI, as it is one of the most common standards for the development of large-scale scientific applications. We utilize our tool-set to study the impact of our approach on an open source HPC application, Nalu. Our tool-set enables us to efficiently identify patterns in the behavior of the application without source-level knowledge. We leverage LDMS to collect system-level performance data and explore the correlation between the system and application events. Also, we demonstrate how our tool-set can help detect anomalies with a low latency. We run tests on two different architectures: a system enabled with Intel Xeon Phi and another system equipped with Intel Xeon processor. Our overhead study shows our method imposes at most 0.5% CPU usage overhead on the application in realistic deployment scenarios.

Spectral matched filtering and its variants (e.g. Adaptive Coherence Estimator or ACE) rely on strong assumptions about target and background distributions. For instance, ACE assumes a Gaussian distribution of background and additive target model. In practice, natural spectral variation, due to effects such as material Bidirectional Reflectance Distribution Function, non-linear mixing with surrounding materials, or material impurities, degrade the performance of matched filter techniques and require an ever-increasing library of target templates measured under different conditions. In this work, we employ the contrastive loss function and paired neural networks to create data-driven target detectors that do not rely on strong assumptions about target and background distribution. Furthermore, by matching spectra to templates in a highly nonlinear fashion via neural networks, our target detectors exhibit improved performance and greater resiliency to natural spectral variation; this performance improvement comes with no increase in target template library size. We evaluate and compare our paired neural network detector to matched filter-based target detectors on a synthetic hyperspectral scene and the well-known Indian Pines AVIRIS hyperspectral image.

A 1 MWt falling particle receiver prototype was designed, built and is being evaluated at Sandia National Laboratories, National Solar Thermal Test Facility (NSTTF). The current prototype has a 1 m2 aperture facing the north field. The current aperture configuration is susceptible to heat and particle losses through the receiver aperture. Several options are being considered for the next design iteration to reduce the risk of heat and particle losses, in addition to improving the receiver efficiency to target levels of ~90%. One option is to cover the receiver aperture with a highly durable and transmissive material such as quartz glass. Quartz glass has high transmittance for wavelengths less than 2.5 microns and low transmittance for wavelengths greater than 2.5 microns to help trap the heat inside the receiver. To evaluate the receiver optical performance, ray-tracing models were set up for several different aperture cover configurations. The falling particle receiver is modeled as a box with a 1 m2 aperture on the north side wall. The box dimensions are 1.57 m wide x 1.77 m tall x 1.67 m deep. The walls are composed of RSLE material modeled as Lambertian surfaces with reflectance of either 0.9 for the pristine condition or 0.5 for soiled walls. The quartz half-shell tubes are 1.46 m long with 105 mm and 110 mm inner and outer diameters, respectively. The half-shell tubes are arranged vertically and slant forward at the top by 30 degrees. Four configurations were considered: concave side of the half-shells facing away from the receiver aperture with (1) no spacing and (2) high spacing between the tubes, and concave side of the half-shells facing the aperture with (3) no spacing and (4) high spacing between the tubes. The particle curtain, in the first modeling approach, is modeled as a diffuse surface with transmittance, reflectance, and absorptance values, which are based on estimates from previous experiments for varying particle flow rates. The incident radiation is from the full NSTTF heliostat field with a single aimpoint at the center of the receiver aperture. The direct incident rays and reflected and scattered rays off the internal receiver surfaces are recorded on the internal walls and particle curtain surfaces as net incident irradiance. The net incident irradiances on the internal walls and particle curtain for the different aperture cover configuration are compared to the baseline configuration. In all cases, just from optical performance alone, the net incident irradiance is reduced from the baseline. However, it is expected that the quartz half-shells will reduce the convective and thermal radiation losses through the aperture. These ray-tracing results will be used as boundary conditions in computational fluid dynamics (CFD) analyses to determine the net receiver efficiency and optimal configuration for the quartz half-shells that minimize heat losses and maximize thermal efficiency.

Designs of conventional heliostats have been varied to reduce cost, improve optical performance or both. In one case, reflective mirror area on heliostats has been increased with the goal of reducing the number of pedestals and drives and consequently reducing the cost on those components. The larger reflective areas, however, increase torques due to larger mirror weights and wind loads. Higher cost heavy-duty motors and drives must be used, which negatively impact any economic gains. To improve on optical performance, the opposite may be true where the mirror reflective areas are reduced for better control of the heliostat pointing and tracking. For smaller heliostats, gravity and wind loads are reduced, but many more heliostats must be added to provide sufficient solar flux to the receiver. For conventional heliostats, there seems to be no clear cost advantage of one heliostat design over other designs. The advantage of ganged heliostats is the pedestal and tracking motors are shared between multiple heliostats, thus can significantly reduce the cost on those components. In this paper, a new concept of cable-suspended tensile ganged heliostats is introduced, preliminary analysis is performed for optical performance and incorporated into a 10 MW conceptual power tower plant where it was compared to the performance of a baseline plant with a conventional radially staggered heliostat field. The baseline plant uses conventional heliostats and the layout optimized in System Advisor Model (SAM) tool. The ganged heliostats are suspended on two guide cables. The cables are attached to rotations arms which are anchored to end posts. The layout was optimized offline and then transferred to SAM for performance evaluation. In the initial modeling of the tensile ganged heliostats for a 10 MW power tower plant, equal heliostat spacing along the guide cables was assumed, which as suspected leads to high shading and blocking losses. The goal was then to optimize the heliostat spacing such that annual shading and blocking losses are minimized. After adjusting the spacing on tensile ganged heliostats for minimal blocking losses, the annual block/shading efficiency was greater than 90% and annual optical efficiency of the field became comparable to the conventional field at slightly above 60%.

Various ganged heliostat concepts have been proposed in the past. The attractive aspect of ganged heliostat concepts is multiple heliostats are grouped so that pedestals, tracking drives, and other components can be shared, thus reducing the number of components. The reduction in the number of components is thought to significantly reduce cost. However, since the drives and tracking mechanisms are shared, accurate on-sun tracking of grouped heliostats becomes challenging because the angular degrees-of-freedom are now limited for the multiple number of combined heliostats. In this paper, the preliminary evaluation of the on-sun tracking of a novel tensile-based cable suspended ganged heliostat concept is provided. In this concept, multiple heliostats are attached to two guide cables. The cables are attached to rotation spreader arms which are anchored to end posts on two ends. The guide cables form a catenary which makes tracking on-sun interesting and challenging. Tracking is performed by rotating the end plates that the two cables are attached to and rotating the individual heliostats in one axis. An additional degree-of-freedom can be added by differentially tensioning the two cables, but this may be challenging to do in practice. Manual on-sun tracking was demonstrated on small-scale prototypes. The rotation arms were coarsely controlled with linear actuators, and the individual heliostats were hand-adjusted in local pitch angle and locked in place with set screws. The coarse angle adjustments showed the tracking accuracy was 3-4 milli-radians. However, with better angle control mechanisms the tracking accuracy can be drastically improved. In this paper, we provide tracking data that was collected for a day, which showed feasibility for automated on-sun tracking. The next steps are to implement better angle control mechanisms and develop tracking algorithms so that the ganged heliostats can automatically track.

A set of algorithms based on characteristic discontinuous Galerkin methods is presented for tracer transport on the sphere. The algorithms are designed to reduce message passing interface communication volume per unit of simulated time relative to current methods generally, and to the spectral element scheme employed by the U.S. Department of Energy's Exascale Earth System Model (E3SM) specifically. Two methods are developed to enforce discrete mass conservation when the transport schemes are coupled to a separate dynamics solver; constrained transport and Jacobian-combined transport. A communication-efficient method is introduced to enforce tracer consistency between the transport scheme and dynamics solver; this method also provides the transport scheme's shape preservation capability. A subset of the algorithms derived here is implemented in E3SM and shown to improve transport performance by a factor of 2.2 for the model's standard configuration with 40 tracers at the strong scaling limit of one element per core.

Power production of the turbines at the Department of Energy/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility located at the Texas Tech University’s National Wind Institute Research Center was measured experimentally and simulated for neutral atmospheric boundary layer operating conditions. Two V27 wind turbines were aligned in series with the dominant wind direction, and the upwind turbine was yawed to investigate the impact of wake steering on the downwind turbine. Two conditions were investigated, including that of the leading turbine operating alone and both turbines operating in series. The field measurements include meteorological evaluation tower (MET) data and light detection and ranging (lidar) data. Computations were performed by coupling large eddy simulations (LES) in the three-dimensional, transient code Nalu-Wind with engineering actuator line models of the turbines from OpenFAST. The simulations consist of a coarse precursor without the turbines to set up an atmospheric boundary layer inflow followed by a simulation with refinement near the turbines. Good agreement between simulations and field data are shown. These results demonstrate that Nalu-Wind holds the promise for the prediction of wind plant power and loads for a range of yaw conditions.

In its simplest implementation, patent-protected AeroMINE consists of two opposing foils, where a low-pressure zone is generated between them. The low pressure draws fluid through orifices in the foil surfaces from plenums inside the foils. The inner plenums are connected to ambient pressure. If an internal turbine-generator is placed in the path of the flow to the plenums, energy can be extracted. The fluid transports the energy through the plenums, and the turbine-generator can be located at ground level, inside a controlled environment for easy access and to avoid inclement weather conditions or harsh environments. This contained internal turbine-generator has the only moving parts in the system, isolated from people, birds and other wildlife. AeroMINEs could be used in distributed-wind energy settings, where the stationary foil pairs are located on warehouse rooftops, for example. Flow created by several such foil pairs could be combined to drive a common turbine-generator.

The aim of this study is to determine whether multiple U.S. Navy autonomous underwater vehicles (AUVs) could be supported using a small, heaving wave energy converter (WEC). The U.S. Navy operates numerous AUVs that need to be charged periodically onshore or onboard a support ship. Ocean waves provide a vast source of energy that can be converted into electricity using a wave energy converter and stored using a conventional battery. The Navy would benefit from the development of a wave energy converter that could store electrical power and autonomously charge its AUVs offshore. A feasibility analysis is required to ensure that the WEC could support the energy needs of multiple AUVs, remain covert, and offer a strategic military advantage. This paper investigates the Navy's power demands for AUVs and decides whether or not these demands could be met utilizing various measures of WEC efficiency. Wave data from a potential geographic region is analyzed to determine optimal locations for the converter in order to meet the Navy's power demands and mission set.

The recent paradigm shift introduced by the Internet of Things (IoT) has brought embedded systems into focus as a target for both security analysts and malicious adversaries. Typified by their lack of standardized hardware, diverse software, and opaque functionality, IoT devices present unique challenges to security analysts due to the tight coupling between their firmware and the hardware for which it was designed. In order to take advantage of modern program analysis techniques, such as fuzzing or symbolic execution, with any kind of scale or depth, analysts must have the ability to execute firmware code in emulated (or virtualized) environments. However, these emulation environments are rarely available and are cumbersome to create through manual reverse engineering, greatly limiting the analysis of binary firmware. In this work, we explore the problem of firmware re-hosting, the process by which firmware is migrated from its original hardware environment into a virtualized one. We show that an approach capable of creating virtual, interactive environments in an automated manner is a necessity to enable firmware analysis at scale. We present the first proof-of-concept system aiming to achieve this goal, called PRETENDER, which uses observations of the interactions between the original hardware and the firmware to automatically create models of peripherals, and allows for the execution of the firmware in a fully-emulated environment. Unlike previous approaches, these models are interactive, stateful, and transferable, meaning they are designed to allow the program to receive and process new input, a requirement of many analyses. We demonstrate our approach on multiple hardware platforms and firmware samples, and show that the models are flexible enough to allow for virtualized code execution, the exploration of new code paths, and the identification of security vulnerabilities.

We experimentally demonstrate simultaneous generation of second-, third-, fourthharmonic, sum-frequency, four-wave mixing and six-wave mixing processes in III-V semiconductor metasurfaces and show how to tailor second harmonic generation to zerodiffraction order via crystal orientation.

This chapter discusses the motivation for the use of Ultra-Wide-Bandgap Aluminum Gallium Nitride semiconductors for power switching and radio-frequency applications. A review of the relevant figures of merit for both vertical and lateral power switching devices, as well as lateral radio-frequency devices, is presented, demonstrating the potential superior performance of these devices relative to Gallium Nitride. Additionally, representative results from the literature for each device type are reviewed, highlighting recent progress as well as areas for further research.

We investigate deep neural networks to reconstruct and classify hyperspectral images from compressive sensing measurements. Hyperspectral sensors provide detailed spectral information to differentiate materials. However, traditional imagers require scanning to acquire spatial and spectral information, which increases collection time. Compressive sensing is a technique to encode signals into fewer measurements. It can speed acquisition time, but the reconstruction can be computationally intensive. First we describe multilayer perceptrons to reconstruct compressive hyperspectral images. Then we compare two different inputs to machine learning classifiers: compressive sensing measurements and the reconstructed hyperspectral image. The classifiers include support vector machines, K nearest neighbors, and three neural networks (3D convolutional neural networks and recurrent neural networks). The results show that deep neural networks can speed up the time for the acquisition, reconstruction, and classification of compressive hyperspectral images.

Bayesian optimization is an effective surrogate-based optimization method that has been widely used for simulation-based applications. However, the traditional Bayesian optimization (BO) method is only applicable to single-fidelity applications, whereas multiple levels of fidelity exist in reality. In this work, we propose a bi-fidelity known/unknown constrained Bayesian optimization method for design applications. The proposed framework, called sBF-BO-2CoGP, is built on a two-level CoKriging method to predict the objective function. An external binary classifier, which is also another CoKriging model, is used to distinguish between feasible and infeasible regions. The sBF-BO-2CoGP method is demonstrated using a numerical example and a flip-chip application for design optimization to minimize the warpage deformation under thermal loading conditions.

Atomic scale defects critically limit performance of semiconductor materials. To improve materials, defect effects and defect formation mechanisms must be understood. In this paper, we demonstrate multiple examples where molecular dynamics simulations have effectively addressed these issues that were not well addressed in prior experiments. In the first case, we report our recent progress on modelling graphene growth, where we found that defects in graphene are created around periphery of islands throughout graphene growth, not just in regions where graphene islands impinge as believed previously. In the second case, we report our recent progress on modelling TlBr, where we discovered that under an electric field, edge dislocations in TlBr migrate in both slip and climb directions. The climb motion ejects extensive vacancies that can cause the rapid aging of the material seen in experiments. In the third case, we discovered that the growth of InGaN films on (0001) surfaces suffers from a serious polymorphism problem that creates enormous amounts of defects. Growth on surfaces, on the other hand, results in single crystalline wurtzite films without any of these defects. In the fourth case, we first used simulations to derive dislocation energies that do not possess any noticeable statistical errors, and then used these error-free methods to discover possible misuse of misfit dislocation theory in past thin film studies. Finally, we highlight the significance of molecular dynamics simulations in reducing defects in the design space of nanostructures.

Radiation transport in stochastic media is a problem found in a multitude of applications, and the need for tools that are capable of thoroughly modeling this type of problem remains. A collection of approximate methods have been developed to produce accurate mean results, but the demand for methods that are capable of quantifying the spread of results caused by the randomness of material mixing remains. In this work, the new stochastic media transport algorithm Conditional Point Sampling is expanded using Embedded Variance Deconvolution such that it can compute the variance caused by material mixing. The accuracy of this approach is assessed for 1D, binary, Markovian-mixed media by comparing results to published benchmark values, and the behavior of the method is numerically studied as a function of user parameters. We demonstrate that this extension of Conditional Point Sampling is able to compute the variance caused by material mixing with accuracy dependent on the accuracy of the conditional probability function used.

Cables and electronic devices typically employ electromagnetic shields to prevent coupling from external radiation. The imperfect nature of these shields allows external electric and magnetic fields to induce unwanted currents and voltages on the inner conductor by penetrating into the interior regions of the cable. In this paper, we verify a circuit model tool using a previously proposed analytic model [1], by evaluating induced currents and voltages on the inner conductor of the shielded cable. Comparisons with experiments are also provided, aimed to validate the proposed circuit model. We foresee that this circuit model will enable coupling between electromagnetic and circuit simulations.

Sandia National Laboratories has developed a model characterizing the nonlinear encoding operator of the world's first hyperspectral x-ray computed tomography (H-CT) system as a sequence of discrete-to-discrete, linear image system matrices across unique and narrow energy windows. In fields such as national security, industry, and medicine, H-CT has various applications in the non-destructive analysis of objects such as material identification, anomaly detection, and quality assurance. However, many approaches to computed tomography (CT) make gross assumptions about the image formation process to apply post-processing and reconstruction techniques that lead to inferior data, resulting in faulty measurements, assessments, and quantifications. To abate this challenge, Sandia National Laboratories has modeled the H-CT system through a set of point response functions, which can be used for calibration and anaylsis of the real-world system. This work presents the numerical method used to produce the model through the collection of data needed to describe the system; the parameterization used to compress the model; and the decompression of the model for computation. By using this linear model, large amounts of accurate synthetic H-CT data can be efficiently produced, greatly reducing the costs associated with physical H-CT scans. Furthermore, successfully approximating the encoding operator for the H-CT system enables quick assessment of H-CT behavior for various applications in high-performance reconstruction, sensitivity analysis, and machine learning.

The design of satellites usually includes the objective of minimizing mass due to high launch costs, which is challenging due to the need to protect sensitive electronics from the space radiation environment by means of radiation shielding. This is further complicated by the need to account for uncertainties, e.g. in manufacturing. There is growing interest in automated design optimization and uncertainty quantification (UQ) techniques to help achieve that objective. Traditional optimization and UQ approaches that rely exclusively on response functions (e.g. dose calculations) can be quite expensive when applied to transport problems. Previously we showed how adjoint-based transport sensitivities used in conjunction with gradient-based optimization algorithms can be quite effective in designing mass-efficient electron and/or proton shields in one- or two-dimensional Cartesian geometries. In this paper we extend that work to UQ and to robust design (i.e. optimization that considers uncertainties) in 2D. This consists primarily of using the sensitivities to geometric changes, originally derived for optimization, within relevant algorithms for UQ and robust design. We perform UQ analyses on previous optimized designs given some assumed manufacturing uncertainties. We also conduct a new optimization exercise that accounts for the same uncertainties. Our results show much improved computational efficiencies over previous approaches.

We investigate the feasibility of additively manufacturing optical components to accomplish task-specific classification in a computational imaging device. We report on the design, fabrication, and characterization of a non-traditional optical element that physically realizes an extremely compressed, optimized sensing matrix. The compression is achieved by designing an optical element that only samples the regions of object space most relevant to the classification algorithms, as determined by machine learning algorithms. The design process for the proposed optical element converts the optimal sensing matrix to a refractive surface composed of a minimized set of non-repeating, unique prisms. The optical elements are 3D printed using a Nanoscribe, which uses two-photon polymerization for high-precision printing. We describe the design of several computational imaging prototype elements. We characterize these components, including surface topography, surface roughness, and angle of prism facets of the as-fabricated elements.

We investigate deep neural networks to reconstruct and classify hyperspectral images from compressive sensing measurements. Hyperspectral sensors provide detailed spectral information to differentiate materials. However, traditional imagers require scanning to acquire spatial and spectral information, which increases collection time. Compressive sensing is a technique to encode signals into fewer measurements. It can speed acquisition time, but the reconstruction can be computationally intensive. First we describe multilayer perceptrons to reconstruct compressive hyperspectral images. Then we compare two different inputs to machine learning classifiers: compressive sensing measurements and the reconstructed hyperspectral image. The classifiers include support vector machines, K nearest neighbors, and three neural networks (3D convolutional neural networks and recurrent neural networks). The results show that deep neural networks can speed up the time for the acquisition, reconstruction, and classification of compressive hyperspectral images.

Radiation transport in stochastic media is a challenging problem type relevant for applications such as meteorological modeling, heterogeneous radiation shields, BWR coolant, and pebble-bed reactor fuel. A commonly cited challenge for methods performing transport in stochastic media is to simultaneously be accurate and efficient. Conditional Point Sampling (CoPS), a new method for transport in stochastic media, was recently shown to have accuracy comparable to the most accurate approximate methods for a common 1D benchmark set. In this paper, we use a pseudo-interface-based approach to extend CoPS to application in multi-D for Markovian-mixed media, compare its accuracy with published results for other approximate methods, and examine its accuracy and efficiency as a function of user options. CoPS is found to be the most accurate of the compared methods on the examined benchmark suite for transmittance and comparable in accuracy with the most accurate methods for reflectance and internal flux. Numerical studies examine accuracy and efficiency as a function of user parameters providing insight for effective parameter selection and further method development. Since the authors did not implement any of the other approximate methods, there is not yet a valid comparison for efficiency with the other methods.

Virtual testbeds are a core component of cyber experimentation as they allow for fast and relatively inexpensive modeling of computer systems. Unlike simulations, virtual testbeds run real software on virtual hardware which allows them to capture unknown or complex behaviors. However, virtualization is known to increase latency and decrease throughput. Could these and other artifacts from virtualization undermine the experiments that we wish to run? For the past three years, we have attempted to quantify where and how virtual testbeds differ from their physical counterparts to address this concern. While performance differences have been widely studied, we aim to uncover behavioral differences. We have run over 10,000 experiments and processed over half a petabyte of data. Complete details of our methodology and our experimental results from applying that methodology are published in previous work. In this paper, we describe our lessons learned in the process of constructing and instrumenting both physical and virtual testbeds and analyzing the results from each.

The development of scramjet engines is crucial for attaining efficient and stable propulsion under hypersonic flight conditions. Design for well-performing scramjet engines requires accurate flow simulations in conjunction with uncertainty quantification (UQ). We advance computational methods in bringing together UQ and large-eddy simulations for scramjet computations, with a focus on the HIFiRE Direct Connect Rig combustor. In particular, we perform uncertainty propagation for spatially dependent field quantities of interest (QoIs) by treating them as random fields, and numerically compute low-dimensional Karhunen-Loève expansions (KLEs) using a finite number of simulations on non-uniform grids. We also describe a formulation and procedure to extract conditional KLEs that characterize the stochasticity induced by uncertain parameters at given designs. This is achieved by first building a single KLE for each QoI via samples drawn jointly from the parameter and design spaces, and then leverage polynomial chaos expansions to insert input dependencies into the KLE. The ability to access conditional KLEs will be immensely useful for subsequent efforts in design optimization under uncertainty as well as model calibration with field variable measurements.

We propose a probabilistic framework for assessing the consistency of an experimental dataset, i.e., whether the stated experimental conditions are consistent with the measurements provided. In case the dataset is inconsistent, our framework allows one to hypothesize and test sources of inconsistencies. This is crucial in model validation efforts. The framework relies on statistical inference to estimate experimental settings deemed untrustworthy, from measurements deemed accurate. The quality of the inferred variables is gauged by its ability to reproduce held-out experimental measurements; if the new predictions are closer to measurements than before, the cause of the discrepancy is deemed to have been found. The framework brings together recent advances in the use of Bayesian inference and statistical emulators in fluid dynamics with similarity measures for random variables to construct the hypothesis testing approach. We test the framework on two double-cone experiments executed in the LENS-XX wind tunnel and one in the LENS-I tunnel; all three have encountered difficulties when used in model validation exercises. However, the cause behind the difficulties with the LENS-I experiment is known, and our inferential framework recovers it. We also detect an inconsistency with one of the LENS-XX experiments, and hypothesize three causes for it. We check two of the hypotheses using our framework, and we find evidence that rejects them. We end by proposing that uncertainty quantification methods be used more widely to understand experiments and characterize facilities, and we cite three different methods to do so, the third of which we present in this paper.

We present heavy ion and proton data on AlGaN high-voltage HEMTs showing single event burnout (SEB), total ionizing dose, and displacement damage responses. These are the first such data for materials of this type. Two different designs of the epitaxial structure were tested for SEB. The default layout design showed burnout voltages that decreased rapidly with increasing LET, falling to about 25% of nominal breakdown voltage for ions with LET of about 34 MeV · cm2/mg for both structures. Samples of the device structure with lower AlN content were tested with varying gate-drain spacing and revealed an improved robustness to heavy ions, resulting in burnout voltages that did not decrease up to at least 33.9 MeV · cm2/mg. Failure analysis showed that there was consistently a point, location random, where gate and drain had been shorted. Oscilloscope traces of terminal voltages and currents during burnout events lend support to the hypothesis that burnout events begin with a heavy ion strike in the vulnerable region between gate and drain. This subsequently initiates a cascade of events resulting in damage that is largely manifested elsewhere in the device. This hypothesis also suggests a path for greatly improving the susceptibility to SEB as development of this technology goes forward. Testing with 2.5-MeV protons showed only minor changes in device characteristics.

Data assimilation techniques are investigated to determine how high-speed experimental measurements can be infused into a combustion simulation with the goal of capturing transient combustion events and isolating model deficiencies. To this end, an ensemble Kalman filter (EnKF) is employed to assimilate simultaneous measurements from tomographic PIV and OH-PLIF into a combustion LES of a turbulent DME jet flame, taking into consideration experimental uncertainties and modeling errors. It is shown that by assimilating experimental data, EnKF improves the prediction of the extinction and reignition dynamics observed in this flame. Subsequently, the capability of the assimilation method in evaluating the model performance is examined by considering an assimilation sequence. It is shown that the combustion model investigated (namely a flamelet/progress variable model) exhibits a tendency to relax towards a more reactive state, indicating a deficiency in quantitatively predicting the extent of extinction and reignition with this particular model.

Fractures within the earth control rock strength and fluid flow, but their dynamic nature is not well understood. As part of a series of underground chemical explosions in granite in Nevada, we collected and analyzed microfracture density data sets prior to, and following, individual explosions. Our work shows an ~4-fold increase in both open and filled microfractures following the explosions. Based on the timing of core retrieval, filling of some new fractures occurs in as little as 6 wk after fracture opening under shallow (<100 m) crustal conditions. These results suggest that near-surface fractures may fill quite rapidly, potentially changing permeability on time scales relevant to oil, gas, and geothermal energy production; carbon sequestration; seismic cycles; and radionuclide migration from nuclear waste storage and underground nuclear explosions.

Here, in this contribution, we discuss our experiences organizing the Best Practices for HPC Software Developers (HPC-BP) webinar series, an effort for the dissemination of software development methodologies, tools and experiences to improve developer productivity and software sustainability. HPC-BP is an outreach component of the IDEAS Productivity Project [4] and has been designed to support the IDEAS mission to work with scientific software development teams to enhance their productivity and the sustainability of their codes. The series, which was launched in 2016, has just presented its 22nd webinar. We summarize and distill our experiences with these webinars, including what we consider to be "best practices" in the execution of both individual webinars and a long-running series like HPC-BP. We also discuss future opportunities and challenges in continuing the series.

Input/output (I/O) from various sources often contend for scarcely available bandwidth. For example, checkpoint/restart (CR) protocols can help to ensure application progress in failure-prone environments. However, CR I/O alongside an application's normal, requisite I/O can increase I/O contention and might negatively impact performance. In this work, we consider different aspects (system-level scheduling policies and hardware) that optimize the overall performance of concurrently executing CR-based applications that share I/O resources. We provide a theoretical model and derive a set of necessary constraints to minimize the global waste on a given platform. Our results demonstrate that Young/Daly's optimal checkpoint interval, despite providing a sensible metric for a single, undisturbed application, is not sufficient to optimally address resource contention at scale. We show that by combining optimal checkpointing periods with contention-aware system-level I/O scheduling strategies, we can significantly improve overall application performance and maximize the platform throughput. Finally, we evaluate how specialized hardware, namely burst buffers, may help to mitigate the I/O contention problem. Altogether, these results provide critical analysis and direct guidance on how to design efficient, CR ready, large -scale platforms without a large investment in the I/O subsystem.

The image classification accuracy of a TaO_x ReRAM-based neuromorphic computing accelerator is evaluated after intentionally inducing a displacement damage up to a fluence of 10¹⁴ 2.5-MeV Si ions/cm² on the analog devices that are used to store weights. Results are consistent with a radiation-induced oxygen vacancy production mechanism. When the device is in the high-resistance state during heavy ion radiation, the device resistance, linearity, and accuracy after training are only affected by high fluence levels. Here, the findings in this paper are in accordance with the results of previous studies on TaO_x-based digital resistive random access memory. When the device is in the low-resistance state during irradiation, no resistance change was detected, but devices with a 4-kΩ inline resistor did show a reduction in accuracy after training at 10¹⁴ 2.5-MeV Si ions/cm². This indicates that changes in resistance can only be somewhat correlated with changes to devices’ analog properties. This paper demonstrates that TaO_x devices are radiation tolerant not only for high radiation environment digital memory applications but also when operated in an analog mode suitable for neuromorphic computation and training on new data sets.

A multiscale modeling platform that supports the “virtual” qualification of commercial-off-the-shelf parts is presented. The multiscale approach is divided into two modules. The first module generates information related to the bipolar junction transistor gain degradation that is a function of fabrication process, operational, and environmental inputs. The second uses this information as inputs for radiation-enabled circuit simulations. The prototype platform described in this paper estimates the total ionizing dose and dose rate responses of linear bipolar integrated circuits for different families of components. The simulation and experimental results show good correlation and suggest this platform to be a complementary tool within the radiation-hardness assurance flow. Finally, the platform may reduce some of the costly reliance on testing for all systems.

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This paper highlights how, in the backdrop of prevalent tensions between India and Pakistan, deterrence stability is endangered even when overall strategic stability prevails. Unresolved legacy issues including an outstanding boundary dispute, conflict over Kashmir, and terrorism mar India-Pakistan relations. Variables undermining the prospects for long term peace, among others, include growing mutual mistrust, the proxy war that India believes is state perpetrated, increasing conventional asymmetry, rapid advancement in weapon technologies, growing size of nuclear arsenals, doctrinal mismatch and above all, bilateral gridlock in confidence building measures and arms control measures.

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Discharge Permit (DP)-1845 was issued by the New Mexico Environment (NMED) Ground Water Quality Bureau (GWQB) for discharges via up to three injection wells in a phased Treatability Study of in-situ bioremediation of groundwater at the Sandia National Laboratories, New Mexico, Technical Area-V Groundwater Area of Concern. This report fulfills the quarterly reporting requirements set forth in DP-1845, Section IV.B, Monitoring and Reporting. This reporting period is July 1 through September 30, 2018. The report is due to NMED GWQB by February 1, 2019.

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This Sandia National Laboratories, New Mexico Environmental Restoration Operations (ER) Consolidated Quarterly Report (ER Quarterly Report) fulfills all quarterly reporting requirements set forth in the Compliance Order on Consent. Table I-1 lists the six sites remaining in the corrective action process. This edition of the ER Quarterly Report does not include Section II "Perchlorate Screening Quarterly Groundwater Monitoring Report" because no groundwater samples were analyzed for perchlorate during this reporting period. Additionally, Section III is not included in this edition of the ER Quarterly Report because there is no detailed Technical Area-V Groundwater information to present.

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Safety basis analysts throughout the U.S. Department of Energy (DOE) complex rely heavily on the information provided in the DOE Handbook, DOE-HDBK-3010, Airborne Release Fractions/Rates and Respirable Fractions for Nonreactor Nuclear Facilities, to determine radionuclide source terms from postulated accident scenarios. In calculating source terms, analysts tend to use the DOE Handbook's bounding values on airborne release fractions (ARFs) and respirable fractions (RFs) for various categories of insults (representing potential accident release categories). This is typically due to both time constraints and the avoidance of regulatory critique. Unfortunately, these bounding ARFs/RFs represent extremely conservative values. Moreover, they were derived from very limited small-scale bench/laboratory experiments and/or from engineered judgment. Thus, the basis for the data may not be representative of the actual unique accident conditions and configurations being evaluated. The goal of this research is to develop a more accurate and defensible method to determine bounding values for the DOE Handbook using state-of-art multi-physics-based computer codes. This enables us to better understand the fundamental physics and phenomena associated with the types of accidents in the handbook. In this fourth year, we improved existing computational capabilities to better model fragmentation situations to capture small fragments during an impact accident. In addition, we have provided additional new information for various sections of Chapters 4 and 5 of the Handbook on free fall powders and impacts of solids, and have provided the damage ratio simulations for containers (7A drum and standard waste box) for various drops and impact scenarios. Thus, this work provides a low-cost method to establish physics-justified safety bounds by considering specific geometries and conditions that may not have been previously measured and/or are too costly to perform during an experiment.

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