Publications

Abstract not provided.

Residual stress is a contributor to stress corrosion cracking (SCC) and a common byproduct of additive manufacturing (AM). Here the relationship between residual stress and SCC susceptibility in laser powder bed fusion AM 316L stainless steel was studied through immersion in saturated boiling magnesium chloride per ASTM G36-94. The residual stress was varied by changing the sample height for the as-built condition and additionally by heat treatments at 600°C, 800°C, and 1,200°C to control, and in some cases reduce, residual stress. In general, all samples in the as-built condition showed susceptibility to SCC with the thinner, lower residual stress samples showing shallower cracks and crack propagation occurring perpendicular to melt tracks due to local residual stress fields. The heat-treated samples showed a reduction in residual stress for the 800°C and 1,200°C samples. Both were free of cracks after >300 h of immersion in MgCl2, while the 600°C sample showed similar cracking to their as-built counterpart. Geometrically necessary dislocation (GND) density analysis indicates that the dislocation density may play a major role in the SCC susceptibility.

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

Driven by the exceedingly high computational demands of simulating mechanical response in complex engineered systems with finely resolved finite element models, there is a critical need to optimally reduce the fidelity of such simulations. The minimum required fidelity is constrained by error tolerances on the simulation results, but error bounds are often impossible to obtain a priori. One such source of error is the variability of material properties within a body due to spatially non-uniform processing conditions and inherent stochasticity in material microstructure. This study seeks to quantify the effects of microstructural heterogeneity on component- and system-scale performance to aid in the choice of an appropriate material model and spatial resolution for finite element analysis.

A primary objective of repository modeling is identification and assessment of features and processes providing safety performance. Sensitivity analyses typically provide information on how input parameters affect performance, not features and processes. To quantify the effects of features and processes, tracers can be introduced virtually in model simulations and tracked in informative ways. This paper describes five ways virtual tracers can be used to directly measure the relative importance of several features, processes, and combinations of features and processes in repository performance assessment modeling.

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A critical parameter for the well integrity in geothermal storage and production wells subjected to frequent thermal cycling is the interface between the steel and cement. In geothermal energy storage and energy production wells an insulating cement sheath is necessary to minimize heat losses through the heat uptake by cooler rock formations with high thermal conductivity. Also critical parameters for the well integrity in geothermal storage and production wells subjected to frequent thermal cycling is the interface between metal casing and cement composite. A team from Sandia and Brookhaven National Labs is evaluating special cement formulations to facilitate use during severe and repeated thermal cycling in geothermal wells; this paper reports on recent finding using these more recently developed cements. For this portion of the laboratory study we report on preliminary results from subjecting this cement to high temperature (T> 200°C), at a confining pressure of 13.8 MPa, and pore water pressure of 10.4 MPa. Building on previous work, we studied two sample types; solid cement and a steel cylinder sheathed with cement. In the first sample type we measured fluid flow at increasing elevated temperatures and pressure. In the second sample type, we flowed water through the inside of the steel cylinder rapidly to develop an inner to outer thermal gradient using this specialized test geometry. In the paper we report on water permeability estimates at elevated temperatures and the results of rapid thermal cycling of a steel/cement interface. Posttest observations of the steel-cement interface reveal insight into the nature of the steel/cement bond.

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

Identifying the location of faults in a fast and accurate manner is critical for effective protection and restoration of distribution networks. This paper describes an efficient method for detecting, localizing, and classifying faults using advanced signal processing and machine learning tools. The method uses an Isolation Forest technique to detect the fault. Then Continuous Wavelet Transform (CWT) is used to analyze the traveling waves produced by the faults. The CWT coefficients of the current signals at the time of arrival of the traveling wave present unique characteristics for different fault types and locations. These CWT coefficients are fed into a Convolutional Neural Network (CNN) to train and classify fault events. The results show that for multiple fault scenarios and solar PV conditions, the method is able to determine the fault type and location with high accuracy.

Fires of practical interest are often large in scale and involve turbulent behavior. Fire simulation tools are often utilized in an under-resolved prediction to assess fire behavior. Data are scarce for large fires because they are difficult to instrument. A helium plume scenario has been used as a surrogate for much of the fire phenomenology (O'Hern et al., 2005), including buoyancy, mixing, and advection. A clean dataset of this nature makes an excellent platform for assessing model accuracy. We have been participating in a community effort to validate fire simulation tools, and the SIERRA/Fuego code is compared here with the historical dataset. Our predictions span a wide range of length-scales, and comparisons are made to species mass fraction and two velocity components for a number of heights in the core of the plume. We detail our approach to the comparisons, which involves some accommodation for the uncertainty in the inflow boundary condition from the test. We show evolving improvement in simulation accuracy with increasing mesh resolution and benchmark the accuracy through comparisons with the data.

Transportation of sodium-bonded spent fuel appears to present no unique challenges. Storage systems for this fuel should be designed to keep water, both liquid and vapor, from contacting the spent fuel. This fuel is not suitable for geologic disposal; therefore, how the spent sodium bonded fuel will be processed and the characteristics of the final disposal waste form(s) need to be considered. TRISO spent fuel appears to present no unique challenges in terms of transportation, storage, or disposal. If the graphite block is disposed of with the TRISO spent fuel, the 14C and 3H generated would need to be considered in the postclosure performance assessment. Salt waste from the molten salt reactor has yet to be transported or stored and might be a challenge to dispose of in a non-salt repositories. Like sodium-bonded spent fuel, how the salt will be treated and the characteristics of the final disposal waste form(s) need to be considered. In addition, radiolysis in the frozen salt waste form continues to generate gas, which presents a hazard. Both HALEU and high-enriched uranium SNF are currently being stored and transported by the DOE. Disposal of fuels with enrichments greater than 5% was included in the disposal plan for Yucca Mountain. The increased potential for criticality associated with the higher enriched SNF is mitigated by additional criticality control measures. Fuels that are similar to some ATFs were part of the disposal plan for Yucca Mountain. Some of the properties of these fuels (swelling, generation of 14C) would have to be considered as part of a postclosure performance assessment.

The Ghareb Formation in the Yasmin Plain of Israel is under investigation as a potential disposal rock for nuclear waste disposal. Triaxial deformation tests and hydrostatic water-permeability tests were conducted with samples of the Ghareb to assess relevant thermal, hydrological, and mechanical properties. Axial deformation tests were performed on dry and water-saturated samples at effective pressures ranging from 0.7 to 19.6 MPa and temperatures of 23 ℃ and 100 ℃, while permeability tests were conducted at ambient temperatures and effective pressures ranging from 0.7 to 20 MPa. Strength and elastic moduli increase with increasing effective pressure for the triaxial tests. Dry room temperature tests are generally the strongest, while the samples deformed at 100 ℃ exhibit large permanent compaction even at low effective pressures. Water permeability decreases by 1-2 orders of magnitude under hydrostatic conditions while experiencing permanent volume loss of 4-5%. Permeability loss is retained after unloading, resulting from permanent compaction. A 3-D compaction model was used to demonstrate that compaction in one direction is associated with de-compaction in the orthogonal directions. The model accurately reproduces the measured axial and transverse strain components. The experimentally constrained deformational properties of the Ghareb will be used for 3-D thermal-hydrological-mechanical modelling of borehole stability.

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Propagating thermal runaway events are a significant threat to utility-scale storage installations. A propagating thermal runaway event is a cascading series of failures in which energy released from a failed cell triggers subsequent failures in nearby cells. Without intervention, propagation can turn an otherwise manageable single cell failure into a full system conflagration. This study presents a method of mitigating the severity of propagating thermal runaway events in utility-scale storage systems by leveraging the capabilities of a module-interfaced power conversion architecture. The method involves strategic depletion of storage modules to delay or arrest propagation, reducing the total thermal energy released in the failure event. The feasibility of the method is assessed through simulations of propagating thermal runaway events in a 160 kW/80 kWh energy storage system.

The latest high temperature (HT) microcontrollers and memory technology have been investigated for the purpose of enhancing downhole instrumentation capabilities at temperatures above 210°C. As part of the effort, five microcontrollers (Honeywell HT83C51, RelChip RC10001, Texas Instruments SM470R1B1M-HT, SM320F2812-HT, SM320F28335-HT) and one memory chip (RelChip RC2110836) have been evaluated to its rated temperature for a period of one month to determine life expectancy and performance. Pulse rate of the integrated circuit and internal memory scan were performed during testing by remotely located axillary components. This paper will describe challenges encountered in the operation and HT testing of these components. Long-term HT tests results show the variation in power consumption and packaging degradation. The work described in this paper improves downhole instrumentation by enabling greater sensor counts and improving data accuracy and transfer rates at temperatures between 210°C and 300°C.

Growing interest in renewable energy sources has led to an increased installation rate of distributed energy resources (DERs) such as solar photovoltaics (PVs) and wind turbine generators (WTGs). The variable nature of DERs has created several challenges for utilities and system operators related to maintaining voltage and frequency. New grid standards are requiring DERs to provide voltage regulation across distribution networks. Volt-Var Curve (VVC) control is an autonomous grid-support function that provides voltage regulation based on the relationship between voltage and reactive power. This paper evaluates the performance of a WTG operating with VVC control. The evaluation of the model involves a MATLAB/Simulink simulation of a distribution system. For this simulation the model considers three WTGs and a variable load that creates a voltage event.

This paper reports the experimental comparison of two silicon photomultipliers (SiPMs): the MicroFJ-30035 by ONSemi and the ASD-NUV3S-P by AdvanSiD, in terms of gain, dark count rate, and crosstalk probability. SiPMs are solid state photon detectors that enable high sensitivity light readout. They have low-voltage power requirements, small form factor, and are durable. For these reasons, they are being considered as replacements for vacuum photomultiplier tubes in some applications. However, their performance relies on several parameters, which need to be carefully characterized to enable their high-fidelity simulation and SiPM-based design of devices capable to operate in harsh environments. The parameters tend to vary between manufacturers and processing technologies. In this work, we have compared the MicroFJ and ASD SiPMs in terms of gain, dark count rate, and crosstalk probability. We found that the dark count rate of the MicroFJ was 16% higher than the ASD. Also, the gain of the MicroFJ is 3.5 times higher than the ASD. Finally, the crosstalk probability of the ASD 1.96 times higher than the MicroFJ. Our findings are in good agreement with manufacturer reported values.

Any program tasked with the evaluation and acquisition of algorithms for use in deployed scenarios must have an impartial, repeatable, and auditable means of benchmarking both candidate and fielded algorithms. Success in this endeavor requires a body of representative sensor data, data labels indicating the proper algorithmic response to the data as adjudicated by subject matter experts, a means of executing algorithms under review against the data, and the ability to automatically score and report algorithm performance. Each of these capabilities should be constructed in support of program and mission goals. By curating and maintaining data, labels, tests, and scoring methodology, a program can understand and continually improve the relationship between benchmarked and fielded performance of acquired algorithms. A system supporting these program needs, deployed in an environment with sufficient computational power and necessary security controls is a powerful tool for ensuring due diligence in evaluation and acquisition of mission critical algorithms. This paper describes the Seascape system and its place in such a process.

Scalable coherent control hardware for quantum information platforms is rapidly growing in priority as their number of available qubits continues to increase. As these systems scale, more calibration steps are needed, leading to challenges with system instability as calibrated parameters drift. Moreover, the sheer amount of data required to run circuits with large depth tends to balloon, especially when implementing state-of-the-art dynamical-decoupling gates which require advanced modulation techniques. We present a control system that addresses these challenges for trapped-ion systems, through a combination of novel features that eliminate the need for manual bookkeeping, reduction in data transfer bandwidth requirements via gate compression schemes, and other automated error handling techniques. Moreover, we describe an embedded pulse compiler that applies staged optimization, including compressed intermediate representations of parsed output products, performs in-situ mutation of compressed gate data to support high-level algorithmic feedback to account for drift, and can be run entirely on chip.

This paper applies sensitivity and uncertainty analysis to compare two model alternatives for fuel matrix degradation for performance assessment of a generic crystalline repository. The results show that this model choice has little effect on uncertainty in the peak 129I concentration. The small impact of this choice is likely due to the higher importance of uncertainty in the instantaneous release fraction and differences in epistemic uncertainty between the alternatives.

Abstract not provided.

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

The focus of this study is on spectral equivalence results for higher-order tensor product finite elements in the H(curl), H(div), and L2 function spaces. For certain choices of the higher-order shape functions, the resulting mass and stiffness matrices are spectrally equivalent to those for an assembly of lowest-order edge-, face- or interior-based elements on the associated Gauss–Lobatto–Legendre (GLL) mesh.

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

Abstract not provided.

Primary frequency control in power systems is being challenged by the large-scale integration of inverter-based resources (IBRs) because they do not typically respond to frequency fluctuations. This paper suggests introducing new reserve products into the electricity market that provide incentive for IBRs to contribute to primary frequency control in ways that take advantage of their fast-acting capabilities. In addition to a Primary Frequency Response (PFR) reserve product, which accommodates standard droop control, we suggest introducing a Fast Frequency Response (FFR) reserve product, a reserve product for Virtual Inertia (VI), which is also known as synthetic inertia, and an inertia product. We adopt a reserve requirement that guarantees sufficient primary frequency response reserve to adequately arrest frequency decline in response to a large generator outage within a certain margin. We place this reserve requirement into a real-time co-optimization problem, derive prices for each product and analyze the incentives provided to IBRs.

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Quantifying gas-surface interactions for hypersonic reentry applications remains a challenging and complex problem where credible models are needed to design and analyze thermal protection systems. A flexible sensitivity analysis approach is demonstrated to analyze finite-rate ablation models to identify reaction parameters and mechanisms of influence on predicted quantities of interest. Simulations of hypersonic flow over a sphere-cone are presented using parameterized Park, Zhluktov and Abe (ZA), and MURI finite-rate models that describe the oxidation and sublimation of carbon. The results presented in this study emphasize the importance of characterizing model inputs that are shown to have a high impact on predicted quantities and build evidence to assess credibility of these models.

This paper provides a study of the potential impacts of climate change on intermittent renewable energy resources, battery storage, and resource adequacy in Public Service Company of New Mexico's Integrated Resource Plan for 2020 - 2040. Climate change models and available data were first evaluated to determine uncertainty and potential changes in solar irradiance, temperature, and wind speed in NM in the coming decades. These changes were then implemented in solar and wind energy models to determine impacts on renewable energy resources in NM. Results for the extreme climate-change scenario show that the projected wind power may decrease by ~13% due to projected decreases in wind speed. Projected solar power may decrease by ~4% due to decreases in irradiance and increases in temperature in NM. Uncertainty in these climateinduced changes in wind and solar resources was accommodated in probabilistic models assuming uniform distributions in the annual reductions in solar and wind resources. Uncertainty in battery storage performance was also evaluated based on increased temperature, capacity fade, and degradation in roundtrip efficiency. The hourly energy balance was determined throughout the year given uncertainties in the renewable energy resources and energy storage. The loss of load expectation (LOLE) was evaluated for the 2040 No New Combustion portfolio and found to increase from 0 days/year to a median value of ~2 days/year due to potential reductions in renewable energy resources and battery storage performance and capacity. A rank-regression analyses revealed that battery round-trip efficiency was the most significant parameter that impacted LOLE, followed by solar resource, wind resource, and battery fade. An increase in battery storage capacity to ~30,000 MWh from a baseline value of ~14,000 MWh was required to reduce the median value of LOLE to ~0.2 days/year with consideration of potential climate impacts and battery degradation.

Sandia provided technical assistance to Kit Carson Electric Cooperative (KCEC) to assess the technical merits of a proposed community resilience microgrid project in the Village of El Rito, New Mexico (NM). The project includes a proposed community resilience microgrid in the Village of El Rito, NM, around the campus of Northern New Mexico College (NNMC). A conceptual microgrid analysis plan was performed, considering a campus and community-wide approach. The analysis results provided conceptual microgrid configurations, optimized according to the performance metrics defined. The campus microgrid was studied independently and many conceptual microgrid solutions were provided that met the performance requirements. Considering the existing 1.5 MW PV system on campus far exceeds the simulated campus load peak and energy demand, a small battery installation was deemed sufficient to support the campus microgrid goals. Following the analysis and consultation, it was determined that the core Resilient El Rito team will need to further investigate the results for additional economic and environmental considerations to continue toward the best approach for their goals and needs.

Hardware design in high-performance computing (HPC) is often highly experimental. Exploring new designs is difficult and time-consuming, requiring lengthy vendor cooperation. RISC-V is an open-source processor ISA that improves the accessibility of chip design, including the ability to do hardware/software co-design using open-source hardware and tools. Co-design allows design decisions to easily flow across the hardware/software boundary and influence future design ideas. However, new hardware designs require corresponding software to drive and test them. Conventional operating systems like Linux are massively complex and modification is time-prohibitive. In this paper, we describe our port of the Kitten lightweight kernel operating system to RISC-V in order to provide an alternative to Linux for conducting co-design research. Kitten's small code base and simple resource management policies are well matched for quickly exploring new hardware ideas that may require radical operating system modifications and restructuring. Our evaluation shows that Kitten on RISC-V is functional and provides similar performance to Linux for single-core benchmarks. This provides a solid foundation for using Kitten in future co-design research involving RISC-V.

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

Geothermal energy has been underutilized in the U.S., primarily due to the high cost of drilling in the harsh environments encountered during the development of geothermal resources. Drilling depths can approach 5,000 m with temperatures reaching 170 C. In situ geothermal fluids are up to ten times more saline than seawater and highly corrosive, and hard rock formations often exceed 240 MPa compressive strength. This combination of extreme conditions pushes the limits of most conventional drilling equipment. Furthermore, enhanced geothermal systems are expected to reach depths of 10,000 m and temperatures more than 300 °C. To address these drilling challenges, Sandia developed a proof-of-concept tool called the auto indexer under an annual operating plan task funded by the Geothermal Technologies Program (GTP) of the U.S. Department of Energy Geothermal Technologies Office. The auto indexer is a relatively simple, elastomer-free motor that was shown previously to be compatible with pneumatic hammers in bench-top testing. Pneumatic hammers can improve penetration rates and potentially reduce drilling costs when deployed in appropriate conditions. The current effort, also funded by DOE GTP, increased the technology readiness level of the auto indexer, producing a scaled prototype for drilling larger diameter boreholes using pneumatic hammers. The results presented herein include design details, modeling and simulation results, and testing results, as well as background on percussive hammers and downhole rotation.

In March 2021, a functional area drill was held at the Remote Sensing Laboratory–Nellis that focused on using CBRNResponder and the Digital Field Monitoring (DFM) tablets for sample hotline operations and the new paper Sample Control Forms (SCFs) for sample collection. Participants included staff trained and billeted as sample control specialists and Consequence Management Response Team (CMRT) field monitoring personnel. Teams were able to successfully gather and transfer samples to the sample control hotline staff through the manual process, though there were several noted areas for improvement. In July and October 2021, two additional functional area drills were held at Sandia National Laboratories that focused on field sample collection and custody transfer at the sample control hotline for the Consequence Management (CM) Radiological Assistance Program (RAP) program. The overarching goal of the drills was to evaluate the current CM process for sample collection, sample drop off, and sample control using the CBRNResponder mobile and web-based applications. The July 2021 drill had an additional focus to have a subset of samples analyzed by the local analytical laboratory, Radiation Protection Sample Diagnostics (RPSD) laboratory, to evaluate the Laboratory Access portal on CBRNResponder. All three drills were able to accomplish their objectives however, there were several issues noted (Observations: 25 Urgent, 29 Important, and 22 Improvement Opportunities). The observations were prioritized according to their impact on the mission as well as categorized to align with the programmatic functional area required to address the issue. This report provides additional detail on each observation for skillset/program leads and software developers to consider for future improvement or mandatory efforts.

The application of traveling wave principles for fault detection in distribution systems is challenging because of multiple reflections from the laterals and other lumped elements, particularly when we consider communication-free applications. We propose and explore the use of Shapelets to characterize fault signatures and a data-driven machine learning model to accurately classify the faults based on their distance. Studies of a simple 5-bus system suggest that the use of Shapelets for detecting faults is promising. The application to practical three-phase distribution feeders is the subject of continuing research.

High pressure hydrogen storage vessels are frequently retired upon reaching their designed number of pressure cycles, even in cases where the in-use pressure cycles are significantly less severe than the design pressure cycle. One method for extending the life of hydrogen vessels is recertification through non-destructive evaluation (NDE); however, NDE techniques are frequently evaluated with machined defects in test samples rather than fatigue cracks which occur during pressure cycling and may be more difficult to detect. In this paper, 50 mm wide ring sections (called C-rings, since they represent slightly more than half the circumference) were extracted from pressure vessels and mechanically cycled to establish fatigue cracks. Sub-millimeter starter notches were machined, via plunge electrical discharge machining (EDM), to control the location of crack initiation. Crack growth was monitored via direct current potential difference (DCPD) and backface strain gauges, both of which were shown to be good indicators for crack propagation. The C-ring geometry and fatigue crack growth were modeled to demonstrate the ability to monitor/control the crack length and area, which can be used to develop calibration samples of varying crack depth for NDE techniques. Additionally, this sample is intended to evaluate the influence of residual stresses on the sensitivity of NDE techniques, such as the design stresses in autofrettaged vessels.

The Sandia Optical Fringe Analysis Slope Tool (SOFAST) is a tool that has been developed at Sandia to measure the surface slope of concentrating solar power optics. This tool has largely remained of research quality over the past few years. Since SOFAST is important to ongoing tests happening at Sandia as well as an interest to others outside Sandia, there is a desire to bring SOFAST up to professional software standards. The goal of this effort was to make progress in several broad areas including: code quality, sample data collection, and validation and testing. During the course of this effort, much progress was made in these areas. SOFAST is now a much more professional grade tool. There are, however, some areas of improvement that could not be addressed in the timeframe of this work and will be addressed in the continuation of this effort.

Performance assessment is an important tool to estimate the long-term safety for a nuclear waste repository. Performance assessment simulations are subject to multiple kinds of uncertainty including stochastic uncertainty, state of knowledge uncertainty, and model uncertainty. Task F1 of the DECOVALEX project involves comparison of the models and methods used in post-closure performance assessment of deep geologic repositories in fractured crystalline rock, providing an opportunity to compare the effects of different sources of uncertainty. A generic reference case for a mined repository in fractured crystalline rock was put together by participating teams, where each team was responsible for determining how best to represent and implement the model. This work presents the preliminary crystalline reference case results for the Department of Energy (DOE) team.

With machine learning (ML) technologies rapidly expanding to new applications and domains, users are collaborating with artificial intelligence-assisted diagnostic tools to a larger and larger extent. But what impact does ML aid have on cognitive performance, especially when the ML output is not always accurate? Here, we examined the cognitive effects of the presence of simulated ML assistance-including both accurate and inaccurate output-on two tasks (a domain-specific nuclear safeguards task and domain-general visual search task). Patterns of performance varied across the two tasks for both the presence of ML aid as well as the category of ML feedback (e.g., false alarm). These results indicate that differences such as domain could influence users' performance with ML aid, and suggest the need to test the effects of ML output (and associated errors) in the specific context of use, especially when the stimuli of interest are vague or ill-defined.

To meet the challenges oflow-carbon power generation, distributed energy resources (DERs) such as solar and wind power generators are now widely integrated into the power grid. Because of the autonomous nature of DERs, ensuring properly regulated output voltages of the individual sources to the grid distribution system poses a technical challenge to grid operators. Stochastic, model-free voltage regulations methods such as deep reinforcement learning (DRL) have proven effective in the regulation of DER output voltages; however, deriving an optimal voltage control policy using DRL over a large state space has a large computational time complexity. In this paper we illustrate a computationally efficient method for deriving an optimal voltage control policy using a parallelized DRL ensemble. Additionally, we illustrate the resiliency of the control ensemble when random noise is introduced by a cyber adversary.

We present a field-deployable microfluidic immunoassay device in response to the need for sensitive, quantitative, and high-throughput protein detection at point-of-need. The portable microfluidic system facilitates eight magnetic bead-based sandwich immunoassays from raw samples in 45 minutes. An innovative bead actuation strategy was incorporated into the system to automate multiple sample process steps with minimal user intervention. The device is capable of quantitative and sensitive protein analysis with a 10 pg/ml detection limit from interleukin 6-spiked human serum samples. We envision the reported device offering ultrasensitive point-of-care immunoassay tests for timely and accurate clinical diagnosis.

We examine coupling into azimuthal slots on an infinite cylinder with a infinite length interior cavity operating both at the fundamental cavity modal frequencies, with small slots and a resonant slot, as well as higher frequencies. The coupling model considers both radiation on an infinite cylindrical exterior as well as a half space approximation. Bounding calculations based on maximum slot power reception and interior power balance are also discussed in detail and compared with the prior calculations. For higher frequencies limitations on matching are imposed by restricting the loads ability to shift the slot operation to the nearest slot resonance; this is done in combination with maximizing the power reception as a function of angle of incidence. Finally, slot power mismatch based on limited cavity load quality factor is considered below the first slot resonance.

For many industries addressing varied customer needs means producing a family of products that satisfy a range of design requirements. Manufacturers seek to design this family of products while exploiting opportunities for shared components to reduce manufacturing cost and complexity. We present a mixed-integer programming formulation that determines the optimal design for each product, the number and design of shared components, and the allocation of those shared components across the products in the family. This formulation and workflow for product family design has created significant business impact on the industrial design of product families for large-scale commercial HVAC chillers in Carrier Global Corporation. We demonstrate the approach on an open case study based on a transcritical CO2 refrigeration cycle. This case study and our industrial experience show that the formulation is computationally tractable and can significantly reduce engineering time by replacing the manual design process with an automated approach.

Organizations that monitor for underground nuclear explosive tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high - quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. In this study we use waveform template event metadata to seek corroborating detections from multiple stations in the International Monitoring System of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization. We build upon events detected in a prior waveform correlation study of mining blasts in two geographic regions, Wyoming and Scandinavia. Using a set of expert analyst-reviewed waveform correlation events that were declared to be true positive detections, we explore criteria for choosing the waveform correlation detections that are most likely to lead to bulletin-worthy events and reduction of analyst effort.

This paper studies a novel mixed-integer linear programming (MILP) formulation on the application of mobile energy storage (MES) to assist with black-start restoration following the full blackout of an electrical network. By synthesizing techniques in the literature to model generator black start and MES activity, the formulation is the first to integrate the two concepts. Furthermore, it recognizes that the manner in which MES facilitates black-start (BS) restoration may differ depending on what component damages occurred during the event that induced the blackout. Within the IEEE 14-Bus System, testing of the formulation has not only confirmed its efficacy but also underscored circumstances where BS restoration could especially benefit from MES intervention in practice. With an MES sized at 2.59% of total MW generation capacity, in certain damage configuration categories the median load energy unserved is reduced by as much as 45.52 MWh (8.26%), and the median final load supplied is raised by as much as 22.98 MW (10.39%).

In this paper, we address the problem of convergence of sequential variational inference filter (VIF) through the application of a robust variational objective and H∞-norm based correction for a linear Gaussian system. As the dimension of state or parameter space grows, performing the full Kalman update with the dense covariance matrix for a large-scale system requires increased storage and computational complexity, making it impractical. The VIF approach, based on mean-field Gaussian variational inference, reduces this burden through the variational approximation to the covariance usually in the form of a diagonal covariance approximation. The challenge is to retain convergence and correct for biases introduced by the sequential VIF steps. We desire a frame-work that improves feasibility while still maintaining reasonable proximity to the optimal Kalman filter as data is assimilated. To accomplish this goal, a H∞-norm based optimization perturbs the VIF covariance matrix to improve robustness. This yields a novel VIF-H∞ recursion that employs consecutive variational inference and H∞ based optimization steps. We explore the development of this method and investigate a numerical example to illustrate the effectiveness of the proposed filter.

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

Monitoring cavern leaching after each calendar year of oil sales is necessary to support cavern stability efforts and long-term availability for oil drawdowns in the U.S. Strategic Petroleum Reserve. Modeling results from the SANSMIC code and recent sonars are compared to show projected changes in the cavern’s geometry due to leaching from raw-water injections. This report aims to give background on the importance of monitoring cavern leaching and provide a detailed explanation of the process used to create the leaching plots used to monitor cavern leaching. In the past, generating leaching plots for each cavern in a given leaching year was done manually, and every cavern had to be processed individually. A Python script, compatible with Earth Volumetric Studio, was created to automate most of the process. The script makes a total of 26 plots per cavern to show leaching history, axisymmetric representation of leaching, and SANSMIC modeling of future leaching. The current run time for the script is one hour, replacing 40-50 hours of the monitoring cavern leaching process.

The application of traveling wave principles for fault detection in distribution systems is challenging because of multiple reflections from the laterals and other lumped elements, particularly when we consider communication-free applications. We propose and explore the use of Shapelets to characterize fault signatures and a data-driven machine learning model to accurately classify the faults based on their distance. Studies of a simple 5-bus system suggest that the use of Shapelets for detecting faults is promising. The application to practical three-phase distribution feeders is the subject of continuing research.

In accident scenarios involving release of tritium during handling and storage, the level of risk to human health is dominated by the extent to which radioactive tritium is oxidized to the water form (T2O or THO). At some facilities, tritium inventories consist of very small quantities stored at sub-atmospheric pressure, which means that tritium release accident scenarios will likely produce concentrations in air that are well below the lower flammability limit. It is known that isotope effects on reaction rates should result in slower oxidation rates for heavier isotopes of hydrogen, but this effect has not previously been quantified for oxidation at concentrations well below the lower flammability limit for hydrogen. This work describes hydrogen isotope oxidation measurements in an atmospheric tube furnace reactor. These measurements consist of five concentration levels between 0.01% and 1% protium or deuterium and two residence times. Oxidation is observed to occur between about 550°C and 800°C, with higher levels of conversion achieved at lower temperatures for protium with respect to deuterium at the same volumetric inlet concentration and residence time. Computational fluid dynamics simulations of the experiments were used to customize reaction orders and Arrhenius parameters in a 1-step oxidation mechanism. The trends in the rates for protium and deuterium are extrapolated based on guidance from literature to produce kinetic rate parameters appropriate for tritium oxidation at low concentrations.

An array of Wave Energy Converters (WEC) is required to supply a significant power level to the grid. However, the control and optimization of such an array is still an open research question. This paper analyzes two aspects that have a significant impact on the power production. First the spacing of the buoys in a WEC array will be analyzed to determine the optimal shift between the buoys in an array. Then the wave force interacting with the buoys will be angled to create additional sequencing between the electrical signals. A cost function is proposed to minimize the power variation and energy storage while maximizing the delivered energy to the onshore point of common coupling to the electrical grid.

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This paper presents an investigation into sampling strategies for reducing the computational expense of creating error models for steady hypersonic flow surrogate models. The error model describes the quantity of interest error between a reduced-order model prediction and a full-order model solution. The sampling strategies are separated into three categories: distinct training sets, single training set, and augmented single training set for the reduced-order model and the error model. Using a distinct training set, three sampling strategies are investigated: latin hypercube sampling, latin hypercube sampling with a maximin criterion, and a D-Optimal design. It was found that using a D-Optimal design was the most effective at producing an accurate error model with the fewest number of training points. When using a single training set, the leave-one-out cross validation approach was used on the D-Optimal design training set. This produced an error model with an R2 value of greater than 0.8, but it had some outliers due to high nonlinearities in the space. Augmenting the training points of the error model helped improve its accuracy. Using a D-Optimal design with distinct training sets cut the computational cost of creating the error model by 15% and using the LOOCV approach with the D-Optimal design cut the cost by 64%.

Injecting CO2 into a deep geological formation (i.e., geological carbon storage, GCS) can induce earthquakes along preexisting faults in the earth's upper crust. Seismic survey and regional geo-structure analysis are typically employed to map the faults prone to earthquakes prior to injection. However, earthquakes induced by fluid injection from other subsurface energy storage and recovery activities show that systematic evaluation of the potential of induced seismicity associated with GCS is necessary. This study mechanistically investigates how multiphysical interaction among injected CO2, preexisting pore fluids and rock matrix alters stress states on faults and which physical mechanisms can nucleate earthquakes along the faults. Increased injection pressure is needed to overcome capillary entry pressure of the fault zone, driven by the contrast of fluids' wetting characteristics. Accumulated CO2 within the reservoir delays post shut-in reduction in pressure and stress fields along the fault that may enhance the potential for earthquake nucleation after terminating injection operations. Elastic energy generated by coupled processes transfers to low-permeability or hydraulically isolated basement faults, which can initiate slip of the faults. Our findings from generic studies suggest that geomechanical simulations integrated with multiphase flow system are essential to detect deformation-driven signals and mitigate potential seismic hazards associated with CO2 injection.

Large scale non-intrusive inspection (NII) of commercial vehicles is being adopted in the U.S. at a pace and scale that will result in a commensurate growth in adjudication burdens at land ports of entry. The use of computer vision and machine learning models to augment human operator capabilities is critical in this sector to ensure the flow of commerce and to maintain efficient and reliable security operations. The development of models for this scale and speed requires novel approaches to object detection and novel adjudication pipelines. Here we propose a notional combination of existing object detection tools using a novel ensembling framework to demonstrate the potential for hierarchical and recursive operations. Further, we explore the combination of object detection with image similarity as an adjacent capability to provide post-hoc oversight to the detection framework. The experiments described herein, while notional and intended for illustrative purposes, demonstrate that the judicious combination of diverse algorithms can result in a resilient workflow for the NII environment.

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Electrical discharges in humid media are of great interest in biomedical and environmental applications, such as plasma medicine, disinfection, gas treatment, and removal of volatile organic contaminants. Plasmas in high water content produce a mixture of highly reactive species, including reactive oxygen species that are strongly oxidative. We investigate two key reactive species: hydroxyl radical (OH) and hydrogen peroxide (H 2 O2 ) using time-resolved 2D imaging in the afterglow of a dielectric barrier discharge plasma in humid helium, powered by a ns-pulser. The stagnation discharge plane is created by introducing the feed gas concentrically through the powered electrode which impinges on a grounded steel surface.

Snow and ice accumulation on photovoltaic (PV) panels is a recognized-but poorly quantified-contributor to PV performance, not only in geographic areas that see persistent snow in winter but also at lower latitudes, where frozen precipitation and 'snowmageddon' events can wreak havoc with the solar infrastructure. In addition, research on the impact of snow and cold on PV systems has not kept pace with the proliferation of new technologies, the rapid deployment of PV in northern latitudes, and experiences with long-term field performance. This paper describes the value of a dedicated outdoor research facility for longitudinal performance and reliability studies of emerging technologies in cold climates.

We investigate the space complexity of two graph streaming problems: MAX-CUT and its quantum analogue, QUANTUM MAX-CUT. Previous work by Kapralov and Krachun [STOC 19] resolved the classical complexity of the classical problem, showing that any (2 - ?)-approximation requires O(n) space (a 2-approximation is trivial with O(log n) space). We generalize both of these qualifiers, demonstrating O(n) space lower bounds for (2 - ?)-approximating MAX-CUT and QUANTUM MAX-CUT, even if the algorithm is allowed to maintain a quantum state. As the trivial approximation algorithm for QUANTUM MAX-CUT only gives a 4-approximation, we show tightness with an algorithm that returns a (2 + ?)-approximation to the QUANTUM MAX-CUT value of a graph in O(log n) space. Our work resolves the quantum and classical approximability of quantum and classical Max-Cut using o(n) space.We prove our lower bounds through the techniques of Boolean Fourier analysis. We give the first application of these methods to sequential one-way quantum communication, in which each player receives a quantum message from the previous player, and can then perform arbitrary quantum operations on it before sending it to the next. To this end, we show how Fourier-analytic techniques may be used to understand the application of a quantum channel.

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In transit visualization offers a desirable approach to performing in situ visualization by decoupling the simulation and visualization components. This decoupling requires that the data be transferred from the simulation to the visualization, which is typically done using some form of aggregation and redistribution. As the data distribution is adjusted to match the visualization’s parallelism during redistribution, the data transport layer must have knowledge of the input data structures to partition or merge them. In this chapter, we will discuss an alternative approach suitable for quickly integrating in transit visualization into simulations without incurring significant overhead or aggregation cost. Our approach adopts an abstract view of the input simulation data and works only on regions of space owned by the simulation ranks, which are sent to visualization clients on demand.

Many, if not all, Waste Management Organisation programs will include criticality safety. As criticality safety in the long-term, i.e. considered over post-closure timescales in dedicated disposal facilities, is a unique challenge for geological disposal there is limited opportunity for sharing of experience within an individual organization/country. Therefore, sharing of experience and knowledge between WMOs to understand any similarities and differences will be beneficial in understanding where the approaches are similar and where they are not, and the reasons for this. To achieve this benefit a project on Post-Closure Criticality Safety has been established through the Implementing Geological Disposal - Technology Platform with the overall aim to facilitate the sharing of this knowledge. This project currently has 11 participating nations, including the United States and this paper presents the current position in the United States.

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This paper presents a simulation and respective analysis of traveling waves from a 5-bus distribution system connected to a grid-forming inverter (GFMI). The goal is to analyze the numerical differences in traveling waves if a GFMI is used in place of a traditional generator. The paper introduces the topic of traveling waves and their use in distribution systems for fault clearing. Then it introduces a Simulink design of said 5-bus system around which this paper is centered. The system is subject to various simulation tests of which the results and design are explained further in the paper to discuss if and how exactly inverters affect traveling waves and how different design choices for the system can impact these waves. Finally, a consideration is made for what these traveling waves represent in a practical environment and how to properly address them using the information derived in this study.

Weyl points are point degeneracies that occur in momentum space of 3D periodic materials and are associated with a quantized topological charge. Here, the splitting of a quadratic (charge-2) Weyl point into two linear (charge-1) Weyl points in a 3D micro-printed photonic crystal is observed experimentally via Fourier-transform infrared spectroscopy. Using a theoretical analysis rooted in symmetry arguments, it is shown that this splitting occurs along high-symmetry directions in the Brillouin zone. This micro-scale observation and control of Weyl points is important for realizing robust topological devices in the near-infrared.

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The state of charge (SoC) estimated by Battery Management Systems (BMSs) could be vulnerable to False Data Injection Attacks (FDIAs), which aim to disturb state estimation. Inaccurate SoC estimation, due to attacks or suboptimal estimators, could lead to thermal runaway, accelerated degradation of batteries, and other undesirable events. In this paper, an ambient temperature-dependent model is adopted to represent the physics of a stack of three series-connected battery cells, and an Unscented Kalman Filter (UKF) is utilized to estimate the SoC for each cell. A Cumulative Sum (CUSUM) algorithm is used to detect FDIAs targeting the voltage sensors in the battery stack. The UKF was more accurate in state and measurement estimation than the Extended Kalman Filter (EKF) for Maximum Absolute Error (MAE) and Root Mean Squared Error (RMSE). The CUSUM algorithm described in this paper was able to detect attacks as low as ±1 mV when one or more voltage sensor was attacked under various ambient temperatures and attack injection times.

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This user’s guide documents capabilities in Sierra/SolidMechanics which remain “in-development” and thus are not tested and hardened to the standards of capabilities listed in Sierra/SM 5.4 User’s Guide. Capabilities documented herein are available in Sierra/SM for experimental use only until their official release. These capabilities include, but are not limited to, novel discretization approaches such as the conforming reproducing kernel (CRK) method, numerical fracture and failure modeling aids such as the extended finite element method (XFEM) and J-integral, explicit time step control techniques, dynamic mesh rebalancing, as well as a variety of new material models and finite element formulations.

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Geographic analysis of photovoltaic (PV) performance factors across large regions can help relevant stakeholders make informed, and reduced risk decisions. High temporal and spatial resolution meteorological data from the National Solar Radiation Database are used to investigate performance and cost as an effect of varying system characteristics such as the module temperature coefficients, mounting configurations and coatings. The results demonstrated the strong climatic dependence that these characteristics have on annual energy yield whereas the revenues were dominated by the electricity price.

Dynamical systems subject to intermittent contact are often modeled with piecewise-smooth contact forces. However, the discontinuous nature of the contact can cause inaccuracies in numerical results or failure in numerical solvers. Representing the piecewise contact force with a continuous and smooth function can mitigate these problems, but not all continuous representations may be appropriate for this use. In this work, five representations used by previous researchers (polynomial, rational polynomial, hyperbolic tangent, arctangent, and logarithm-arctangent functions) are studied to determine which ones most accurately capture nonlinear behaviors including super- and subharmonic resonances, multiple solutions, and chaos. The test case is a single-DOF forced Duffing oscillator with freeplay nonlinearity, solved using direct time integration. This work intends to expand on past studies by determining the limits of applicability for each representation and what numerical problems may occur.

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

Given a graph, finding the distance-2 maximal independent set (MIS-2) of the vertices is a problem that is useful in several contexts such as algebraic multigrid coarsening or multilevel graph partitioning. Such multilevel methods rely on finding the independent vertices so they can be used as seeds for aggregation in a multilevel scheme. We present a parallel MIS-2 algorithm to improve performance on modern accelerator hardware. This algorithm is implemented using the Kokkos programming model to enable performance portability. We demonstrate the portability of the algorithm and the performance on a variety of architectures (x86/ARM CPUs and NVIDIA/AMD GPUs). The resulting algorithm is also deterministic, producing an identical result for a given input across all of these platforms. The new MIS-2 implementation outperforms implementations in state of the art libraries like CUSP and ViennaCL by 3-8x while producing similar quality results. We further demonstrate the benefits of this approach by developing parallel graph coarsening scheme for two different use cases. First, we develop an algebraic multigrid (AMG) aggregation scheme using parallel MIS-2 and demonstrate the benefits as opposed to previous approaches used in the MueLu multigrid package in Trilinos. We also describe an approach for implementing a parallel multicolor 'cluster' Gauss-Seidel preconditioner using this MIS-2 coarsening, and demonstrate better performance with an efficient, parallel, mul-ticolor Gauss-Seidel algorithm.

There is a need to perform offline anomaly detection in count data streams to simultaneously identify both systemic changes and outliers, simultaneously. We propose a new algorithmic method, called the Anomaly Detection Pipeline, which leverages common statistical process control procedures in a novel way to accomplish this. The method we propose does not require user-defined control or phase I training data, automatically identifying regions of stability for improved parameter estimation to support change point detection. The method does not require data to be normally distributed, and it detects outliers relative to the regimes in which they occur. Our proposed method performs comparably to state-of-the-art change point detection methods, provides additional capabilities, and is extendable to a larger set of possible data streams than known methods.

A sodium pool fire in the containment of a sodium-cooled fast reactor (SFR) plant can occur due to a pipe leak or break. Accumulation of the sodium in a pool would allow the sodium to react with the atmosphere of the containment, such as oxygen, to cause a fire. Sodium fire is important to model because the heat addition and aerosol generation that occur. Any fission products trapped in the leaked sodium coolant may also be released into the containment, which can affect workers and the public if the containment is breached. This paper describes progress of an international collaborative research in SFR sodium fire modeling between the United States and Japan under the framework of the Civil Nuclear Energy Research and Development Working Group (CNWG). In this collaboration between Sandia National Laboratories (SNL) and Japan Atomic Energy Agency (JAEA), the validation basis for and modeling capabilities of sodium pool fires in MELCOR of SNL and SPHINCS of JAEA are being assessed. Additional model improvements for the sodium pool fire in MELCOR are discussed. The MELCOR results for the sodium pool fire model enhancement in MELCOR agreed well with the JAEA's F7 pool fire experiments and compared closely with SPHINCS.

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Many teams struggle to adapt and right-size software engineering best practices for quality assurance to fit their context. Introducing software quality is not usually framed in a way that motivates teams to take action, thus resulting in it becoming a "check the box for compliance"activity instead of a cultural practice that values software quality and the effort to achieve it. When and how can we provide effective incentives for software teams to adopt and integrate meaningful and enduring software quality practices? We explored this question through a persona-based ideation exercise at the 2021 Collegeville Workshop on Scientific Software in which we created three unique personas that represent different scientific software developer perspectives.

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

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The National Academy of Sciences, Engineering, and Medicine (NASEM) defines reproducibility as 'obtaining consistent computational results using the same input data, computational steps, methods, code, and conditions of analysis,' and replicability as 'obtaining consistent results across studies aimed at answering the same scientific question, each of which has obtained its own data' [1]. Due to an increasing number of applications of artificial intelligence and machine learning (AI/ML) to fields such as healthcare and digital medicine, there is a growing need for verifiable AI/ML results, and therefore reproducible research and replicable experiments. This paper establishes examples of irreproducible AI/ML applications to medical sciences and quantifies the variance of common AI/ML models (Artificial Neural Network, Naive Bayes classifier, and Random Forest classifiers) for tasks on medical data sets.

The world's oceans hold a tremendous amount of energy and are a promising resource of renewable energy. Wave Energy Converters (WECs) are a technology being developed to extract the energy from the ocean efficiently and economically. The main components of a WEC include a buoy, an electric machine, an energy storage system, and a connection to the onshore grid. Since the absorption of the energy in the ocean's waves is a complex hydrodynamic process a power-take-off (PTO) mechanism must be used to convert the mechanical motion of the buoy into usable electric energy. This conversion can be done by using a rack-and-pinion gear system to transform the linear velocity of the buoy into a rotational velocity that is used to turn the electric machine. To extract the most energy from the ocean waves a controller must be implemented on the electric machine to make the buoy resonate with the frequency of the waves. For irregular wave climates a multi-resonance controller can be utilized to resonate with the wave spectrum and optimize the power output of the WEC.

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

Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are violated, the existing system security margins will be largely unknown. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of Reinforcement-based Machine Learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. The approach requires the training of a Machine Learning Agent to accomplish this task using modeled data and employs it as a decision support tool under severe, near-blackout conditions.

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Reinforcement Learning (RL) provides an ability to train an artificial intelligent agent in dynamic and uncertain environments. RL has demonstrated an impressive performance capability to learn nearly optimal policies in various application domains including aerospace. Despite the demonstrated performance outcomes of RL, characterizing performance boundaries, explaining the logic behind RL decisions, and quantifying resulting uncertainties in RL outputs are major challenges that slow down the adoption of RL in real-time systems. This is particularly true for aerospace systems where the risk of failure is high and performance envelopes of systems of interest may be small. To facilitate adoption of learning agents in real-time systems, this paper presents a three-part Test and Evaluation (T&E) framework for RL built from Systems engineering for artificial intelligence (SE4AI) perspective. This T&E framework introduces robustness testing approaches to characterize performance bounds on RL, employs Explainable AI techniques, namely Shapley Additive Explanations (SHAP) to examine RL decision-making, and incorporates validation of RL outputs with known and accepted solutions. This framework is applied to a high-speed aerospace vehicle emergency descent problem where RL is trained to provide an angle of attack command and the framework is utilized to comprehensively examine the impact of uncertainties in the vehicle's altitude, velocity, and flight path angle. The robustness testing characterizes acceptable ranges of disturbances in flight parameters, while SHAP exposes the most significant features that impact RL selection of angle of attack-in this case the vehicle altitude. Finally, RL outputs are compared to trajectory generated by indirect optimal control methods for validation.

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Type 2 high-pressure hydrogen vessels for storage at hydrogen refueling stations are designed assuming a predefined operational pressure cycle and targeted autofrettage conditions. However, the resulting finite life depends significantly on variables associated with the autofrettage process and the pressure cycles actually realized during service, which many times are not to the full range of the design. Clear guidance for cycle counting is lacking, therefore industry often defaults to counting every repressurization as a full range pressure cycle, which is an overly conservative approach. In-service pressure cycles used to predict the growth of cracks in operational pressure vessels results in significantly longer life, since most in-service pressure cycles are only a fraction of the full design pressure range. Fatigue crack growth rates can vary widely for a given pressure range depending on the details of the residual strains imparted during the autofrettage process because of their influence on crack driving forces. Small changes in variables associated with the autofrettage process, e.g., the target autofrettage overburden pressure, can result in large changes in the residual stress profile leading to possibly degraded fatigue life. In this paper, computational simulation was used for sensitivity studies to evaluate the effect of both operating conditions and autofrettage conditions on fatigue life for Type 2 highpressure hydrogen vessels. The analysis in this paper explores these sensitivities, and the results are used to provide guidance on cycle counting. In particular, we identify the pressure cycle ranges that can be ignored over the life of the vessel as having negligible effect on fatigue life. This study also examines the sensitivity of design life to the autofrettage process and the impact on life if the targeted residual strain is not achieved during manufacturing.

Abstract not provided.