Publications

Machining-based deformation processing is used to produce metal foil and flat wire (strip) with suitable properties and quality for electrical power and renewable energy applications. In contrast to conventional multistage rolling, the strip is produced in a single-step and with much less process energy. Examples are presented from metal systems of varied workability, and strip product scale in terms of size and production rate. By utilizing the large-strain deformation intrinsic to cutting, bulk strip with ultrafine-grained microstructure, and crystallographic shear-texture favourable for formability, are achieved. Implications for production of commercial strip for electric motor applications and battery electrodes are discussed.

Event-based sensors are a novel sensing technology which capture the dynamics of a scene via pixel-level change detection. This technology operates with high speed (>10 kHz), low latency (10 µs), low power consumption (<1 W), and high dynamic range (120 dB). Compared to conventional, frame-based architectures that consistently report data for each pixel at a given frame rate, event-based sensor pixels only report data if a change in pixel intensity occurred. This affords the possibility of dramatically reducing the data reported in bandwidth-limited environments (e.g., remote sensing) and thus, the data needed to be processed while still recovering significant events. Degraded visual environments, such as those generated by fog, often hinder situational awareness by decreasing optical resolution and transmission range via random scattering of light. To respond to this challenge, we present the deployment of an event-based sensor in a controlled, experimentally generated, well-characterized degraded visual environment (a fog analogue), for detection of a modulated signal and comparison of data collected from an event-based sensor and from a traditional framing sensor.

The structure-property linkage is one of the two most important relationships in materials science besides the process-structure linkage, especially for metals and polycrystalline alloys. The stochastic nature of microstructures begs for a robust approach to reliably address the linkage. As such, uncertainty quantification (UQ) plays an important role in this regard and cannot be ignored. To probe the structure-property linkage, many multi-scale integrated computational materials engineering (ICME) tools have been proposed and developed over the last decade to accelerate the material design process in the spirit of Material Genome Initiative (MGI), notably crystal plasticity finite element model (CPFEM) and phase-field simulations. Machine learning (ML) methods, including deep learning and physics-informed/-constrained approaches, can also be conveniently applied to approximate the computationally expensive ICME models, allowing one to efficiently navigate in both structure and property spaces effortlessly. Since UQ also plays a crucial role in verification and validation for both ICME and ML models, it is important to include UQ in the picture. In this paper, we summarize a few of our recent research efforts addressing UQ aspects of homogenized properties using CPFEM in a big picture context.

The design of thermal protection systems (TPS), including heat shields for reentry vehicles, rely more and more on computational simulation tools for design optimization and uncertainty quantification. Since high-fidelity simulations are computationally expensive for full vehicle geometries, analysts primarily use reduced-physics models instead. Recent work has shown that projection-based reduced-order models (ROMs) can provide accurate approximations of high-fidelity models at a lower computational cost. ROMs are preferable to alternative approximation approaches for high-consequence applications due to the presence of rigorous error bounds. The following paper extends our previous work on projection-based ROMs for ablative TPS by considering hyperreduction methods which yield further reductions in computational cost and demonstrating the approach for simulations of a three-dimensional flight vehicle. We compare the accuracy and potential performance of several different hyperreduction methods and mesh sampling strategies. This paper shows that with the correct implementation, hyperreduction can make ROMs up to 1-3 orders of magnitude faster than the full order model by evaluating the residual at only a small fraction of the mesh nodes.

The error detection performance of cyclic redundancy check (CRC) codes combined with bit framing in digital serial communication systems is evaluated. Advantages and disadvantages of the combined method are treated in light of the probability of undetected errors. It is shown that bit framing can increase the burst error detection of the CRC but it can also adversely affect CRC random error detection performance. To quantify the effect of bit framing on CRC error detection the concept of error "exposure"is introduced. Our investigations lead us to propose resilient generator polynomials that, when combined with bit framing, can result in improved CRC error detection performance at no additional implementation cost. Example results are generated for short codewords showing that proper choice of CRC generator polynomial can improve error detection performance when combined with bit framing. The implication is that CRC combined with bit framing can reduce the probability of undetected errors even under high error rate conditions.

Kolmogorov's theory of turbulence assumes that the small-scale turbulent structures in the energy cascade are universal and are determined by the energy dissipation rate and the kinematic viscosity alone. However, thermal fluctuations, absent from the continuum description, terminate the energy cascade near the Kolmogorov length scale. Here, we propose a simple superposition model to account for the effects of thermal fluctuations on small-scale turbulence statistics. For compressible Taylor-Green vortex flow, we demonstrate that the superposition model in conjunction with data from direct numerical simulation of the Navier-Stokes equations yields spectra and structure functions that agree with the corresponding quantities computed from the direct simulation Monte Carlo method of molecular gas dynamics, verifying the importance of thermal fluctuations in the dissipation range.

The novel Hydromine harvests energy from flowing water with no external moving parts, resulting in a robust system with minimal environmental impact. Here two deployment scenarios are considered: an offshore floating platform configuration to capture energy from relatively steady ocean currents at megawatt-scale, and a river-based system at kilowatt-scale mounted on a pylon. Hydrodynamic and techno-economic models are developed. The hydrodynamic models are used to maximize the efficiency of the power conversion. The techno-economic models optimize the system size and layout and ultimately seek to minimize the levelized-cost-of-electricity produced. Parametric and sensitivity analyses are performed on the models to optimize performance and reduce costs.

Extreme meteorological events, such as hurricanes and floods, cause significant infrastructure damage and, as a result, prolonged grid outages. To mitigate the negative effect of these outages and enhance the resilience of communities, microgrids consisting of solar photovoltaics (PV), energy storage (ES) technologies, and backup diesel generation are being considered. Furthermore, it is necessary to take into account how the extreme event affects the systems' performance during the outage, often referred to as black-sky conditions. In this paper, an optimization model is introduced to properly size ES and PV technologies to meet various duration of grid outages for selected critical infrastructure while considering black-sky conditions. A case study of the municipality of Villalba, Puerto Rico is presented to identify the several potential microgrid configurations that increase the community's resilience. Sensitivity analyses are performed around the grid outage durations and black-sky conditions to better decide what factors should be considered when scoping potential microgrids for community resilience.

The widespread adoption of residential solar PV requires distribution system studies to ensure the addition of solar PV at a customer location does not violate the system constraints, which can be referred to as locational hosting capacity (HC). These model-based analyses are prone to error due to their dependencies on the accuracy of the system information. Model-free approaches to estimate the solar PV hosting capacity for a customer can be a good alternative to this approach as their accuracies do not depend on detailed system information. In this paper, an Adaptive Boosting (AdaBoost) algorithm is deployed to utilize the statistical properties (mean, minimum, maximum, and standard deviation) of the customer's historical data (real power, reactive power, voltage) as inputs to estimate the voltage-constrained PV HC for the customer. A baseline comparison approach is also built that utilizes just the maximum voltage of the customer to predict PV HC. The results show that the ensemble-based AdaBoost algorithm outperformed the proposed baseline approach. The developed methods are also compared and validated by existing state-of-the-art model-free PV HC estimation methods.

In this work, a modular and open-source platform has been developed for integrating hybrid battery energy storage systems that are intended for grid applications. Alongside integration, this platform will facilitate testing and optimal operation of hybrid storage technologies. Here, a hardware testbed and a control software have been designed, where the former comprises commercial Lithium-iron-phosphate (LiFePO4) and Lead Acid (Pb - acid) cells, custom built Dual Active Bridge (DAB) DC-DC converters, and a commercial DC-AC conversion system. In this testbed the batteries have an operating voltage range of 11-15V, the DC-AC conversion stage has a DC link voltage of 24V, and it connects to a 208V3-φ grid. The hardware testbed can be scaled up to higher voltages. The control software is developed in Python, and the firmware for all the hardware components is developed in C. This software implements hybrid charge/discharge protocols that are suitable for each battery technology for preventing cell degradation, and perform uninter-rupted quality checks on selected battery packs. The developed platform provides flexibility, modularity, safety and economic benefits to utility-scale storage integration.

The increased complexity of high-consequence digital system designs with intricate interactions between numerous components has placed a greater need on ensuring that the design satisfies its intended requirements. This digital assurance can only come about through rigorous mathematical analysis of the design. This manuscript provides a detailed description of a formal language semantics that can be used for modeling and verification of systems. We use Event-B to build a formalized semantics that supports the construction of triggered enable statecharts with a run-to-completion scheduling. Rodin has previously been used to develop and analyse models using this semantics.

As the width and depth of quantum circuits implemented by state-of-the-art quantum processors rapidly increase, circuit analysis and assessment via classical simulation are becoming unfeasible. It is crucial, therefore, to develop new methods to identify significant error sources in large and complex quantum circuits. In this work, we present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most and thus helps to identify the most significant sources of error. The technique requires no classical verification of the circuit output and is thus a scalable tool for debugging large quantum programs in the form of circuits. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.

A quantum-cascade-laser-absorption-spectroscopy (QCLAS) diagnostic was used to characterize post-detonation fireballs of RP-80 detonators via measurements of temperature, pressure, and CO column pressure at a repetition rate of 1 MHz. Scanned-wavelength direct-absorption spectroscopy was used to measure CO absorbance spectra near 2008.5 cm−1 which are dominated by the P(0,31), P(2,20), and P(3,14) transitions. Line-of-sight (LOS) measurements were acquired 51 and 91 mm above the detonator surface. Three strategies were employed to facilitate interpretation of the LAS measurements in this highly nonuniform environment and to evaluate the accuracy of four post-detonation fireball models: (1) High-energy transitions were used to deliberately bias the measurements to the high-temperature outer shell, (2) a novel dual-zone absorption model was used to extract temperature, pressure, and CO measurements in two distinct regions of the fireball at times where pressure variations along the LOS were pronounced, and (3) the LAS measurements were compared with synthetic LAS measurements produced using the simulated distributions of temperature, pressure, and gas composition predicted by reactive CFD modeling. The results indicate that the QCLAS diagnostic provides high-fidelity data for evaluating post-detonation fireball models, and that assumptions regarding thermochemical equilibrium and carbon freeze-out during expansion of detonation gases have a large impact on the predicted chemical composition of the fireball.

The Sliding Scale of Cybersecurity is a framework for understanding the actions that contribute to cybersecurity. The model consists of five categories that provide varying value towards cybersecurity and incur varying implementation costs. These categories range from offensive cybersecurity measures providing the least value and incurring the greatest cost, to architecture providing the greatest value and incurring the least cost. This paper presents an application of the Sliding Scale of Cybersecurity to the Tiered Cybersecurity Analysis (TCA) of digital instrumentation and control systems for advanced reactors. The TCA consists of three tiers. Tier 1 is design and impact analysis. In Tier 1 it is assumed that the adversary has control over all digital systems, components, and networks in the plant, and that the adversary is only constrained by the physical limitations of the plant design. The plant’s safety design features are examined to determine whether the consequences of an attack by this cyber-enabled adversary are eliminated or mitigated. Accident sequences that are not eliminated or mitigated by security by design features are examined in Tier 2 analysis. In Tier 2, adversary access pathways are identified for the unmitigated accident sequences, and passive measures are implemented to deny system and network access to those pathways wherever feasible. Any systems with remaining susceptible access pathways are then examined in Tier 3. In Tier 3, active defensive cybersecurity architecture features and cybersecurity plan controls are applied to deny the adversary the ability to conduct the tasks needed to cause a severe consequence. Earlier application of the TCA in the design process provides greater opportunity for an efficient graded approach and defense-in-depth.

We present the SEU sensitivity and SEL results from proton and heavy ion testing performed on NVIDIA Xavier NX and AMD Ryzen V1605B GPU devices in both static and dynamic operation.

This paper presents a die-embedded glass interposer with minimum warpage for 5G/6G applications. The interposer performs high integration with low-loss interconnects by embedding multiple chips in the same glass substrate and interconnecting the chips through redistributive layers (RDL). Novel processes for cavity creation, multi-die embedding, carrier- less RDL build up and heat spreader attachment are proposed and demonstrated in this work. Performance of the interposer from 1 GHz to 110 GHz are evaluated. This work provides an advanced packaging solution for low-loss die-to-die and die-to-package interconnects, which is essential to high performance wireless system integration.

This paper describes the methodology of designing a replacement blade tip and winglet for a wind turbine blade to demonstrate the potential of additive-manufacturing for wind energy. The team will later field-demonstrate this additive-manufactured, system-integrated tip (AMSIT) on a wind turbine. The blade tip aims to reduce the cost of wind energy by improving aerodynamic performance and reliability, while reducing transportation costs. This paper focuses on the design and modeling of a winglet for increased power production while maintaining acceptable structural loads of the original Vestas V27 blade design. A free-wake vortex model, WindDVE, was used for the winglet design analysis. A summary of the aerodynamic design process is presented along with a case study of a specific design.

Modern Industrial Control Systems (ICS) attacks evade existing tools by using knowledge of ICS processes to blend their activities with benign Supervisory Control and Data Acquisition (SCADA) operation, causing physical world damages. We present Scaphy to detect ICS attacks in SCADA by leveraging the unique execution phases of SCADA to identify the limited set of legitimate behaviors to control the physical world in different phases, which differentiates from attacker's activities. For example, it is typical for SCADA to setup ICS device objects during initialization, but anomalous during process-control. To extract unique behaviors of SCADA execution phases, Scaphy first leverages open ICS conventions to generate a novel physical process dependency and impact graph (PDIG) to identify disruptive physical states. Scaphy then uses PDIG to inform a physical process-aware dynamic analysis, whereby code paths of SCADA process-control execution is induced to reveal API call behaviors unique to legitimate process-control phases. Using this established behavior, Scaphy selectively monitors attacker's physical world-targeted activities that violates legitimate process-control behaviors. We evaluated Scaphy at a U.S. national lab ICS testbed environment. Using diverse ICS deployment scenarios and attacks across 4 ICS industries, Scaphy achieved 95% accuracy & 3.5% false positives (FP), compared to 47.5% accuracy and 25% FP of existing work. We analyze Scaphy's resilience to futuristic attacks where attacker knows our approach.

Pulsed dielectric barrier discharges (DBD) in He-H2O and He-H2O-O2 mixtures are studied in near atmospheric conditions using temporally and spatially resolved quantitative 2D imaging of the hydroxyl radical (OH) and hydrogen peroxide (H2O2). The primary goal was to detect and quantify the production of these strongly oxidative species in water-laden helium discharges in a DBD jet configuration, which is of interest for biomedical applications such as disinfection of surfaces and treatment of biological samples. Hydroxyl profiles are obtained by laser-induced fluorescence (LIF) measurements using 282 nm laser excitation. Hydrogen peroxide profiles are measured by photo-fragmentation LIF (PF-LIF), which involves photo-dissociating H2O2 into OH with a 212.8 nm laser sheet and detecting the OH fragments by LIF. The H2O2 profiles are calibrated by measuring PF-LIF profiles in a reference mixture of He seeded with a known amount of H2O2. OH profiles are calibrated by measuring OH-radical decay times and comparing these with predictions from a chemical kinetics model. Two different burst discharge modes with five and ten pulses per burst are studied, both with a burst repetition rate of 50 Hz. In both cases, dynamics of OH and H2O2 distributions in the afterglow of the discharge are investigated. Gas temperatures determined from the OH-LIF spectra indicate that gas heating due to the plasma is insignificant. The addition of 5% O2 in the He admixture decreases the OH densities and increases the H2O2 densities. The increased coupled energy in the ten-pulse discharge increases OH and H2O2 mole fractions, except for the H2O2 in the He-H2O-O2 mixture which is relatively insensitive to the additional pulses.

Filamentous fungi can synthesize a variety of nanoparticles (NPs), a process referred to as mycosynthesis that requires little energy input, do not require the use of harsh chemicals, occurs at near neutral pH, and do not produce toxic byproducts. While NP synthesis involves reactions between metal ions and exudates produced by the fungi, the chemical and biochemical parameters underlying this process remain poorly understood. Here, the role of fungal species and precursor salt on the mycosynthesis of zinc oxide (ZnO) NPs is investigated. This data demonstrates that all five fungal species tested are able to produce ZnO structures that can be morphologically classified into i) well-defined NPs, ii) coalesced/dissolving NPs, and iii) micron-sized square plates. Further, species-dependent preferences for these morphologies are observed, suggesting potential differences in the profile or concentration of the biochemical constituents in their individual exudates. This data also demonstrates that mycosynthesis of ZnO NPs is independent of the anion species, with nitrate, sulfate, and chloride showing no effect on NP production. Finally, these results enhance the understanding of factors controlling the mycosynthesis of ceramic NPs, supporting future studies that can enable control over the physical and chemical properties of NPs formed through this “green” synthesis method.

Puerto Rico faced a double strike from hurricanes Irma and Maria in 2017. The resulting damage required a comprehensive rebuild of electric infrastructure. There are plans and pilot projects to rebuild with microgrids to increase resilience. This paper provides a techno-economic analysis technique and case study of a potential future community in Puerto Rico that combines probabilistic microgrid design analysis with tiered circuits in building energy modeling. Tiered circuits in buildings allow electric load reduction via remote disconnection of non-critiñl circuits during an emergency. When coupled to a microgrid, tiered circuitry can reduce the chances of a microgrid's storage and generation resources being depleted. The analysis technique is applied to show 1) Approximate cost savings due to a tiered circuit structure and 2) Approximate cost savings gained by simultaneously considering resilience and sustainability constraints in the microgrid optimization. The analysis technique uses a resistive capacitive thermal model with load profiles for four tiers (tier 1-3 and non-critical loads). Three analyses were conducted using: 1) open-source software called Tiered Energy in Buildings and 2) the Microgrid Design Toolkit. For a fossil fuel based microgrid 30% of the total microgrid costs of 1.18 million USD were calculated where the non-tiered case keeps all loads 99.9% available and the tiered case keeps tier 1 at 99.9%, tier 2 at 95%, tier 3 at 80% availability, with no requirement on non-critical loads. The same comparison for a sustainable microgrid showed 8% cost savings on a 5.10 million USD microgrid due to tiered circuits. The results also showed 6-7% cost savings when our analysis technique optimizes sustainability and resilience simultaneously in comparison to doing microgrid resilience analysis and renewables net present value analysis independently. Though highly specific to our case study, similar assessments using our analysis technique can elucidate value of tiered circuits and simultaneous consideration of sustainability and resilience in other locations.

High reliability (Hi-Rel) electronics for mission critical applications are handled with extreme care; stress testing upon full assembly can increase a likelihood of degrading these systems before their deployment. Moreover, novel material parts, such as wide bandgap semiconductor devices, tend to have more complicated fabrication processing needs which could ultimately result in larger part variability or potential defects. Therefore, an intelligent screening and inspection technique for electronic parts, in particular gallium nitride (GaN) power transistors, is presented in this paper. We present a machine-learning-based non-intrusive technique that can enhance part-selection decisions to categorize the part samples to the population's expected electrical characteristics. This technique provides relevant information about GaN HEMT device characteristics without having to operate all of these devices at the high current region of the transfer and output characteristics, lowering the risk of damaging the parts prematurely. The proposed non-intrusive technique uses a small signal pulse width modulation (PWM) of various frequencies, ranging from 10 kHz to 500 kHz, injected into the transistor terminals and the corresponding output signals are observed and used as training dataset. Unsupervised clustering techniques with K-means and feature dimensional reduction through principal component analysis (PCA) have been used to correlate a population of GaN HEMT transistors to the expected mean of the devices' electrical characteristic performance.

A method is presented to detect clear-sky periods for plane-of-array, time-averaged irradiance data that is based on the algorithm originally described by Reno and Hansen. We show this new method improves the state-of-the-art by providing accurate detection at longer data intervals, and by detecting clear periods in plane-of-array data, which is novel. We illustrate how accurate determination of clear-sky conditions helps to eliminate data noise and bias in the assessment of long-term performance of PV plants.

Phosphor thermometry has become an established remote sensing technique for acquiring the temperature of surfaces and gas-phase flows. Often, phosphors are excited by a light source (typically emitting in the UV region), and their temperature-sensitive emission is captured. Temperature can be inferred from shifts in the emission spectra or the radiative decay lifetime during relaxation. While recent work has shown that the emission of several phosphors remains thermographic during x-ray excitation, the radiative decay lifetime was not investigated. The focus of the present study is to characterize the lifetime decay of the phosphor Gd2O2S:Tb for temperature sensitivity after excitation from a pulsed x-ray source. These results are compared to the lifetime decays found for this phosphor when excited using a pulsed UV laser. Results show that the lifetime of this phosphor exhibits comparable sensitivity to temperature between both excitation sources for a temperature range between 21 °C to 140 °C in increments of 20 °C. This work introduces a novel method of thermometry for researchers to implement when employing x-rays for diagnostics.

Multiple rotors on single structures have long been proposed to increase wind turbine energy capture with no increase in rotor size, but at the cost of additional mechanical complexity in the yaw and tower designs. Standard turbines on their own very-closely-spaced towers avoid these disadvantages but create a significant disadvantage; for some wind directions the wake turbulence of a rotor enters the swept area of a very close downwind rotor causing low output, fatigue stress, and changes in wake recovery. Knowing how the performance of pairs of closely spaced rotors varies with wind direction is essential to design a layout that maximizes the useful directions and minimizes the losses and stress at other directions. In the current work, the high-fidelity large-eddy simulation (LES) code Exa-Wind/Nalu-Wind is used to simulate the wake interactions from paired-rotor configurations in a neutrally stratified atmospheric boundary layer to investigate performance and feasibility. Each rotor pair consists of two Vestas V27 turbines with hub-to-hub separation distances of 1.5 rotor diameters. The on-design wind direction results are consistent with previous literature. For an off-design wind direction of 26.6°, results indicate little change in power and far-wake recovery relative to the on-design case. At a direction of 45.0°, significant rotor-wake interactions produce an increase in power but also in far-wake velocity deficit and turbulence intensity. A severely off-design case is also considered.

Complex angle theory can offer new fundamental insights into refraction at the absorptive interface. In this work we propose a new method to induce isofrequency opening via addition of scattering in the dual interface system.

The V31 containment vessel was procured by the US Army Recovered Chemical Material Directorate (RCMD) as a third-generation EDS containment vessel. It is the fifth EDS vessel to be fabricated under Code Case 2564 of the 2019 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 24 lb (11 kg) TNT-equivalent for up to 1092 detonations. This report documents the results of explosive tests that were performed on the vessel at Sandia National Laboratories in Albuquerque, New Mexico to qualify the vessel for field operations use. There were three design basis configurations for qualification testing. Qualification test (1) consisted of a simulated M55 rocket motor and warhead assembly of 24 lb (11 kg) of Composition C-4 (30 lb [14 kg] TNT equivalent). This test was considered the maximum load case, based on modeling and simulation methods performed by Sandia prior to the vessel design phase. Qualification test (2) consisted of a regular, right circular cylinder, unitary charge, located central to the vessel interior of 19.2 lb (8.72 kg) of Composition C-4 (24 lb [11 kg] TNT equivalent). Qualification test (3) consisted of a 12-pack of regular, right circular cylinders of 2 lb (908 g) each, distributed evenly inside the vessel (totaling 19.2 lb [8.72 kg] of C-4, or 24 lb [11 kg] TNT equivalent). All vessel acceptance criteria were met.

This presentation describes a new effort to better understand insulator flashover in high current, high voltage pulsed power systems. Both experimental and modeling investigations are described. Particular emphasis is put upon understand flashover that initiate in the anode triple junction (anode-vacuum-dielectric).

Measurements of gas-phase temperature and pressure in hypersonic flows are important for understanding gas-phase fluctuations which can drive dynamic loading on model surfaces and to study fundamental compressible flow turbulence. To achieve this capability, femtosecond coherent anti-Stokes Raman scattering (fs CARS) is applied in Sandia National Laboratories’ cold-flow hypersonic wind tunnel facility. Measurements were performed for tunnel freestream temperatures of 42–58 K and pressures of 1.5–2.2 Torr. The CARS measurement volume was translated in the flow direction during a 30-second tunnel run using a single computer-controlled translation stage. After broadband femtosecond laser excitation, the rotational Raman coherence was probed twice, once at an early time where the collisional environment has not affected the Raman coherence, and another at a later time after the collisional environment has led to significant dephasing of the Raman coherent. The gas-phase temperature was obtained primarily from the early-probe CARS spectra, while the gas-phase pressure was obtained primarily from the late-probe CARS spectra. Challenges in implementing fs CARS in this facility such as changes in the nonresonant spectrum at different measurement location are discussed.

Multiple Input Multiple Output (MIMO) vibration testing provides the capability to expose a system to a field environment in a laboratory setting, saving both time and money by mitigating the need to perform multiple and costly large-scale field tests. However, MIMO vibration test design is not straightforward oftentimes relying on engineering judgment and multiple test iterations to determine the proper selection of response Degree of Freedom (DOF) and input locations that yield a successful test. This work investigates two DOF selection techniques for MIMO vibration testing to assist with test design, an iterative algorithm introduced in previous work and an Optimal Experiment Design (OED) approach. The iterative-based approach downselects the control set by removing DOF that have the smallest impact on overall error given a target Cross Power Spectral Density matrix and laboratory Frequency Response Function (FRF) matrix. The Optimal Experiment Design (OED) approach is formulated with the laboratory FRF matrix as a convex optimization problem and solved with a gradient-based optimization algorithm that seeks a set of weighted measurement DOF that minimize a measure of model prediction uncertainty. The DOF selection approaches are used to design MIMO vibration tests using candidate finite element models and simulated target environments. The results are generalized and compared to exemplify the quality of the MIMO test using the selected DOF.

Two relatively under-reported facets of fuel storage fire safety are examined in this work for a 250, 000 gallon two-tank storage system. Ignition probability is linked to the radiative flux from a presumed fire. First, based on observed features of existing designs, fires are expected to be largely contained within a designed footprint that will hold the full spilled contents of the fuel. The influence of the walls and the shape of the tanks on the magnitude of the fire is not a well-described aspect of conventional fire safety assessment utilities. Various resources are herein used to explore the potential hazard for a contained fire of this nature. Second, an explosive attack on the fuel storage has not been widely considered in prior work. This work explores some options for assessing this hazard. The various methods for assessing the constrained conventional fires are found to be within a reasonable degree of agreement. This agreement contrasts with the hazard from an explosive dispersal. Best available assessment techniques are used, which highlight some inadequacies in the existing toolsets for making predictions of this nature. This analysis, using the best available tools, suggests the offset distance for the ignition hazard from a fireball will be on the same order as the offset distance for the blast damage. This suggests the buy-down of risk by considering the fireball is minimal when considering the blast hazards. Assessment tools for the fireball predictions are not particularly mature, and ways to improve them for a higher-fidelity estimate are noted.

We propose a set of benchmark tests for current-voltage (IV) curve fitting algorithms. Benchmark tests enable transparent and repeatable comparisons among algorithms, allowing for measuring algorithm improvement over time. An absence of such tests contributes to the proliferation of fitting methods and inhibits achieving consensus on best practices. Benchmarks include simulated curves with known parameter solutions, with and without simulated measurement error. We implement the reference tests on an automated scoring platform and invite algorithm submissions in an open competition for accurate and performant algorithms.

Advanced finite-element discretizations and preconditioners for models of poroelasticity have attracted significant attention in recent years. The equations of poroelasticity offer significant challenges in both areas, due to the potentially strong coupling between unknowns in the system, saddle-point structure, and the need to account for wide ranges of parameter values, including limiting behavior such as incompressible elasticity. This paper was motivated by an attempt to develop monolithic multigrid preconditioners for the discretization developed in [C. Rodrigo et al., Comput. Methods App. Mech. Engrg, 341 (2018), pp. 467-484]; we show here why this is a difficult task and, as a result, we modify the discretization in [Rodrigo et al.] through the use of a reduced-quadrature approximation, yielding a more “solver-friendly” discretization. Local Fourier analysis is used to optimize parameters in the resulting monolithic multigrid method, allowing a fair comparison between the performance and costs of methods based on Vanka and Braess-Sarazin relaxation. Numerical results are presented to validate the local Fourier analysis predictions and demonstrate efficiency of the algorithms. Finally, a comparison to existing block-factorization preconditioners is also given.

We propose a set of benchmark tests for current-voltage (IV) curve fitting algorithms. Benchmark tests enable transparent and repeatable comparisons among algorithms, allowing for measuring algorithm improvement over time. An absence of such tests contributes to the proliferation of fitting methods and inhibits achieving consensus on best practices. Benchmarks include simulated curves with known parameter solutions, with and without simulated measurement error. We implement the reference tests on an automated scoring platform and invite algorithm submissions in an open competition for accurate and performant algorithms.

The DevOps movement, which aims to accelerate the continuous delivery of high-quality software, has taken a leading role in reshaping the software industry. Likewise, there is growing interest in applying DevOps tools and practices in the domains of computational science and engineering (CSE) to meet the ever-growing demand for scalable simulation and analysis. Translating insights from industry to research computing, however, remains an ongoing challenge; DevOps for science and engineering demands adaptation and innovation in those tools and practices. There is a need to better understand the challenges faced by DevOps practitioners in CSE contexts in bridging this divide. To that end, we conducted a participatory action research study to collect and analyze the experiences of DevOps practitioners at a major US national laboratory through the use of storytelling techniques. We share lessons learned and present opportunities for future investigation into DevOps practice in the CSE domain.

Prescriptive approaches for the cybersecurity of digital nuclear instrumentation and control (I&C) systems can be cumbersome and costly. These considerations are of particular concern for advanced reactors that implement digital technologies for monitoring, diagnostics, and control. A risk-informed performance-based approach is needed to enable the efficient design of secure digital I&C systems for nuclear power plants. This paper presents a tiered cybersecurity analysis (TCA) methodology as a graded approach for cybersecurity design. The TCA is a sequence of analyses that align with the plant, system, and component stages of design. Earlier application of the TCA in the design process provides greater opportunity for an efficient graded approach and defense-in-depth. The TCA consists of three tiers. Tier 1 is design and impact analysis. In Tier 1 it is assumed that the adversary has control over all digital systems, components, and networks in the plant, and that the adversary is only constrained by the physical limitations of the plant design. The plant's safety design features are examined to determine whether the consequences of an attack by this cyber-enabled adversary are eliminated or mitigated. Accident sequences that are not eliminated or mitigated by security by design features are examined in Tier 2 analysis. In Tier 2, adversary access pathways are identified for the unmitigated accident sequences, and passive measures are implemented to deny system and network access to those pathways wherever feasible. Any systems with remaining susceptible access pathways are then examined in Tier 3. In Tier 3, active defensive cybersecurity architecture features and cybersecurity plan controls are applied to deny the adversary the ability to conduct the tasks needed to cause a severe consequence. Tier 3 is not performed in this analysis because of the design maturity required for this tier of analysis.

Austenitic stainless steels are used in high-pressure hydrogen containment infrastructure for their resistance to hydrogen embrittlement. Applications for the use of austenitic stainless steels include pressure vessels, tubing, piping, valves, fittings and other piping components. Despite their resistance to brittle behavior in the presence of hydrogen, austenitic stainless steels can exhibit degraded fracture performance. The mechanisms of hydrogen-assisted fracture, however, remain elusive, which has motivated continued research on these alloys. There are two principal approaches to evaluate the influence of gaseous hydrogen on mechanical properties: internal and external hydrogen, respectively. The austenite phase has high solubility and low diffusivity of hydrogen at room temperature, which enables introduction of hydrogen into the material through thermal precharging at elevated temperature and pressure; a condition referred to as internal hydrogen. H-precharged material can subsequently be tested in ambient conditions. Alternatively, mechanical testing can be performed while test coupons are immersed in gaseous hydrogen thereby evaluating the effects of external hydrogen on property degradation. The slow diffusivity of hydrogen in austenite at room temperature can often be a limiting factor in external hydrogen tests and may not properly characterize lower bound fracture behavior in components exposed to hydrogen for long time periods. In this study, the differences between internal and external hydrogen environments are evaluated in the context of fracture resistance measurements. Fracture testing was performed on two different forged austenitic stainless steel alloys (304L and XM-11) in three different environments: 1) non-charged and tested in gaseous hydrogen at pressure of 1,000 bar (external H2), 2) hydrogen precharged and tested in air (internal H), 3) hydrogen precharged and tested in 1,000 bar H2 (internal H + external H2). For all environments, elastic-plastic fracture measurements were conducted to establish J-R curves following the methods of ASTM E1820. Following fracture testing, fracture surfaces were examined to reveal predominant fracture mechanisms for the different conditions and to characterize differences (and similarities) in the macroscale fracture processes associated with these environmental conditions.

Despite state-of-the-art deep learning-based computer vision models achieving high accuracy on object recognition tasks, x-ray screening of baggage at checkpoints is largely performed by hand. Part of the challenge in automation of this task is the relatively small amount of available labeled training data. Furthermore, realistic threat objects may have forms or orientations that do not appear in any training data, and radiographs suffer from high amounts of occlusion. Using deep generative models, we explore data augmentation techniques to expand the intra-class variation of threat objects synthetically injected into baggage radiographs using openly available baggage x-ray datasets. We also benchmark the performance of object detection algorithms on raw and augmented data.

In this study, we develop an end-to-end deep learning-based inverse design approach to determine the scatterer shape necessary to achieve a target acoustic field. This approach integrates non-uniform rational B-spline (NURBS) into a convolutional autoencoder (CAE) architecture while concurrently leveraging (in a weak sense) the governing physics of the acoustic problem. By utilizing prior physical knowledge and NURBS parameterization to regularize the ill-posed inverse problem, this method does not require enforcing any geometric constraint on the inverse design space, hence allowing the determination of scatterers with potentially any arbitrary shape (within the set allowed by NURBS). A numerical study is presented to showcase the ability of this approach to identify physically-consistent scatterer shapes capable of producing user-defined acoustic fields.

Creation of a Sandia internally developed, shock-hardened Recoverable Data Recorder (RDR) necessitated experimentation by ballistically-firing the device into water targets at velocities up to 5,000 ft/s. The resultant mechanical environments were very severe—routinely achieving peak accelerations in excess of 200 kG and changes in pseudo-velocity greater than 38,000 inch/s. High-quality projectile deceleration datasets were obtained though high-speed imaging during the impact events. The datasets were then used to calibrate and validate computational models in both CTH and EPIC. Hydrodynamic stability in these environments was confirmed to differ from aerodynamic stability; projectile stability is maintained through a phenomenon known as “tail-slapping” or impingement of the rear of the projectile on the cavitation vapor-water interface which envelopes the projectile. As the projectile slows the predominate forces undergo a transition which is outside the codes’ capabilities to calculate accurately, however, CTH and EPIC both predict the projectile trajectory well in the initial hypervelocity regime. Stable projectile designs and the achievable acceleration space are explored through a large parameter sweep of CTH simulations. Front face chamfer angle has the largest influence on stability with low angles being more stable.

Here we examine models for particle curtain dispersion using drag based formalisms and their connection to streamwise pressure difference closures. Focusing on drag models, we specifically demonstrate that scaling arguments developed in DeMauro et. al. [1] using early time drag modeling can be extended to include late time particle curtain dispersion behavior by weighting the dynamic portion of the drag relative velocity e.g. (Formula Presented) by the inverse of the particle volume fraction to the ¼th power. The additional parameter e.g. α introduced in this scaling is related to the model drag parameters by employing an early-time latetime matching argument. Comparison with the scaled measurements of DeMauro et. al. suggest that the proposed modification is an effective formalism. Next, the connection between drag-based models and streamwise pressure difference-based expressions is explored by formulating simple analytical models that verify an empirical (Daniel and Wagner [2]) upstream-downstream expression. Though simple, these models provide physics-based approached describing shock particle curtain interaction behavior.

Carbonaceous particulate produced by a diesel engine and turbojet engine combustor are analyzed by transmission electron microscopy (TEM) for differences in nanostructure before and after pulsed laser annealing. Soot is examined between low/high diesel engine torque and low/high turbojet engine thrust. Small differences in nascent nanostructure are magnified by the action of high-temperature annealing induced by pulsed laser heating. Lamellae length distributions show occurrence of graphitization while tortuosity analyses reveal lamellae straightening. Differences in internal particle structure (hollow shells versus internal graphitic ribbons) are interpreted as due to higher internal sp3 and O-atom content under the higher power conditions with hypothesized greater turbulence and resulting partial premixing. TEM in concert with fringe analyses reveal that a similar degree of annealing occurs in the primary particles in soot from both diesel engine and turbojet engine combustors—despite the aggregate and primary size differences between these sources. Implications of these results for source identification of the combustion particulate and for laser-induced incandescence (LII) measurements of concentration are discussed with inter-instrument comparison of soot mass from both diesel and turbojet soot sources.

Geomagnetic disturbances (GMDs) give rise to geomagnetically induced currents (GICs) on the earth's surface which find their way into power systems via grounded transformer neutrals. The quasi-dc nature of the GICs results in half-cycle saturation of the power grid transformers which in turn results in transformer failure, life reduction, and other adverse effects. Therefore, transformers need to be more resilient to dc excitation. This paper sets forth dc immunity metrics for transformers. Furthermore, this paper sets forth a novel transformer architecture and a design methodology which employs the dc immunity metrics to make it more resilient to dc excitation. This is demonstrated using a time-stepping 2D finite element analysis (FEA) simulation. It was found that a relatively small change in the core geometry significantly increases transformer resiliency with respect to dc excitation.

High-altitude electromagnetic pulse events are a growing concern for electric power grid vulnerability assessments and mitigation planning, and accurate modeling of surge arrester mitigations installed on the grid is necessary to predict pulse effects on existing equipment and to plan future mitigation. While some models of surge arresters at high frequency have been proposed, experimental backing for any given model has not been shown. This work examines a ZnO lightning surge arrester modeling approach previously developed for accurate prediction of nanosecond-scale pulse response. Four ZnO metal-oxide varistor pucks with different sizes and voltage ratings were tested for voltage and current response on a conducted electromagnetic pulse testbed. The measured clamping response was compared to SPICE circuit models to compare the electromagnetic pulse response and validate model accuracy. Results showed good agreement between simulation results and the experimental measurements, after accounting for stray testbed inductance between 100 and 250 nH.

Computational simulation allows scientists to explore, observe, and test physical regimes thought to be unattainable. Validation and uncertainty quantification play crucial roles in extrapolating the use of physics-based models. Bayesian analysis provides a natural framework for incorporating the uncertainties that undeniably exist in computational modeling. However, the ability to perform quality Bayesian and uncertainty analyses is often limited by the computational expense of first-principles physics models. In the absence of a reliable low-fidelity physics model, phenomenological surrogate or machine learned models can be used to mitigate this expense; however, these data-driven models may not adhere to known physics or properties. Furthermore, the interactions of complex physics in high-fidelity codes lead to dependencies between quantities of interest (QoIs) that are difficult to quantify and capture when individual surrogates are used for each observable. Although this is not always problematic, predicting multiple QoIs with a single surrogate preserves valuable insights regarding the correlated behavior of the target observables and maximizes the information gained from available data. A method of constructing a Gaussian Process (GP) that emulates multiple QoIs simultaneously is presented. As an exemplar, we consider Magnetized Liner Inertial Fusion, a fusion concept that relies on the direct compression of magnetized, laser-heated fuel by a metal liner to achieve thermonuclear ignition. Magneto-hydrodynamics (MHD) codes calculate diagnostics to infer the state of the fuel during experiments, which cannot be measured directly. The calibration of these diagnostic metrics is complicated by sparse experimental data and the expense of high-fidelity neutron transport models. The development of an appropriate surrogate raises long-standing issues in modeling and simulation, including calibration, validation, and uncertainty quantification. The performance of the proposed multi-output GP surrogate model, which preserves correlations between QoIs, is compared to the standard single-output GP for a 1D realization of the MagLIF experiment.

This work investigates the low- and high-temperature ignition and combustion processes, applied to the Engine Combustion Network Spray A flame, combining advanced optical diagnostics and large-eddy simulations (LES). Simultaneous high-speed (50 kHz) formaldehyde (CH2O) planar laser-induced fluorescence (PLIF) and line-of-sight OH* chemiluminescence imaging were used to measure the low- and high-temperature flame, during ignition as well as during quasi-steady combustion. While tracking the cool flame at the laser sheet plane, the present experimental setup allows detection of distinct ignition spots and dynamic fluctuations of the lift-off length over time, which overcomes limitations for flame tracking when using schlieren imaging [Sim et al.Proc. Combust. Inst. 38 (4) (2021) 5713–5721]. After significant development to improve LES prediction of the low-and high-temperature flame position, both during the ignition processes and quasi-steady combustion, the simulations were analyzed to gain understanding of the mixture variance and how this variance affects formation/consumption of CH2O. Analysis of the high-temperature ignition period shows that a key improvement in the LES is the ability to predict heterogeneous ignition sites, not only in the head of the jet, but in shear layers at the jet edge close to the position where flame lift-off eventually stabilizes. The LES analysis also shows concentrated pockets of CH2O, in the center of jet and at 20 mm downstream of the injector (in regions where the equivalence ratio is greater than 6), that are of similar length scale and frequency as the experiment (approximately 5–6 kHz). The periodic oscillation of CH2O match the frequency of pressure waves generated during auto-ignition and reflected within the constant-volume vessel throughout injection. The ability of LES to capture the periodic appearance and destruction of CH2O is particularly important because these structures travel downstream and become rich premixed flames that affect soot production.

A high altitude electromagnetic pulse (HEMP) or other similar geomagnetic disturbance (GMD) has the potential to severely impact the operation of large-scale electric power grids. By introducing low-frequency common-mode (CM) currents, these events can impact the performance of key system components such as large power transformers. In this work, a solid-state transformer (SST) that can replace susceptible equipment and improve grid resiliency by safely absorbing these CM insults is described. An overview of the proposed SST power electronics and controls architecture is provided, a system model is developed, and the performance of the SST in response to a simulated CM insult is evaluated. Compared to a conventional magnetic transformer, the SST is found to recover quickly from the insult while maintaining nominal ac input/output behavior.

Extreme meteorological events, such as hurricanes and floods, cause significant infrastructure damage and, as a result, prolonged grid outages. To mitigate the negative effect of these outages and enhance the resilience of communities, microgrids consisting of solar photovoltaics (PV), energy storage (ES) technologies, and backup diesel generation are being considered. Furthermore, it is necessary to take into account how the extreme event affects the systems' performance during the outage, often referred to as black-sky conditions. In this paper, an optimization model is introduced to properly size ES and PV technologies to meet various duration of grid outages for selected critical infrastructure while considering black-sky conditions. A case study of the municipality of Villalba, Puerto Rico is presented to identify the several potential microgrid configurations that increase the community's resilience. Sensitivity analyses are performed around the grid outage durations and black-sky conditions to better decide what factors should be considered when scoping potential microgrids for community resilience.

Experiments were conducted on a wave tank model of a bottom raised oscillating surge wave energy converter (OSWEC) model in regular waves. The OSWEC model shape was a thin rectangular flap, which was allowed to pitch in response to incident waves about a hinge located at the intersection of the flap and the top of the supporting foundation. Torsion springs were added to the hinge in order to position the pitch natural frequency at the center of the wave frequency range of the wave maker. The flap motion as well as the loads at the base of the foundation were measured. The OSWEC was modeled analytically using elliptic functions in order to obtain closed form expressions for added mass and radiation damping coefficients, along with the excitation force and torque. These formulations were derived and reported in a previous publication by the authors. While analytical predictions of the foundation loads agree very well with experiments, large discrepancies are seen in the pitch response close to resonance. These differences are analyzed by conducting a sensitivity study, in which system parameters, including damping and added mass values, are varied. The likely contributors to the differences between predictions and experiments are attributed to tank reflections, standing waves that can occur in long, narrow wave tanks, as well as the thin plate assumption employed in the analytical approach.

The current interest in hypersonic flows and the growing importance of plasma applications necessitate the development of diagnostics for high-enthalpy flow environments. Reliable and novel experimental data at relevant conditions will drive engineering and modeling efforts forward significantly. This study demonstrates the usage of nanosecond Coherent Anti-Stokes Raman Scattering (CARS) to measure temperature in an atmospheric, high-temperature (> 5500 K) air plasma. The experimental configuration is of interest as the plasma is close to thermodynamic equilibrium and the setup is a test-bed for heat shield materials. The determination of the non-resonant background at such high-temperatures is explored and rotational-vibrational equilibrium temperatures of the N2 ground state are determined via fits of the theory to measured spectra. Results show that the accuracy of the temperature measurements is affected by slow periodic variations in the plasma, causing sampling error. Moreover, depending on the experimental configuration, the measurements can be affected by two-beam interaction, which causes a bias towards lower temperatures, and stimulated Raman pumping, which causes a bias towards higher temperatures. The successful demonstration of CARS at the present conditions, and the exploration of its sensitivities, paves the way towards more complex measurements, e.g. close to interfaces in high-enthalpy plasma flows.