Publications

Inverse problems constrained by partial differential equations (PDEs) play a critical role in model development and calibration. In many applications, there are multiple uncertain parameters in a model that must be estimated. However, high dimensionality of the parameters and computational complexity of the PDE solves make such problems challenging. A common approach is to reduce the dimension by fixing some parameters (which we will call auxiliary parameters) to a best estimate and use techniques from PDE-constrained optimization to estimate the other parameters. In this article, hyper-differential sensitivity analysis (HDSA) is used to assess the sensitivity of the solution of the PDE-constrained optimization problem to changes in the auxiliary parameters. Foundational assumptions for HDSA require satisfaction of the optimality conditions which are not always practically feasible as a result of ill-posedness in the inverse problem. We introduce novel theoretical and computational approaches to justify and enable HDSA for ill-posed inverse problems by projecting the sensitivities on likelihood informed subspaces and defining a posteriori updates. Our proposed framework is demonstrated on a nonlinear multiphysics inverse problem motivated by estimation of spatially heterogeneous material properties in the presence of spatially distributed parametric modeling uncertainties.

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.

Surrogate construction is an essential component for all non-deterministic analyses in science and engineering. The efficient construction of easy and cheaper-to-run alternatives to a computationally expensive code paves the way for outer loop workflows for forward and inverse uncertainty quantification and optimization. Unfortunately, the accurate construction of a surrogate still remains a task that often requires a prohibitive number of computations, making the approach unattainable for large-scale and high-fidelity applications. Multifidelity approaches offer the possibility to lower the computational expense requirement on the highfidelity code by fusing data from additional sources. In this context, we have demonstrated that multifidelity Bayesian Networks (MFNets) can efficiently fuse information derived from models with an underlying complex dependency structure. In this contribution, we expand on our previous work by adopting a basis adaptation procedure for the selection of the linear model representing each data source. Our numerical results demonstrate that this procedure is computationally advantageous because it can maximize the use of limited data to learn and exploit the important structures shared among models. Two examples are considered to demonstrate the benefits of the proposed approach: an analytical problem and a nuclear fuel finite element assembly. From these two applications, a lower dependency of MFnets on the model graph has been also observed.

Accurate characterization of electrical device behavior is a key component of developing accurate electrical models and assessing reliability. Measurements characterizing an electrical device can be produced from current-voltage (I-V) sweeps. We introduce the pairwise midpoint method (PMM) for estimating the mean of a functional data set and apply it to I-V sweeps from a Zener diode. Comparisons indicate that the PMM is a viable method for describing the mean behavior of a functional data set.

We demonstrate coherent anti-Stokes Raman scattering (CARS) detection of the CO and N2 molecules in the reaction layer of a graphite material sample exposed to the 5000-6000 K plume of an inductively-coupled plasma torch operating on air. CO is a dominant product in the surface oxidative reaction of graphite and lighter weight carbon-based thermalprotection-system materials. A standard nanosecond CARS approach using Nd:YAG and a single broadband dye laser with ~200 cm-1 spectral width is employed for demonstration measurements, with the CARS volume located less than 1-mm from an ablating graphite sample. Quantitative measurements of both temperature and the CO/N2 ratio are obtained from model fits to CARS spectra that have been averaged for 5 laser shots. The results indicate that CARS can be used for space- and time-resolved detection of CO in high-temperature ablation tests near atmospheric pressure.

The Information Harm Triangle (IHT) is an approach that seeks to simplify the defense-in-depth design of digital instrumentation and control (I&C) systems. The IHT provides a novel framework for understanding how cyber-attacks targeting digital I&C systems can harm the physical process. The utility of the IHT arises from the decomposition of cybersecurity analysis into two orthogonal vectors: data harm and physical information harm. Cyber-attacks on I&C systems can only directly cause data harm. Data harm is then transformed into physical information harm by unsafe control actions (UCAs) identified using Systems-Theoretic Process Analysis (STPA). Because data harm and physical information harm are orthogonal, defense-in-depth can be achieved by identifying control measures that independently limit data harm and physical information harm. This paper furthers the development of the IHT by investigating the defense-in-depth design of cybersecurity measures for sequences of UCAs. The effects of the order and timing of UCAs are examined for several case studies to determine how to represent these sequences using the IHT. These considerations are important for the identification of data harm and physical information harm security measures, and they influence the selection of efficient measures to achieve defense-in-depth. This research enables the benefits of the IHT's simple approach to be realized for increasingly complex cyber-attack scenarios.

The National Aeronautics and Space Administration’s (NASA) Artemis program seeks to establish the first long-term presence on the Moon as part of a larger goal of sending the first astronauts to Mars. To accomplish this, the Artemis program is designed to develop, test, and demonstrate many technologies needed for deep space exploration and supporting life on another planet. Long-term operations on the lunar base include habitation, science, logistics, and in-situ resource utilization (ISRU). In this paper, a Lunar DC microgrid (LDCMG) structure is the backbone of the energy distribution, storage, and utilization infrastructure. The method to analyze the LDCMG power distribution network and ESS design is the Hamiltonian surface shaping and power flow control (HSSPFC). This ISRU system will include a networked three-microgrid system which includes a Photo-voltaic (PV) array (generation) on one sub-microgrid and water extraction (loads) on the other two microgrids. A system's reduced-order model (ROM) will be used to create a closed-form analytical model. Ideal ESS devices will be placed alongside each state of the ROM. The ideal ESS devices determine the response needed to conform to a specific operating scenario and system specifications.

Reactive classical molecular dynamics simulations of sodium silicate glasses, xNa2O–(100 − x)SiO2 (x = 10–30), under quasi-static loading, were performed for the analysis of molecular scale fracture mechanisms. Mechanical properties of the sodium silicate glasses were consistent with experimentally reported values, and the amount of crack propagation varied with reported fracture toughness values. The most crack propagation occurred in NS20 systems (20-mol% Na2O) compared with the other simulated compositions. Dissipation via two mechanisms, the first through sodium migration as a lower activation energy process and the second through structural rearrangement as a higher activation energy process, was calculated and accounted for the energy that was not stored elastically or associated with the formation of new fracture surfaces. A correlation between crack propagation and energy dissipation was identified, with systems with higher crack propagation exhibiting less energy dissipation. Sodium silicate glass compositions with lower energy dissipation also exhibited the most sodium movement and structural rearrangement within 10 Å of the crack tip during loading. Therefore, high sodium mobility near the crack tip may enable energy dissipation without requiring formation of structural defects. Therefore, the varying mobilities of the network modifiers near crack tips influence the brittleness and the crack growth rate of modified amorphous oxide systems.

Computational engineering models often contain unknown entities (e.g. parameters, initial and boundary conditions) that require estimation from other measured observable data. Estimating such unknown entities is challenging when they involve spatio-temporal fields because such functional variables often require an infinite-dimensional representation. We address this problem by transforming an unknown functional field using Alpert wavelet bases and truncating the resulting spectrum. Hence the problem reduces to the estimation of few coefficients that can be performed using common optimization methods. We apply this method on a one-dimensional heat transfer problem where we estimate the heat source field varying in both time and space. The observable data is comprised of temperature measured at several thermocouples in the domain. This latter is composed of either copper or stainless steel. The optimization using our method based on wavelets is able to estimate the heat source with an error between 5% and 7%. We analyze the effect of the domain material and number of thermocouples as well as the sensitivity to the initial guess of the heat source. Finally, we estimate the unknown heat source using a different approach based on deep learning techniques where we consider the input and output of a multi-layer perceptron in wavelet form. We find that this deep learning approach is more accurate than the optimization approach with errors below 4%.

Transient stability is often the limiting factor in power transmission. Due to lack of inertia, high penetration of inverter-based generation may exacerbate such instabilities. Emerging energy storage systems (ESSs) present a unique opportunity for addressing transient stability via real-power modulation. Such control could help avoid instabilities or the need for remedial actions such as load shedding. Some promising ESS transient-stability control strategies proposed in the literature utilize real-time wide-area generator speeds as the fundamental feedback signals. A potential proxy for the generator speed is electrical frequency obtained from a phasor-measurement unit (PMU) incorporated into a wide-area measurement system. This paper examines the impact of using PMU-based frequency measurements for such applications.

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.

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.

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 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.

Two-dimensional (2D) layered oxides have recently attracted wide attention owing to the strong coupling among charges, spins, lattice, and strain, which allows great flexibility and opportunities in structure designs as well as multifunctionality exploration. In parallel, plasmonic hybrid nanostructures exhibit exotic localized surface plasmon resonance (LSPR) providing a broad range of applications in nanophotonic devices and sensors. A hybrid material platform combining the unique multifunctional 2D layered oxides and plasmonic nanostructures brings optical tuning into the new level. In this work, a novel self-assembled Bi2MoO6 (BMO) 2D layered oxide incorporated with plasmonic Au nanoinclusions has been demonstrated via one-step pulsed laser deposition (PLD) technique. Comprehensive microstructural characterizations, including scanning transmission electron microscopy (STEM), differential phase contrast imaging (DPC), and STEM tomography, have demonstrated the high epitaxial quality and particle-in-matrix morphology of the BMO-Au nanocomposite film. DPC-STEM imaging clarifies the magnetic domain structures of BMO matrix. Three different BMO structures including layered supercell (LSC) and superlattices have been revealed which is attributed to the variable strain states throughout the BMO-Au film. Owing to the combination of plasmonic Au and layered structure of BMO, the nanocomposite film exhibits a typical LSPR in visible wavelength region and strong anisotropy in terms of its optical and ferromagnetic properties. This study opens a new avenue for developing novel 2D layered complex oxides incorporated with plasmonic metal or semiconductor phases showing great potential for applications in multifunctional nanoelectronics devices. [Figure not available: see fulltext.]

Physics-Based Reduced Order Models (ROMs) tend to rely on projection-based reduction. This family of approaches utilizes a series of responses of the full-order model to assemble a suitable basis, subsequently employed to formulate a set of equivalent, low-order equations through projection. However, in a nonlinear setting, physics-based ROMs require an additional approximation to circumvent the bottleneck of projecting and evaluating the nonlinear contributions on the reduced space. This scheme is termed hyper-reduction and enables substantial computational time reduction. The aforementioned hyper-reduction scheme implies a trade-off, relying on a necessary sacrifice on the accuracy of the nonlinear terms’ mapping to achieve rapid or even real-time evaluations of the ROM framework. Since time is essential, especially for digital twins representations in structural health monitoring applications, the hyper-reduction approximation serves as both a blessing and a curse. Our work scrutinizes the possibility of exploiting machine learning (ML) tools in place of hyper-reduction to derive more accurate surrogates of the nonlinear mapping. By retaining the POD-based reduction and introducing the machine learning-boosted surrogate(s) directly on the reduced coordinates, we aim to substitute the projection and update process of the nonlinear terms when integrating forward in time on the low-order dimension. Our approach explores a proof-of-concept case study based on a Nonlinear Auto-regressive neural network with eXogenous Inputs (NARX-NN), trying to potentially derive a superior physics-based ROM in terms of efficiency, suitable for (near) real-time evaluations. The proposed ML-boosted ROM (N3-pROM) is validated in a multi-degree of freedom shear frame under ground motion excitation featuring hysteretic nonlinearities.

Quantum computing testbeds exhibit high-fidelity quantum control over small collections of qubits, enabling performance of precise, repeatable operations followed by measurements. Currently, these noisy intermediate-scale devices can support a sufficient number of sequential operations prior to decoherence such that near term algorithms can be performed with proximate accuracy (like chemical accuracy for quantum chemistry problems). While the results of these algorithms are imperfect, these imperfections can help bootstrap quantum computer testbed development. Demonstrations of these algorithms over the past few years, coupled with the idea that imperfect algorithm performance can be caused by several dominant noise sources in the quantum processor, which can be measured and calibrated during algorithm execution or in post-processing, has led to the use of noise mitigation to improve typical computational results. Conversely, benchmark algorithms coupled with noise mitigation can help diagnose the nature of the noise, whether systematic or purely random. Here, we outline the use of coherent noise mitigation techniques as a characterization tool in trapped-ion testbeds. We perform model-fitting of the noisy data to determine the noise source based on realistic physics focused noise models and demonstrate that systematic noise amplification coupled with error mitigation schemes provides useful data for noise model deduction. Further, in order to connect lower level noise model details with application specific performance of near term algorithms, we experimentally construct the loss landscape of a variational algorithm under various injected noise sources coupled with error mitigation techniques. This type of connection enables application-aware hardware code-sign, in which the most important noise sources in specific applications, like quantum chemistry, become foci of improvement in subsequent hardware generations.

The challenge of cyberattack detection can be illustrated by the complexity of the MITRE ATT&CKTM matrix, which catalogues >200 attack techniques (most with multiple sub-techniques). To reliably detect cyberattacks, we propose an evidence-based approach which fuses multiple cyber events over varying time periods to help differentiate normal from malicious behavior. We use Bayesian Networks (BNs) - probabilistic graphical models consisting of a set of variables and their conditional dependencies - for fusion/classification due to their interpretable nature, ability to tolerate sparse or imbalanced data, and resistance to overfitting. Our technique utilizes a small collection of expert-informed cyber intrusion indicators to create a hybrid detection system that combines data-driven training with expert knowledge to form a host-based intrusion detection system (HIDS). We demonstrate a software pipeline for efficiently generating and evaluating various BN classifier architectures for specific datasets and discuss explainability benefits thereof.

High penetrations of residential solar PV can cause voltage issues on low-voltage (LV) secondary networks. Distribution utility planners often utilize model-based power flow solvers to address these voltage issues and accommodate more PV installations without disrupting the customers already connected to the system. These model-based results are computationally expensive and often prone to errors. In this paper, two novel deep learning-based model-free algorithms are proposed that can predict the change in voltages for PV installations without any inherent network information of the system. These algorithms will only use the real power (P), reactive power (Q), and voltage (V) data from Advanced Metering Infrastructure (AMI) to calculate the change in voltages for an additional PV installation for any customer location in the LV secondary network. Both algorithms are tested on three datasets of two feeders and compared to the conventional model-based methods and existing model-free methods. The proposed methods are also applied to estimate the locational PV hosting capacity for both feeders and have shown better accuracies compared to an existing model-free method. Results show that data filtering or pre-processing can improve the model performance if the testing data point exists in the training dataset used for that model.

Aperture near-field microscopy and spectroscopy (a-SNOM) enables the direct experimental investigation of subwavelength-sized resonators by sampling highly confined local evanescent fields on the sample surface. Despite its success, the versatility and applicability of a-SNOM is limited by the sensitivity of the aperture probe, as well as the power and versatility of THz sources used to excite samples. Recently, perfectly absorbing photoconductive metasurfaces have been integrated into THz photoconductive antenna detectors, enhancing their efficiency and enabling high signal-to-noise ratio THz detection at significantly reduced optical pump powers. Here, we discuss how this technology can be applied to aperture near-field probes to improve both the sensitivity and potentially spatial resolution of a-SNOM systems. In addition, we explore the application of photoconductive metasurfaces also as near-field THz sources, providing the possibility of tailoring the beam profile, polarity and phase of THz excitation. Photoconductive metasurfaces therefore have the potential to broaden the application scope of aperture near-field microscopy to samples and material systems which currently require improved spatial resolution, signal-to-noise ratio, or more complex excitation conditions.

Well-skipping radical-radical reactions can provide a chain-propagating pathway for formation of polycyclic radicals implicated in soot inception. Here we use controlled pyrolysis in a microreactor to isolate and examine the role of well-skipping channels in the phenyl (C6H5) + propargyl (C3H3) radical-radical reaction at temperatures of 800–1600 K and pressures near 25 Torr. The temperature and concentration dependence of the closed-shell (C9H8) and radical (C9H7) products are observed using electron-ionization mass spectrometry. The flow in the reactor is simulated using a boundary layer model employing a chemical mechanism based on recent rate coefficient calculations. Comparison between simulation and experiment shows reasonable agreement, within a factor of 3, while suggesting possible improvements to the model. In contrast, eliminating the well-skipping reactions from the chemistry mechanism causes a much larger discrepancy between simulation and experiment in the temperature dependence of the radical concentration, revealing that the well-skipping pathways, especially to form indenyl radical, are significant at temperatures of 1200 K and higher. While most C9H7 forms by well-skipping at 25 Torr, an additional simulation indicates that the well-skipping channels only contribute around 3% of the C9Hx yield at atmospheric pressure, thus indicating a negligible role of the well-skipping pathways at atmospheric and higher pressures.

The near wake flow field associated with hypersonic blunt bodies is characterized by complex physical phenomena resulting in both steady and time dependent pressure loadings on the base of the vehicle. Here, we focus on the unsteady fluid dynamic pressure fluctuation behavior as a vibratory input loading. Typically, these flows are characterized by a locally low-pressure, separated flow region with an unsteady formation of vortical cells that are locally produced and convected downstream into the far-field wake. This periodic production and transport of vortical elements is very-well known from classical incompressible fluid mechanics and is usually termed as the (Von) Karman vortex street. While traditionally discussed within the scope of incompressible flow, the periodic vortex shedding phenomenon is known for compressible flows as well. To support vehicle vibratory loading design computations, we examine a suite of analytical and high-fidelity computational models supported by dedicated experimental measurements. While large scale simulation approaches offer very high-quality results, they are impractical for design-level decisions, implying that analytically derived reduced order models are essential. The major portions of this effort include an examination of the DeChant-Smith Power Spectral Density (PSD) [1] model to better understand both overall Root Mean Square (RMS) magnitude and functional maximum associated with a critical vortex shedding phenomenon. The critical frequency is examined using computational, experiments and an analytical shear layer frequency model. Finally, the PSD magnitude maximum is studied using a theory-based approach connecting the PSD to the spatial correlation that strongly supports the DeChant-Smith PSD model behavior. These results combine to demonstrate that the current employed PSD models provide plausible reduced order closures for turbulent base pressure fluctuations for high Reynolds number flows over range of Mach numbers. Access to a reliable base pressure fluctuation model then permits simulation of bluff body vibratory input.

This paper presents the uncertainty propagation of turbulent coefficients for the Spalart– Allmaras (SA) turbulence model using projection-based reduced-order models (ROMs). ROMs are used instead of Reynolds-averaged Navier–Stokes (RANS) solvers and stochastic collocation/ Galerkin and Monte Carlo methods because they are computationally inexpensive and tend to offer more accuracy than a polynomial surrogate. The uncertainty propagation is performed on two benchmark RANS cases documented on NASA’s turbulence modeling resource. Uncertainty propagation of the SA turbulent coefficients using a ROMis shown to compare well against uncertainty propagation performed using only RANS and using a Gaussian process regression (GP) model. The ROM is shown to be more robust to the size and spread of the training data compared to a GP model.

Residential solar photovoltaic (PV) systems are interconnected with the distribution grid at low-voltage secondary network locations. However, computational models of these networks are often over-simplified or non-existent, which makes it challenging to determine the operational impacts of new PV installations at those locations. In this work, a model-free locational hosting capacity analysis algorithm is proposed that requires only smart meter measurements at a given location to calculate the maximum PV size that can be accommodated without exceeding voltage constraints. The proposed algorithm was evaluated on two different smart meter datasets measuring over 2,700 total customer locations and was compared against results obtained from conventional model-based methods for the same smart meter datasets. Compared to the model-based results, the model-free algorithm had a mean absolute error (MAE) of less than 0.30 kW, was equally sensitive to measurement noise, and required much less computation time.

Laser absorption spectroscopy (LAS) was used to measure temperature and XH2O at a rate of 500 kHz in post-detonation fireballs of solid explosives. A 25 g hemisphere of pentaerythritol tetranitrate (PETN) was initiated with an exploding-bridgewire detonator to produce a post-detonation fireball that traveled radially toward a hardened optical probe. The probe contained a pressure transducer and the near-infrared optics needed to measure H2O absorption transitions near 7185.6 cm-1 and 6806 cm-1 using peak-picking scanned-wavelength modulation-spectroscopy with first-harmonic-normalized second-harmonic detection (scanned-WMS-2f/1f). The two lasers were scanned across the peak of an absorption line at 500 kHz and modulated at either 35 MHz for the laser near 7185.6 cm-1 or 45.5 MHz for the laser near 6806 cm-1. This enabled measurements of temperature and XH2O at 500 kHz in the shock-heated air and trailing post-detonation fireball. Time histories of pressure, temperature, and XH2O were acquired at multiple standoff distances in order to quantify the temporal evolution of these quantities in the post-detonation environment produced by PETN.

This study investigated the durability of four high temperature coatings for use as a Gardon gauge foil coating. Failure modes and effects analysis have identified Gardon gauge foil coating as a critical component for the development of a robust flux gauge for high intensity flux measurements. Degradation of coating optical properties and physical condition alters flux gauge sensitivity, resulting in flux measurement errors. In this paper, four coatings were exposed to solar and thermal cycles to simulate real-world aging. Solar simulator and box furnace facilities at the National Solar Thermal Test Facility (NSTTF) were utilized in separate test campaigns. Coating absorptance and emissivity properties were measured and combined into a figure of merit (FOM) to characterize the optical property stability of each coating, and physical coating degradation was assessed qualitatively using microscope images. Results suggest rapid high temperature cycling did not significantly impact coating optical properties and physical state. In contrast, prolonged exposure of coatings to high temperatures degraded coating optical properties and physical state. Coatings degraded after 1 hour of exposure at temperatures above 400 °C and stabilized after 6-24 hours of exposure. It is concluded that the combination of high temperatures and prolonged exposure provide the energy necessary to sustain coating surface reactions and alter optical and physical coating properties. Results also suggest flux gauge foil coatings could benefit from long duration high temperature curing (>400 °C) prior to sensor calibration to stabilize coating properties and increase measurement reliability in high flux and high temperature applications.

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.

The research investigates novel techniques to enhance supply chain security via addition of configuration management controls to protect Instrumentation and Control (I&C) systems of a Nuclear Power Plant (NPP). A secure element (SE) is integrated into a proof-of-concept testbed by means of a commercially available smart card, which provides tamper resistant key storage and a cryptographic coprocessor. The secure element simplifies setup and establishment of a secure communications channel between the configuration manager and verification system and the I&C system (running OpenPLC). This secure channel can be used to provide copies of commands and configuration changes of the I&C system for analysis.

In recent years, high-altitude infrasound sensing has become more prolific, demonstrating an enormous value especially when utilized over regions inaccessible to traditional ground-based sensing. Similar to ground-based infrasound detectors, airborne sensors take advantage of the fact that impulsive atmospheric events such as explosions can generate low frequency acoustic waves, also known as infrasound. Due to negligible attenuation, infrasonic waves can travel over long distances, and provide important clues about their source. Here, we report infrasound detections of the Apollo detonation that was carried on 29 October 2020 as part of the Large Surface Explosion Coupling Experiment in Nevada, USA. Infrasound sensors attached to solar hot air balloons floating in the stratosphere detected the signals generated by the explosion at distances 170–210 km. Three distinct arrival phases seen in the signals are indicative of multipathing caused by the small-scale perturbations in the atmosphere. We also found that the local acoustic environment at these altitudes is more complex than previously thought.

Due to their increased levels of reliability, meshed low-voltage (LV) grid and spot networks are common topologies for supplying power to dense urban areas and critical customers. Protection schemes for LV networks often use highly sensitive reverse current trip settings to detect faults in the medium-voltage system. As a result, interconnecting even low levels of distributed energy resources (DERs) can impact the reliability of the protection system and cause nuisance tripping. This work analyzes the possibility of modifying the reverse current relay trip settings to increase the DER hosting capacity of LV networks without impacting fault detection performance. The results suggest that adjusting relay settings can significantly increase DER hosting capacity on LV networks without adverse effects, and that existing guidance on connecting DERs to secondary networks, such as that contained in IEEE Std 1547-2018, could potentially be modified to allow higher DER deployment levels.

Data processing adds substantial soft costs to distributed energy systems. These costs are incurred primarily as labor necessary to collect, normalize, store and communicate data. The open-source Orange Button data exchange standard comprises data taxonomies, common data sources, and interoperable software tools which together can dramatically reduce these costs and thereby accelerate the deployment of distributed energy systems. We describe the data taxonomies and datasets, and the software enabled by these capabilities.

There is a global interest in decarbonizing the existing natural gas infrastructure by blending the natural gas with hydrogen. However, hydrogen is known to embrittle pipeline and pressure vessel steels used in gas transportation and storage applications. Thus, assessing the structural integrity of vintage pipeline (pre-1970s) in the presence of gaseous hydrogen is a critical step towards successful implementation of hydrogen blending into existing infrastructure. To this end, fatigue crack growth (FCG) behavior and fracture resistance of several vintage X52 pipeline steels were evaluated in high purity gaseous hydrogen environments at pressure of 210 bar (3,000 psi) and 34 bar (500 psi). The base metal and seam weld microstructures were characterized using optical microscopy, scanning electron microscopy (SEM) and Vickers hardness mapping. The base metals consisted of ferrite-pearlite banded microstructures, whereas the weld regions contained ferrite and martensite. In one case, a hook-like crack was observed in an electric resistance (seam) weld; whereas hard spots were observed near the bond line of a double-submerged arc (seam) weld. For a given hydrogen gas pressure, comparable FCG rates were observed for the different base metal and weld microstructures. Generally, the higher strength microstructures had lower fracture resistance in hydrogen. In particular, lower fracture resistance was measured when local hard spots were observed in the approximate region of the crack plane of the weld. Samples tested in lower H2 pressure (34 bar) exhibited lower FCG rates (in the lower ∆K regime) and greater fracture resistance when compared to the respective high-pressure (210 bar) hydrogen tests. The hydrogen-assisted fatigue and fracture surfaces were qualitatively characterized using SEM to rationalize the influence of microstructure on the dominant fracture mechanisms in gaseous hydrogen environment.

Snow is a significant challenge for PV plants at northern latitudes, and snow-related power losses can exceed 30 % of annual production. Accurate loss estimates are needed for resource planning and to validate mitigation strategies, but this requires accurate snow detection at the inverter level. In this study, we propose and validate a framework for detecting snow in time-series inverter data. We identify four distinct snow-related power loss modes based on the inverter's operating points and electrical properties of the inverter and PV arrays. We validate these modes and identify their associated physical snow conditions using site images. Finally we examine relative frequencies of the snow power loss modes and their contributions to total power loss.

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.

With the recent surge in big data analytics for hyperdimensional data, there is a renewed interest in dimensionality reduction techniques. In order for these methods to improve performance gains and understanding of the underlying data, a proper metric needs to be identified. This step is often overlooked, and metrics are typically chosen without consideration of the underlying geometry of the data. In this paper, we present a method for incorporating elastic metrics into the t-distributed stochastic neighbour embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). We apply our method to functional data, which is uniquely characterized by rotations, parameterization and scale. If these properties are ignored, they can lead to incorrect analysis and poor classification performance. Through our method, we demonstrate improved performance on shape identification tasks for three benchmark data sets (MPEG-7, Car data set and Plane data set of Thankoor), where we achieve 0.77, 0.95 and 1.00 F1 score, respectively.

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.

This is an investigation on two experimental datasets of laminar hypersonic flows, over a double-cone geometry, acquired in Calspan—University at Buffalo Research Center’s Large Energy National Shock (LENS)-XX expansion tunnel. These datasets have yet to be modeled accurately. A previous paper suggested that this could partly be due to mis-specified inlet conditions. The authors of this paper solved a Bayesian inverse problem to infer the inlet conditions of the LENS-XX test section and found that in one case they lay outside the uncertainty bounds specified in the experimental dataset. However, the inference was performed using approximate surrogate models. In this paper, the experimental datasets are revisited and inversions for the tunnel test-section inlet conditions are performed with a Navier–Stokes simulator. The inversion is deterministic and can provide uncertainty bounds on the inlet conditions under a Gaussian assumption. It was found that deterministic inversion yields inlet conditions that do not agree with what was stated in the experiments. An a posteriori method is also presented to check the validity of the Gaussian assumption for the posterior distribution. This paper contributes to ongoing work on the assessment of datasets from challenging experiments conducted in extreme environments, where the experimental apparatus is pushed to the margins of its design and performance envelopes.

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.

Thermal spray processes benefit from workpiece cooling to prevent overheating of the substrate and to retain metallurgical properties (e.g., temper). Cold-gas “plume quenching” is a plume-targeting cooling technique, where an argon curtain is directed laterally above the substrate surface to re-direct high temperature gases without impacting particle motion. However, there has been little investigation of its effect on the molten particles and the resulting coating properties. This study examined high- and medium- density tantalum and nickel coatings, fabricated by Controlled Atmosphere Plasma Spray with and without plume quenching on aluminum and titanium substrates. To compare the effect of plume quenching, the deposition efficiency was calculated through coating mass gain, and the coating density, stiffness, and adhesion were measured. The tantalum and nickel coatings were largely unaffected by plume quenching with respect to deposition efficiencies, coating density, adhesion, and stiffness. These results indicate that a plume quench could be used without affecting the coating properties for high- and medium-density metals while providing the benefit of substrate cooling that increases with higher plume quench gas flow rates.

Advancements in photonic quantum information systems (QIS) have driven the development of high-brightness, on-demand, and indistinguishable semiconductor epitaxial quantum dots (QDs) as single photon sources. Strain-free, monodisperse, and spatially sparse local-droplet-etched (LDE) QDs have recently been demonstrated as a superior alternative to traditional Stranski-Krastanov QDs. However, integration of LDE QDs into nanophotonic architectures with the ability to scale to many interacting QDs is yet to be demonstrated. We present a potential solution by embedding isolated LDE GaAs QDs within an Al0.4Ga0.6As Huygens’ metasurface with spectrally overlapping fundamental electric and magnetic dipolar resonances. We demonstrate for the first time a position- and size-independent, 1 order of magnitude increase in the collection efficiency and emission lifetime control for single-photon emission from LDE QDs embedded within the Huygens’ metasurfaces. Our results represent a significant step toward leveraging the advantages of LDE QDs within nanophotonic architectures to meet the scalability demands of photonic QIS.

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.

Sandia National Laboratories has conducted geomechanical analysis to evaluate the performance of the Strategic Petroleum Reserve by modeling the viscoplastic, or creep, behavior of the salt in which their oil-storage caverns reside. The operation-driven imbalance between fluid pressure within the salt cavern and in-situ stress acting on the surrounding salt can cause the salt to creep, potentially leading to a loss of the cavern volume and consequently deformation of borehole casings. Therefore, a greater understanding of salt creep's behavior on borehole casing needs to be addressed to drive cavern operations decisions. To evaluate potential casing damage mechanisms with variation in geological constraints (e.g. material characteristics of salt or caprock) or physical mechanisms of cavern leakage, we developed a generic model with a layered and domal geometry including nine caverns, rather than use a specific field-site model, to save computational costs. The geomechanical outputs, such as cavern volume changes, vertical strain along the dome and caprock above the cavern and vertical displacement at the surface or cavern top, quantifies the impact of material parameters and cavern locations as well as multiple operations in multiple caverns on an individual cavern stability.

Vulcan is a new pulsed power system at Sandia National Laboratories based on fast Marx technology. Vulcan will serve as an intermediate scale demonstration of a fast Marx system and as a testbed for vacuum insulator testing. Vulcan uses multiple parallel fast Marxes, in a layout we call a Fast Marx Array (FMA), and a pulse forming line (PFL) to generate pulses up to 5 MV with effective pulse lengths for vacuum insulator testing that are relevant to larger facilities like Z. Vulcan consists of two parallel 25 stage Marxes with a total stored energy of up to 20 kJ. Vulcan applies up to 5 MV to a vacuum insulator stack load, thereby enabling testing of large area insulator stacks with areas on the order of 1000 cm2. The PFL design includes an oil output switch to adjust the voltage stress duration applied to the vacuum insulator. We will discuss Vulcan's design, including the FMA, Marx trigger generator, energy diverter, PFL, oil output switch, and results of initial commissioning experiments.

A wind tunnel test from AEDC Tunnel 9 of a hypersonic turbulent boundary layer is analyzed using several fidelities of numerical simulation including Wall-Modeled Large Eddy Simulation (WMLES), Large Eddy Simulation (LES), and Direct Numerical Simulation (DNS). The DNS was forced to transition to turbulence using a broad spectrum of planar, slow acoustic waves based on the freestream spectrum measured in the tunnel. Results show the flow transitions in a reasonably natural process developing into turbulent flow. This is due to several 2nd mode wave packets advecting downstream and eventually breaking down into turbulence with modest friction Reynolds numbers. The surface shear stress and heat flux agree well with a transitional RANS simulation. Comparisons of DNS data to experimental data showreasonable agreement with regard to mean surface quantities aswell as amplitudes of boundary layer disturbances. The DNS does show early transition relative to the experimental data. Several interesting aspects of the DNS and other numerical simulations are discussed. The DNS data are also analyzed through several common methods such as cross-correlations and coherence of the fluctuating surface pressure.

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.

In this work, we introduce and compare the results of several methods for determining the horizon profile at a PV site, and compare their use cases and limitations. The methods in this paper include horizon detection from time-series irradiance or performance data, modeling from GIS topology data, manual theodolite measurements, and camera-based horizon detection. We compare various combinations of these methods using data from 4 Regional Test Center sites in the US, and 3 World Bank sites in Nepal. The results show many differences between these methods, and we recommend the most practical solutions for various use-cases.

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.

With increasing penetration of variable renewable generation, battery energy storage systems (BESS) are becoming important for power system stability due to their operational flexibility. In this paper, we propose a method for determining the minimum BESS rated power that guarantees security constraints in a grid subject to disturbances induced by variable renewable generation. The proposed framework leverages sensitivity-based inverse uncertainty propagation where the dynamical responses of the states are parameterized with respect to random variables. Using this approach, the original nonlinear optimization problem for finding the security-constrained uncertainty interval may be formulated as a quadratically-constrained linear program. The resulting estimated uncertainty interval is utilized to find the BESS rated power required to satisfy grid stability constraints.

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.

Low-inertia microgrids experience significant frequency deviations compared to bulk-power systems. In such microgrids, energy storage systems (ESSs) can be a viable option to provide fast-frequency support to keep frequency variations within allowable bounds. A model predictive control (MPC)-based strategy is one of the efficient control strategies to enable fast-frequency support through ESSs. MPC provides the capability to explicitly incorporate physical constraints of the microgrid and the ESS into the control formulation while allowing signifi-cant operational flexibility. MPC allows near-optimal control by optimizing the system over a rolling horizon based on a predictive model of the system. However, the effectiveness of MPC relies on the accuracy of this predictive model. This paper proposes a data-driven system identification (SI) based approach to obtain an accurate yet computationally tractable predictive model for frequency support in microgrids. The proposed data-driven MPC is compared with the conventional MPC that utilizes a simplified transfer-function-based predictive model of the system. Results show that the data-driven MPC offers a better quality of service in terms of lower frequency deviations and rate-of-change of frequency (ROCOF).

Characterization of geologic heterogeneity at an enhanced geothermal system (EGS) is crucial for cost-effective stimulation planning and reliable heat production. With recent advances in computational power and sensor technology, large-scale fine-resolution simulations of coupled thermal-hydraulic-mechanical (THM) processes have been available. However, traditional large-scale inversion approaches have limited utility for sites with complex subsurface structures unless one can afford high, often computationally prohibitive, computations. Key computational burdens are predominantly associated with a number of large-scale coupled numerical simulations and large dense matrix multiplications derived from fine discretization of the field site domain and a large number of THM and chemical (THMC) measurements. In this work, we present deep-generative model-based Bayesian inversion methods for the computationally efficient and accurate characterization of EGS sites. Deep generative models are used to learn the approximate subsurface property (e.g., permeability, thermal conductivity, and elastic rock properties) distribution from multipoint geostatistics-derived training images or discrete fracture network models as a prior and accelerated stochastic inversion is performed on the low-dimensional latent space in a Bayesian framework. Numerical examples with synthetic permeability fields with fracture inclusions with THM data sets based on Utah FORGE geothermal site will be presented to test the accuracy, speed, and uncertainty quantification capability of our proposed joint data inversion method.

Low loss silicon nitride ring resonator reflectors provide feedback to a III/V gain chip, achieving single-mode lasing at 772nm. The Si3N4 is fabricated in a CMOS foundry compatible process that achieves loss values of 0.036dB/cm.

Computational simulations of high-speed flow play an important role in the design of hypersonic vehicles, for which experimental data are scarce; however, high-fidelity simulations of hypersonic flow are computationally expensive. Reduced order models (ROMs) have the potential to make many-query problems, such as design optimization and uncertainty quantification, tractable for this domain. Residual minimization-based ROMs, which formulate the projection onto a reduced basis as an optimization problem, are one promising candidate for model reduction of large-scale fluid problems. This work analyzes whether specific choices of norms and objective functions can improve the performance of ROMs of hypersonic flow. Specifically, we investigate the use of dimensionally consistent inner products and modifications designed for convective problems, including ℓ1 minimization and constrained optimization statements to enforce conservation laws. Particular attention is paid to accuracy for problems with strong shocks, which are common in hypersonic flow and challenging for projection-based ROMs. We demonstrate that these modifications can improve the predictability and efficiency of a ROM, though the impact of such formulations depends on the quantity of interest and problem considered.

Motion primitives (MPs) provide a fundamental abstraction of movement templates that can be used to guide and navigate a complex environment while simplifying the movement actions. These MPs, when utilized as an action space in reinforcement learning (RL), can allow an agent to learn to select a sequence of simple actions to guide a vehicle towards desired complex mission outcomes. This is particularly useful for missions involving high speed aerospace vehicles (HSAVs) (i.e., Mach 1 to 30) where near real time trajectory generation is needed but the computational cost and timeliness of trajectory generation remains prohibitive. This paperdemonstrates that when MPs are employed in conjunction with RL, the agent can learn to solve a wider range of problems for HSAV missions. To this end, using both a MP and and non-MP approach, RL is employed to solve the problem of an HSAV arriving at a non-maneuvering moving target at a constant altitude and with an arbitrary, but constant, velocity and heading angle. The MPs for HSAV consist of multiple pull (flight path angle) and turn (heading angle) commands that are defined for a specific duration based on mission phases; whereas the non-MP approach uses angle of attack and bank angle as action space for RL. The paper describes details on HSAV problem formulation to include equations of motion, observation space, telescopic reward function, RL algorithm and hyperparameters, RL curriculum, formation of the MPs, and calculation of time to execute the MP used for the problem. Our results demonstrate that the non-MP approach is unable to even train an agent that is successful in the base-case of the RL curriculum. The MP approach, however, can train an agent with success rate of 76.6% inarriving at a target moving with any heading angle with a velocity between 0 and 500 m/s.

Due to their increased levels of reliability, meshed low-voltage (LV) grid and spot networks are common topologies for supplying power to dense urban areas and critical customers. Protection schemes for LV networks often use highly sensitive reverse current trip settings to detect faults in the medium-voltage system. As a result, interconnecting even low levels of distributed energy resources (DERs) can impact the reliability of the protection system and cause nuisance tripping. This work analyzes the possibility of modifying the reverse current relay trip settings to increase the DER hosting capacity of LV networks without impacting fault detection performance. The results suggest that adjusting relay settings can significantly increase DER hosting capacity on LV networks without adverse effects, and that existing guidance on connecting DERs to secondary networks, such as that contained in IEEE Std 1547-2018, could potentially be modified to allow higher DER deployment levels.

Polymers are widely used as damping materials in vibration and impact applications. Liquid crystal elastomers (LCEs) are a unique class of polymers that may offer the potential for enhanced energy absorption capacity under impact conditions over conventional polymers due to their ability to align the nematic phase during loading. Being a relatively new material, the high rate compressive properties of LCEs have been minimally studied. Here, we investigated the high strain rate compression behavior of different solid LCEs, including cast polydomain and 3D-printed, preferentially oriented monodomain samples. Direct ink write (DIW) 3D printed samples allow unique sample designs, namely, a specific orientation of mesogens with respect to the loading direction. Loading the sample in different orientations can induce mesogen rotation during mechanical loading and subsequently different stress-strain responses under impact. We also used a reference polymer, bisphenol-A (BPA) cross-linked resin, to contrast LCE behavior with conventional elastomer behavior.

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.

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.

Simulation of the interaction of light with matter, including at the few-photon level, is important for understanding the optical and optoelectronic properties of materials and for modeling next-generation nonlinear spectroscopies that use entangled light. At the few-photon level the quantum properties of the electromagnetic field must be accounted for with a quantized treatment of the field, and then such simulations quickly become intractable, especially if the matter subsystem must be modeled with a large number of degrees of freedom, as can be required to accurately capture many-body effects and quantum noise sources. Motivated by this we develop a quantum simulation framework for simulating such light-matter interactions on platforms with controllable bosonic degrees of freedom, such as vibrational modes in the trapped ion platform. The key innovation in our work is a scheme for simulating interactions with a continuum field using only a few discrete bosonic modes, which is enabled by a Green's function (response function) formalism. We develop the simulation approach, sketch how the simulation can be performed using trapped ions, and then illustrate the method with numerical examples. Our work expands the reach of quantum simulation to important light-matter interaction models and illustrates the advantages of extracting dynamical quantities such as response functions from quantum simulations.

Sandia National Laboratories and Idaho National Laboratory deployed state-of-the-art cybersecurity technologies within a virtualized, cyber-physical wind energy site to demonstrate their impact on security and resilience. This work was designed to better quantify cost-benefit tradeoffs and risk reductions when layering different security technologies on wind energy operational technology networks. Standardized step-by-step attack scenarios were drafted for adversaries with remote and local access to the wind network. Then, the team investigated the impact of encryption, access control, intrusion detection, security information and event management, and security, orchestration, automation, and response (SOAR) tools on multiple metrics, including physical impacts to the power system and termination of the adversary kill chain. We found, once programmed, the intrusion detection systems could detect attacks and the SOAR system was able to effectively and autonomously quarantine the adversary, prior to power system impacts. Cyber and physical metrics indicated network and endpoint visibility were essential to provide human defenders situational awareness to maintain system resilience. Certain hardening technologies, like encryption, reduced adversary access, but recognition and response were also critical to maintain wind site operations. Lastly, a cost-benefit analysis was performed to estimate payback periods for deploying cybersecurity technologies based on projected breach costs.

Lithium-ion batteries are typically modeled using porous electrode theory coupled with various transport and reaction mechanisms, along with suitable discretization or approximations for the solid-phase diffusion equation. The solid-phase diffusion equation represents the main computational burden for typical pseudo-2-dimensional (p2D) models since these equations in the pseudo r-dimension must be solved at each point in the computational grid. This substantially increases the complexity of the model as well as the computational time. Traditional approaches towards simplifying solid-phase diffusion possess certain significant limitations, especially in modeling emerging electrode materials which involve phase changes and variable diffusivities. A computationally efficient representation for solid-phase diffusion is discussed in this paper based on symmetric polynomials using Orthogonal Collocation and Galerkin formulation (weak form). A systematic approach is provided to increase the accuracy of the approximation (p form in finite element methods) to enable efficient simulation with a minimal number of semi-discretized equations, ensuring mass conservation even for non-linear diffusion problems involving variable diffusivities. These methods are then demonstrated by incorporation into the full p2D model, illustrating their advantages in simulating high C-rates and short-time dynamic operation of Lithium-ion batteries.

In this paper, the potential for time series classifiers to identify faults and their location in a DC Microgrid is explored. Two different classification algorithms are considered. First, a minimally random convolutional kernel transformation (MINIROCKET) is applied on the time series fault data. The transformed data is used to train a regularized linear classifier with stochastic gradient descent (SDG). Second, a continuous wavelet transform (CWT) is applied on the fault data and a convolutional neural network (CNN) is trained to learn the characteristic patterns in the CWT coefficients of the transformed data. The data used for training and testing the models are acquired from multiple fault simulations on a 750 VDC Microgrid modeled in PSCAD/EMTDC. The results from both classification algorithms are presented and compared. For an accurate classification of the fault location, the MINIROCKET and SGD Classifier model needed signals/features from several measurement nodes in the system. The CWT and CNN based model accurately identified the fault location with signals from a single measurement node in the system. By performing a self-learning monitoring and decision making analysis, protection relays equipped with time series classification algorithms can quickly detect the location of faults and isolate them to improve the protection operations on DC Microgrids.

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.

The use of high-fidelity, real-time physics engines of nuclear power plants in a cyber security training platform is feasible but requires additional research and development. This paper discusses recent developments for cybersecurity training leveraging open-source NPP simulators and network emulation tools. The paper will detail key elements of currently available environments for cybersecurity training. Key elements assessed for each environment are: (i) Management and student user interfaces, (ii) pre-developed baseline and cyber-attack effects, and (iii) capturing student results and performance. Representative and dynamic environments require integration of physics model, network emulation, commercial of the shelf hardware, and technologies that connect these together. Further, orchestration tools for management of the holistic set of models and technologies decrease time in setup and maintenance allow for click to deploy capability. The paper will describe and discuss the Sandia developed environment and open-source tools that incorporates these technologies with click-to-deploy capability. This environment was deployed for delivery of an undergraduate/graduate course with the University of Sao Paulo, Brazil in July 2022 and has been used to investigate new concepts involving Cyber-STPA analysis. This paper captures the identified future improvements, development activities, and lessons learned from the course.

The International Electrotechnical Commission (IEC) Subcommittee SC45A has been active in development of cybersecurity standards and technical reports on the protection of Instrumentation and Control (I&C) and Electrical Power Systems (ES) that perform significant functions necessary for the safe and secure operation of Nuclear Power Plants (NPP). These international standards and reports advance and promote the implementation of good practices around the world. In recent years, there have been advances in NPP cybersecurity risk management nationally and internationally. For example, IAEA publications NSS 17-T [1] and NSS 33-T [2], propose a framework for computer security risk management that implements a risk management program at both the facility and individual system levels. These international approaches (i.e., IAEA), national approaches (e.g., Canada’s HTRA [3]) and technical methods (e.g., HAZCADS [4], Cyber Informed Engineering [5], France’s EBIOS [6]) have advanced risk management within NPP cybersecurity programmes that implement international and national standards. This paper summarizes key elements of the analysis that developed the new IEC Technical Report. The paper identifies the eleven challenges for applying ISO/IEC 27005:2018 [7]. cybersecurity risk management to I&C Systems and EPS of NPPs and a summary comparison of how national approaches address these challenges.

Unlike traditional base excitation vibration qualification testing, multi-axis vibration testing methods can be significantly faster and more accurate. Here, a 12-shaker multiple-input/multiple-output (MIMO) test method called intrinsic connection excitation (ICE) is developed and assessed for use on an example aerospace component. In this study, the ICE technique utilizes 12 shakers, 1 for each boundary condition attachment degree of freedom to the component, specially designed fixtures, and MIMO control to provide an accurate set of loads and boundary conditions during the test. Acceleration, force, and voltage control provide insight into the viability of this testing method. System field test and ICE test results are compared to traditional single degree of freedom specification development and testing. Results indicate the multi-shaker ICE test provided a much more accurate replication of system field test response compared with single degree of freedom testing.

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Earth’s environment can be considered especially harsh due to the cyclic exposure of heat, moisture, oxygen, and ultraviolet (UV) and visible light. Polymer-derived materials subjected to these conditions over time often exhibit symptoms of degradation and deterioration, ultimately leading to accelerated material failure. To combat this, chemical additives known as antioxidants are often used to delay the onset of weathering and oxidative degradation. Phenol-derived antioxidants have been used for decades due to their excellent performance and stability; unfortunately, concerns regarding their toxicity and leaching susceptibility have driven researchers to identify novel solutions to replace phenolic antioxidants. Herein, we report on the antioxidant efficacy of organoborons, which have been known to exhibit antioxidant activity in plants and animals. Four different organoboron molecules were formulated into epoxy materials at various concentrations and subsequently cured into thermoset composites. Their antioxidant performance was subsequently analyzed via thermal, colorimetric, and spectroscopic techniques. Generally, thermal degradation and oxidation studies proved inconclusive and ambiguous. However, aging studies performed under thermal and UV-intensive conditions showed moderate to extreme color changes, suggesting poor antioxidant performance of all organoboron additives. Infrared spectroscopic analysis of the UV aged samples showed evidence of severe material oxidation, while the thermally aged samples showed only slight material oxidation. Solvent extraction experiments showed that even moderately high organoboron concentrations show negligible leaching susceptibility, confirming previously reported results. This finding may have benefits in applications where additive leaching may cause degradation to sensitive materials, such as microelectronics and other materials science related areas.

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Radiation Portal Monitors (RPMs) were deployed throughout the port and border infrastructure of the United States (U.S.) beginning in 2003 to monitor for the possible presence of uncontrolled radiological and nuclear materials. Since that time, the U.S. Government (USG) has learned much about the operational challenges faced in the field. Principal among the shortcomings has been the lack of flexibility afforded the USG when all Internet Protocol (IP) rights and interfaces of the system are owned by the Original Equipment Manufacturer (OEM).

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Neural ordinary differential equations (NODEs) have recently regained popularity as large-depth limits of a large class of neural networks. In particular, residual neural networks (ResNets) are equivalent to an explicit Euler discretization of an underlying NODE, where the transition from one layer to the next is one time step of the discretization. The relationship between continuous and discrete neural networks has been of particular interest. Notably, analysis from the ordinary differential equation viewpoint can potentially lead to new insights for understanding the behavior of neural networks in general. In this work, we take inspiration from differential equations to define the concept of stiffness for a ResNet via the interpretation of a ResNet as the discretization of a NODE. Here, we then examine the effects of stiffness on the ability of a ResNet to generalize, via computational studies on example problems coming from climate and chemistry models. We find that penalizing stiffness does have a unique regularizing effect, but we see no benefit to penalizing stiffness over L² regularization (penalization of network parameter norms) in terms of predictive performance.

Criticality Control Overpack (CCO) containers are being considered for the disposal of defense-related nuclear waste at the Waste Isolation Pilot Plant (WIPP). At WIPP, these containers would be placed in underground disposal rooms, which will naturally close and compact the containers closer to one another over several centuries. This report details simulations to predict the final container configuration as an input to nuclear criticality assessments. Each container was discretely modeled, including the plywood and stainless steel pipe inside the 55-gallon drum, in order to capture its complex mechanical behavior. Although these high-fidelity simulations were computationally intensive, several different material models were considered in an attempt to reasonably bound the horizontal and vertical compaction percentages. When exceptionally strong materials were used for the containers, the horizontal and vertical closure respectively stabilized at 43:9 % and 93:7 %. At the other extreme, when the containers completely degraded and the clay seams between the salt layers were glued, the horizontal and vertical closure reached respective final values of 48:6 % and 100 %.

This report documents the development of the Blue Canyon Dome (BCD) testbed, including test site selection, development, instrumentation, and logistical considerations. The BCD testbed was designed for small-scale explosive tests (~5 kg TNT equivalence maximum) for the purpose of comparing diagnostic signals from different types of explosives, the assumption being that different chemical explosives would generate different signatures on geophysical and other monitoring tools. The BCD testbed is located at the Energetic Materials Research and Testing Center near Socorro, New Mexico. Instrumentation includes an electrical resistivity tomography array, geophones, distributed acoustic sensing, gas samplers, distributed temperature sensing, pressure transducers, and high-speed cameras. This SAND report is a reference for BCD testbed development that can be cited in future publications.

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Poster presentation at Keystone Symposia on BBB penetrating nanobody project.

A natural clinoptilolite sample near the Nevada National Security Site was obtained to study adsorption and retardation on gas transport. Of interest is understanding the competition for adsorption sites that may reduce tracer gas adsorption relative to single-component measurements, which may be affected by the multi-scale pore structure of clinoptilolite. Clinoptilolite has three distinct domains of pore size distributions ranging from nanometers to micrometers: micropores with 0.4–0.7 nm diameters, measured on powders by CO2 adsorption at 273 K, representing the zeolite cages; mesopores with 4–200 nm diameters, observed using liquid nitrogen adsorption at 77 K; and macropores with 300–1000 nm diameters, measured by mercury injection on rock chips (~ 100 mesh), likely representing the microfractures. These pore size distributions are consistent with X-ray computed tomography (CT) and focused ion beam scanning electron microscope (FIB-SEM) images, which are used to construct the three-dimensional (3D) pore network to be used in future gas transport modeling. To quantify tracer gas adsorption in this multi-scale pore structure and multicomponent gas species environment, natural zeolite samples initially in equilibrium in air were exposed to a mixture of tracer gases. As the tracer gases diffuse and adsorb in the sample, the remaining tracer gases outside the sample fractionate. Using a quadrupole mass spectrometer to quantify this fractionation, the degree of adsorption of tracer gases in the multicomponent gas environment and multi-scale pore structure is assessed. The major finding is that Kr reaches equilibrium much faster than Xe in the presence of ambient air, which leads to more Kr uptake than Xe over limited exposure periods. When the clinoptilolite chips were exposed to humid air, the adsorption capability decreases significantly for both Xe and Kr with relative humidity (RH) as low as 3%. Both Xe and Kr reaches equilibrium faster at higher RH. The different, unexpected, adsorption behavior for Xe and Kr is due to their kinetic diameters similar to the micropores in clinoptilolite which makes it harder for Xe to access compared to Kr.

The Big Hill SPR site has a rich data set consisting of multi-arm caliper (MAC) logs collected from the cavern wells. This data set provides insight into the on-going casing deformation at the Big Hill site. This report summarizes the MAC surveys for each well and presents well longevity estimates where possible. Included in the report is an examination of the well twins for each cavern and a discussion on what may or may not be responsible for the different levels of deformation between some of the well twins. The report also takes a systematic view of the MAC data presenting spatial patterns of casing deformation and deformation orientation in an effort to better understand the underlying causes. The conclusions present a hypothesis suggesting the small-scale variations in casing deformation are attributable to similar scale variations in the character of the salt-caprock interface. These variations do not appear directly related to shear zones or faults.

The V31 containment vessel was procured by the US Army Recovered Chemical Materiel 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 twenty-four (24) pounds 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 24lbs of Composition C-4 (30 lb 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 of Composition C-4 (24 lb TNT equivalent). Qualification test (3) consisted of a 12-pack of regular, right circular cylinders of 2 lb each, distributed evenly inside the vessel (totaling 19.2 lb of C-4, or 24 lb TNT equivalent). All vessel acceptance criteria were met.

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We evaluate neural radiance fields (NeRFs) as a method for reconstructing 3D volumetric scenes from low Earth orbit satellite imagery. We leverage commercial satellite data to reconstruct a scene using existing software tools. In doing so, we identify difficulties in these mapping datasets for NeRF generation. We propose potential applications in geospatial intelligence for context and improved image interpretation.

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