Publications

Natural gas pipelines could be an important pathway to transport gaseous hydrogen (GH2) as a cleaner alternative to fossil fuels. However, a comprehensive understanding of hydrogen-assisted fatigue and fracture resistance in pipeline steels is needed, including an assessment of the diverse microstructures present in natural gas infrastructure. In thus study, we focus on modern steel pipe and consider both welded pipe and seamless pipe. In-situ fatigue crack growth (FCG) and fracture tests were conducted on compact tension samples extracted from the base metal, seam-weld, and heat affected zone of an X70 pipe steel in high-purity GH2 (210 bar pressure). Additionally, a seamless X65 pipeline microstructure (with comparable strength) was evaluated to compare the different microstructure of seamless pipe. The different microstructures had comparable FCG rates in GH2, with crack growth rates up to 30 times faster in hydrogen compared to air. In contrast, the fracture resistance in GH2 depended on the characteristics of the microstructure varying in the range of approximately 80 to 110 MPa√m.

Optimization is a key tool for scientific and engineering applications; however, in the presence of models affected by uncertainty, the optimization formulation needs to be extended to consider statistics of the quantity of interest. Optimization under uncertainty (OUU) deals with this endeavor and requires uncertainty quantification analyses at several design locations; i.e., its overall computational cost is proportional to the cost of performing a forward uncertainty analysis at each design location. An OUU workflow has two main components: an inner loop strategy for the computation of statistics of the quantity of interest, and an outer loop optimization strategy tasked with finding the optimal design, given a merit function based on the inner loop statistics. In this work, we propose to alleviate the cost of the inner loop uncertainty analysis by leveraging the so-called multilevel Monte Carlo (MLMC) method, which is able to allocate resources over multiple models with varying accuracy and cost. The resource allocation problem in MLMC is formulated by minimizing the computational cost given a target variance for the estimator. We consider MLMC estimators for statistics usually employed in OUU workflows and solve the corresponding allocation problem. For the outer loop, we consider a derivative-free optimization strategy implemented in the SNOWPAC library; our novel strategy is implemented and released in the Dakota software toolkit. We discuss several numerical test cases to showcase the features and performance of our approach with respect to its Monte Carlo single fidelity counterpart.

Battery systems are typically equipped with state of charge (SoC) estimation algorithms. Sensor measurements used to estimate SoC are susceptible to false data injection attacks (FDIAs) that aim to disturb state estimation and, consequently, damage the system. In this paper, SoC estimation methods are re-purposed to detect FDIAs targeting the current and voltage sensors of a battery stack using a combination of an improved input noise aware unscented Kalman filter (INAUKF) and a cumulative sum detector. The root mean squared error of the states estimated by the INAUKF was at least 85% lower than the traditional unscented Kalman filter for all noise levels tested. The proposed method was able to detect FDIA in the current and voltage sensors of a series-connected battery stack in 99.55% of the simulations.

Ionic liquid (IL) pretreatment methods show incredible promise for the efficient conversion of lignocellulosic feedstocks to fuels and chemicals. Given their low vapor pressures, distillation-based methods of extracting ionic liquids out of biomass post-pretreatment have historically been ignored in favor of alternative methods. We demonstrate a process to distill four acetate-based ionic liquids ([EthA][OAc], [PropA][OAc], [MAEthA][OAc], and [DMAEthA][OAc]) at low pressure and high purity that overcome some disadvantages of “water washing” and “one pot” recovery methods. Out of four tested ILs, ethanolamine acetate ([EthA][OAc]) is shown to have the most agreeable conversion metrics for commercial bioconversion processes achieving 73.6 % and 51.4 % of theoretical glucose and xylose yields respectively and >85 % recovery rates. Our process metrics are factored into a techno-economic analysis where [EthA][OAc] distillation is compared to other recovery methods as well as ethanolamine pretreatment at both milliliter and liter scales. Although our TEA shows [EthA][OAc] distillation underperforming against other processes, we show a step-by-step avenue to reduce sugar production cost below the wholesale dextrose price at scale.

Diesel generators (gensets) are often the lowest-cost electric generation for reliable supply in remote microgrids. The development of converter-dominated diesel-backed microgrids requires accurate dynamic modeling to ensure power quality and system stability. Dynamic response derived using original genset system models often does not match those observed in field experiments. This paper presents the experimental system identification of a frequency dynamics model for a 400 kVA diesel genset. The genset is perturbed via active power load changes and a linearized dynamics model is fit based on power and frequency measurements using moving horizon estimation (MHE). The method is first simulated using a detailed genset model developed in MATLAB/Simulink. The simulation model is then validated against the frequency response obtained from a real 400 kVA genset system at the Power System Integration (PSI) Lab at the University of Alaska Fairbanks (UAF). The simulation and experimental results had model errors of 3.17% and 11.65%, respectively. The resulting genset model can then be used in microgrid frequency dynamic studies, such as for the integration of renewable energy sources.

Deployed Lithium-ion batteries are often equipped with battery management systems (BMSs) that monitor sensor readings, estimate states, and ensure safe operation. Unfortunately, additive bias anomalies could corrupt sensor readings used in state estimation, leading to degraded performance or hazardous conditions. Existing protection features of BMSs can be augmented with model-based anomaly detection by combining battery modeling, estimation, and detection algorithms. In this paper, an equivalent circuit model and charge reservoir model were used to model a battery cell, an unscented Kalman filter was used for estimation, and four online model-based anomaly detection methods (chi-squared test, cumulative sum (CUSUM) algorithm, summation detector, and Shewhart control chart) were compared. When evaluated in terms of false positive rate and detection capability, and the CUSUM algorithm was the superior model-based anomaly detector due to its false positive rate of 0%, its ability to detect large and small magnitude anomalies, and its ability to classify anomalies as positively or negatively biased.

Network Operation Centers (NOCs) and Security Operation Centers (SOCs) play a critical role in addressing a wide range of threats in critical infrastructure systems such as the electric grid. However, when considering the electric grid and related industrial control systems (ICSs), visibility into the information technology (IT), operational technology (OT), and underlying physical process systems are often disconnected and standalone. As the electric grid becomes increasingly cyber-physical and faces dynamic, cyber-physical threats, it is vital that cyber-physical situational awareness (CPSA) across the interconnected system is achieved. In this paper, we review existing NOC and SOC capabilities and visualizations, motivate the need for CPSA, and define design principles with example visualizations for a next-generation grid cyber-physical integrated SOC (CP-ISOC).

Characterizing the subsurface properties such as permeability and thermal conductivity is important for stimulation planning and heat production in enhanced geothermal systems (EGS). Data assimilation methods, such as the Kalman-type methods, are widely used for characterization by assimilating observed dynamic data such as pressure and temperature. However, these approaches only perform well when the parameters follow a Gaussian distribution. The geothermal sites are usually highly heterogeneous with non-Gaussian distributed complex structures such as faults and fractures, which are difficult to characterize. Over the past few years, emerging deep generative models and their impressive applications in different tasks have provided a solution to produce images with complicated features. In this work, we use the Wasserstein Generative Adversarial Network (WGAN), a deep generative model, to generate fractured images from the low-dimensional and Gaussian distributed latent space. The ensemble method, a Kalman-type data assimilation method, is then applied to the latent variables to characterize the permeability fields of a fractured geothermal site using temperature data. A synthetic two-dimensional example is presented to show the performance of our approach.

We present a materials study of AlGaInP grown on GaAs leveraging deep-level optical spectroscopy and time resolved photoluminescence. Our materials may serve as the basis for wide-bandgap analogs of silicon photomultipliers optimized for short wavelength sensing.

Gallium nitride (GaN)-based nanoscale vacuum electron devices, which offer advantages of both traditional vacuum tube operation and modern solid-state technology, are attractive for radiation-hard applications due to the inherent radiation hardness of vacuum electron devices and the high radiation tolerance of GaN. Here, we investigate the radiation hardness of top-down fabricated n-GaN nanoscale vacuum electron diodes (NVEDs) irradiated with 2.5-MeV protons (p) at various doses. We observe a slight decrease in forward current and a slight increase in reverse leakage current as a function of cumulative protons fluence due to a dopant compensation effect. The NVEDs overall show excellent radiation hardness with no major change in electrical characteristics up to a cumulative fluence of 5E14 p/cm2, which is significantly higher than the existing state-of-the-art radiation-hardened devices to our knowledge. The results show promise for a new class of GaN-based nanoscale vacuum electron devices for use in harsh radiation environments and space applications.

The siting of nuclear waste is a process that requires consideration of concerns of the public. This report demonstrates the significant potential for natural language processing techniques to gain insights into public narratives around “nuclear waste.” Specifically, the report highlights that the general discourse regarding “nuclear waste” within the news media has fluctuated in prevalence compared to “nuclear” topics broadly over recent years, with commonly mentioned entities reflecting a limited variety of geographies and stakeholders. General sentiments within the “nuclear waste” articles appear to use neutral language, suggesting that a scientific or “facts-only” framing of “waste”-related issues dominates coverage; however, the exact nuances should be further evaluated. The implications of a number of these insights about how nuclear waste is framed in traditional media (e.g., regarding emerging technologies, historical events, and specific organizations) are discussed. This report lays the groundwork for larger, more systematic research using, for example, transformer-based techniques and covariance analysis to better understand relationships among “nuclear waste” and other nuclear topics, sentiments of specific entities, and patterns across space and time (including in a particular region). By identifying priorities and knowledge needs, these data-driven methods can complement and inform engagement strategies that promote dialogue and mutual learning regarding nuclear waste.

Characterizing the subsurface properties such as permeability and thermal conductivity is important for stimulation planning and heat production in enhanced geothermal systems (EGS). Data assimilation methods, such as the Kalman-type methods, are widely used for characterization by assimilating observed dynamic data such as pressure and temperature. However, these approaches only perform well when the parameters follow a Gaussian distribution. The geothermal sites are usually highly heterogeneous with non-Gaussian distributed complex structures such as faults and fractures, which are difficult to characterize. Over the past few years, emerging deep generative models and their impressive applications in different tasks have provided a solution to produce images with complicated features. In this work, we use the Wasserstein Generative Adversarial Network (WGAN), a deep generative model, to generate fractured images from the low-dimensional and Gaussian distributed latent space. The ensemble method, a Kalman-type data assimilation method, is then applied to the latent variables to characterize the permeability fields of a fractured geothermal site using temperature data. A synthetic two-dimensional example is presented to show the performance of our approach.

Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the performance loss rate (PLR). Since the real values of PLR are unknown, a variety of unvalidated values are reported. As such, the PV industry commonly assumes PLR based on statistically extracted ranges from the literature. However, the accuracy and uncertainty of PLR depend on several parameters including seasonality, local climatic conditions, and the response of a particular PV technology. In addition, the specific data pipeline and statistical method used affect the accuracy and uncertainty. To provide insights, a framework of (≈200 million) synthetic simulations of PV performance datasets using data from different climates is developed. Time series with known PLR and data quality are synthesized, and large parametric studies are conducted to examine the accuracy and uncertainty of different statistical approaches over the contiguous US, with an emphasis on the publicly available and “standardized” library, RdTools. In the results, it is confirmed that PLRs from RdTools are unbiased on average, but the accuracy and uncertainty of individual PLR estimates vary with climate zone, data quality, PV technology, and choice of analysis workflow. Best practices and improvement recommendations based on the findings of this study are provided.

We use complete polarization tomography of photon pairs generated in semiconductor metasurfaces via spontaneous parametric down-conversion to show how bound states in the continuum resonances affect the polarization state of the emitted photons.

Point-source transient events (PSTEs) - optical events that are both extremely fast and extremely small - pose several challenges to an imaging system. Due to their speed, accurately characterizing such events often requires detectors with very high frame rates. Due to their size, accurately detecting such events requires maintaining coverage over an extended field-of-view, often through the use of imaging focal plane arrays (FPA) with a global shutter readout. Traditional imaging systems that meet these requirements are costly in terms of price, size, weight, power consumption, and data bandwidth, and there is a need for cheaper solutions with adequate temporal and spatial coverage. To address these issues, we develop a novel compressed sensing algorithm adapted to the rolling shutter readout of an imaging system. This approach enables reconstruction of a PSTE signature at the sampling rate of the rolling shutter, offering a 1-2 order of magnitude temporal speedup and a proportional reduction in data bandwidth. We present empirical results demonstrating accurate recovery of PSTEs using measurements that are spatially undersampled by a factor of 25, and our simulations show that, relative to other compressed sensing algorithms, our algorithm is both faster and yields higher quality reconstructions. We also present theoretical results characterizing our algorithm and corroborating simulations. The potential impact of our work includes the development of much faster, cheaper sensor solutions for PSTE detection and characterization.

Characterizing shielding effectiveness (SE) of enclosures is important in aerospace, military, and consumer applications. Direct SE measurement of an enclosure or chassis may be considered an exact characterization, but there are several sources of possible variability in such measurements, e.g., mechanical tolerances, the absence of components during test that exist in a final assembly, movement of components and cables, and perturbations due to probes and associated cabling. In [1] , internal stirrers were investigated as a way to sample the variation of SE of small enclosures when populated with random metallic objects. Here, we explore this idea as a way to quantify variability and sensitivity of an SE measurement, not only indicating the uncertainty of the SE measurement, but also delineating frequency ranges where either deterministic or statistical simulations should be applied.

Sandia National Laboratories (SNL) has completed a comparative evaluation of three design assessment approaches for a 2-liter (2L) capacity containment vessel (CV) of a novel plutonium air transport (PAT) package designed to survive the hypothetical accident condition (HAC) test sequence defined in Title 10 of the United States (US) Code of Federal Regulations (CFR) Part 71.74(a), which includes a 129 meter per second (m/s) impact of the package into an essentially unyielding target. CVs for hazardous materials transportation packages certified in the US are typically designed per the requirements defined in the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code (B&PVC) Section III Division 3 Subsection WB “Class TC Transportation Containments.” For accident conditions, the level D service limits and analysis approaches specified in paragraph WB-3224 are applicable. Data derived from finite element analyses of the 129 m/s impact of the 2L-PAT package were utilized to assess the adequacy of the CV design. Three different CV assessment approaches were investigated and compared, one based on stress intensity limits defined in subparagraph WB-3224.2 for plastic analyses (the stress-based approach), a second based on strain limits defined in subparagraph WB-3224.3, subarticle WB-3700, and Section III Nonmandatory Appendix FF for the alternate strain-based acceptance criteria approach (the strain-based approach), and a third based on failure strain limits derived from a ductile fracture model with dependencies on the stress and strain state of the material, and their histories (the Xue-Wierzbicki (X-W) failure-integral-based approach). This paper gives a brief overview of the 2L-PAT package design, describes the finite element model used to determine stresses and strains in the CV generated by the 129 m/s impact HAC, summarizes the three assessment approaches investigated, discusses the analyses that were performed and the results of those analyses, and provides a comparison between the outcomes of the three assessment approaches.

Tabulated chemistry models are widely used to simulate large-scale turbulent fires in applications including energy generation and fire safety. Tabulation via piecewise Cartesian interpolation suffers from the curse-of-dimensionality, leading to a prohibitive exponential growth in parameters and memory usage as more dimensions are considered. Artificial neural networks (ANNs) have attracted attention for constructing surrogates for chemistry models due to their ability to perform high-dimensional approximation. However, due to well-known pathologies regarding the realization of suboptimal local minima during training, in practice they do not converge and provide unreliable accuracy. Partition of unity networks (POUnets) are a recently introduced family of ANNs which preserve notions of convergence while performing high-dimensional approximation, discovering a mesh-free partition of space which may be used to perform optimal polynomial approximation. We assess their performance with respect to accuracy and model complexity in reconstructing unstructured flamelet data representative of nonadiabatic pool fire models. Our results show that POUnets can provide the desirable accuracy of classical spline-based interpolants with the low memory footprint of traditional ANNs while converging faster to significantly lower errors than ANNs. For example, we observe POUnets obtaining target accuracies in two dimensions with 40 to 50 times less memory and roughly double the compression in three dimensions. We also address the practical matter of efficiently training accurate POUnets by studying convergence over key hyperparameters, the impact of partition/basis formulation, and the sensitivity to initialization.

Dendrites enable neurons to perform nonlinear operations. Existing silicon dendrite circuits sufficiently model passive and active characteristics, but do not exploit shunting inhibition as an active mechanism. We present a dendrite circuit implemented on a reconfigurable analog platform that uses active inhibitory conductance signals to modulate the circuit's membrane potential. We explore the potential use of this circuit for direction selectivity by emulating recent observations demonstrating a role for shunting inhibition in a directionally-selective Drosophila (Fruit Fly) neuron.

As conventional direct connections of synchronous generators are being phased out, inverter-based resources (IBRs) with grid support functions are increasingly being integrated into power systems. This transition requires the development of accurate dynamic models for IBRs to predict how power systems will adapt to varying levels of IBRs penetration, establish grid code requirements, and ensure compliance. This study introduces an active probing signal-based data-driven modeling technique to accurately derive the dynamics model of a smart photovoltaic inverter operating in Volt-Watt and Freq-Watt modes, in compliance with the IEEE 1547-2018 standard. The paper focuses on investigating how the dynamics of the PV inverter model respond to fluctuations in solar irradiance, utilizing real-time digital simulator experimentation. The experimental analysis demonstrates that the amplitude of dynamics fluctuates with changes in irradiance across both operational modes and confirms the active power's dependence on irradiance levels. Furthermore, the nature of inverter dynamics varies distinctly between the different modes of activation. Critically, our findings indicate that dynamic models require DC-gain adjustments to accommodate contrasting irradiance levels, highlighting a negative gradient linear relationship between the DC-gain of each model and the irradiance.

We present a physics-driven modeling framework for early-time detonation soot formation, integrating hydrodynamic flow simulations and particle growth simulations to predict particle dynamics. Validated against SAXS data, our model supports diffusion-limited growth. Molecular dynamics simulations provide diffusion rates to keep particle models species-informed. The methodology is tested on a gram-scale colliding-wave explosive geometry to explore sensitivity of particle fusion to temperature and initial size. This modeling framework, decoupled from empirical methods, enhances predictive capabilities in explosive soot modeling.

This study is focused on developing a numerical model to evaluate the performance of a hydraulic PTO system for the TALOS Wave Energy Converter. The WEC device is described and the architecture of the hydraulic PTO system is presented with detail. The WEC is modeled using WEC-Sim, and the PTO is modeled using the Simscape Fluids library from Simulink. The hydraulic PTO is based on a constant pressure configuration that is suitable for WEC passive control. The hydraulic system is composed by a set of rectifying valves and two hydraulic accumulators that reduce the stiffness of the system and also serve as energy storage devices. One of the advantages of this hydraulic PTO architecture is the possibility of controlling the electric generator to operate around the optimal efficiency operating point. The main components of the hydraulic PTO are off-the-shelf devices that are commercially available, which will facility a future deployment of the designed system. The design variables used for this study are the accumulator size, the maximum pressure in the accumulators, the hydraulic motor maximum displacement, and the shaft speed in the electric generator. The performance of the system is evaluated individually, using sinusoidal inputs that replicates regular wave conditions. In addition to this, the numerical model of the PTO is coupled to a WEC-Sim simulation of the TALOS Wave Energy Converter with six PTOs to generate a wave-to-wire model. The main objective of this work is to present a comprehensive design methodology that could serve as a guideline for future research efforts focused on implementing control algorithms on multi degree of freedom WECs.

A method for battery state of charge (SoC) estimation that compensates input noise using an adaptive square-root unscented Kalman filter (ASRUKF) is presented in this paper. In contrast to traditional state estimation approaches that consider deterministic system inputs, this method can improve the accuracy of battery state estimator by considering that the measurements of the control input variable of the filter, the cell currents, are subject to noise. Also, this paper presents two estimators for input and output noise covariance. The proposed method consists of initialization, state correction, sigma point calculations, state prediction, and covariance estimation steps and is demonstrated using simulations. We simulate two battery cycling protocols of three series-connected batteries whose SoC is estimated by the proposed method. The results show that the improved ASRUKF can track closely the states and achieves a 20.63 % reduction in SoC estimation error when compared to a benchmark that does not consider input noise.

Deep neural networks for automatic target recognition (ATR) have been shown to be highly successful for a large variety of Synthetic Aperture Radar (SAR) benchmark datasets. However, the black box nature of neural network approaches raises concerns about how models come to their decisions, especially when in high-stake scenarios. Accordingly, a variety of techniques are being pursued seeking to offer understanding of machine learning algorithms. In this paper, we first provide an overview of explainability and interpretability techniques introducing their concepts and the insights they produce. Next we summarize several methods for computing specific approaches to explainability and interpretability as well as analyzing their outputs. Finally, we demonstrate the application of several attribution map methods and apply both attribution analysis metrics as well as localization interpretability analysis to six neural network models trained on the Synthetic and Measured Paired Labeled Experiment (SAMPLE) dataset to illustrate the insights these methods offer for analyzing SAR ATR performance.

Coherent anti-Stokes Raman scattering (CARS) and nitric oxide molecular tagging velocimetry (NO-MTV) are used to characterize the freestream in Sandia’s Hypersonic Shock Tunnel (HST) using a burst-mode laser operated at 100-kHz. Experiments are performed at nominal freestream velocities of 3 and 4 km/s using both air and N2 test gas. The CARS diagnostic provides nonequilibrium characterization of the flow by measuring vibrational and rotational temperatures of N2 and O2, which are compared to NO temperatures from separate laser absorption experiments. Simultaneous, colinear freestream velocities are measured using NO MTV along with pitot pressures. This extensive freestream dataset is compared to nonequilibrium CFD capable of modeling species-specific, vibrational temperatures throughout the nozzle expansion. Significant nonequilibrium between vibrational and rotational temperatures are measured at each flow condition. N2 exhibits the most nonequilibrium followed by O2 and NO. The CFD model captures this trend, although it consistently overpredicts N2 vibrational temperatures. The modeled temperatures agree with the O2 data. At 3 km/s, the modeled NO nonequilibrium is underpredicted, whereas it is overpredicted at 4 km/s. Good agreement is seen between CFD and the velocity and rotational temperature measurements. Experiments with water added to the test gas yielded no discernable difference in vibrational relaxation.

With the amount of neuromorphic tools and frame-works growing in number, we recognize a need to increase interoperability within our field. As an illustration of this, we explore linking two independently constructed tools. Specifically, we detail the construction of an a execution backend based on STACS: Simulation Tool for Asynchronous Cortical Streams for the Fugu spiking neural algorithms framework. STACS extends the computational scope of Fugu, enabling fast simulation of large-scale neural networks. Combining these two tools is shown to be mutually beneficial, ultimately enabling more functionality than either tool on its own. We discuss design considerations, in-cluding recognizing the advantages of straightforward standards. Further, we provide some benchmark results showing drastic improvements in execution time.

The Rotor Aerodynamics, Aeroelastics, and Wake (RAAW) project's main objective was collecting data for validation of aerodynamic and aeroelastic codes for large, flexible rotors. These data come from scanning lidars of the inflow and wake, met tower, profiling lidar, blade deflection from photogrammetry, turbine SCADA data (including root bending loads), and hub-mounted SpinnerLidar inflow measurements. The goal of the present work is to analyze various methods to align the SpinnerLidar inflow data in time and space with individual blade loading. These methods would prove a way of analyzing turbine response while estimating the flowfield at each blade and provide a way of improving turbine response understanding using field data in real time, not just from simulations. The hub-mounted SpinnerLidar measures the inflow in the rotor frame meaning the locations of the blades relative to the measurement pattern do not change. The present work outlines some methods for correlating the SpinnerLidar inflow measurements with root bending loads in the rotor frame of reference accounting for both changes in wind speed and rotor speed from the measurement location one diameter upstream to each blade.

In order to characterize and benchmark computational hardware, software, and algorithms, it is essential to have many problem instances on-hand. This is no less true for quantum computation, where a large collection of real-world problem instances would allow for benchmarking studies that in turn help to improve both algorithms and hardware designs. To this end, here we present a large dataset of qubit-based quantum Hamiltonians. The dataset, called HamLib (for Hamiltonian Library), is freely available online and contains problem sizes ranging from 2 to 1000 qubits. HamLib includes problem instances of the Heisenberg model, Fermi-Hubbard model, Bose-Hubbard model, molecular electronic structure, molecular vibrational structure, MaxCut, Max-k-SAT, Max-k-Cut, QMaxCut, and the traveling salesperson problem. The goals of this effort are (a) to save researchers time by eliminating the need to prepare problem instances and map them to qubit representations, (b) to allow for more thorough tests of new algorithms and hardware, and (c) to allow for reproducibility and standardization across research studies.

The Rydberg dipole blockade has emerged as the standard mechanism to induce entanglement between neutral-Atom qubits. In these protocols, laser fields that couple qubit states to Rydberg states are modulated to implement entangling gates. Here we present an alternative protocol to implement entangling gates via Rydberg dressing and a microwave-field-driven spin-flip blockade [Y.-Y. Jau, Nat. Phys. 12, 71 (2016)1745-247310.1038/nphys3487]. We consider the specific example of qubits encoded in the clock states of cesium. An auxiliary hyperfine state is optically dressed so that it acquires partial Rydberg character. It thus acts as a proxy Rydberg state, with a nonlinear light shift that plays the role of blockade strength. A microwave-frequency field coupling a qubit state to this dressed auxiliary state can be modulated to implement entangling gates. Logic gate protocols designed for the optical regime can be imported to this microwave regime, for which experimental control methods are more robust. We show that unlike the strong dipole-blockade regime usually employed in Rydberg experiments, going to a moderate-spin-flip-blockade regime results in faster gates and smaller Rydberg decay. We study various regimes of operations that can yield high-fidelity two-qubit entangling gates and characterize their analytical behavior. In addition to the inherent robustness of microwave control, we can design these gates to be more robust to laser amplitude and frequency noises at the cost of a small increase in Rydberg decay.

Vapor-deposited PETN films undergo significant microstructure evolution when exposed to elevated temperatures, even for short periods of time. This accelerated aging impacts initiation behavior and can lead to chemical changes as well. In this study, as-deposited and aged PETN films are characterized using scanning electron microscopy and ultra-high performance liquid chromatography and compared with changes in initiation behavior measured via a high-throughput experimental platform that uses laser-driven flyers to sequentially impact an array of small explosive samples. Accelerated aging leads to rapid coarsening of the grain structure. At longer times, little additional coarsening is evident, but the distribution of porosity continues to evolve. These changes in microstructure correspond to shifts in the initiation threshold and onset of reactions to higher flyer impact velocities.

Multi-axis testing has become a popular test method because it provides a more realistic simulation of a field environment when compared to traditional vibration testing. However, field data may not be available to derive the multi-axis environment. This means that methods are needed to generate “virtual field data” that can be used in place of measured field data. Transfer path analysis (TPA) has been suggested as a method to do this since it can be used to estimate the excitation forces on a legacy system and then apply these forces to a new system to generate virtual field data. This chapter will provide a review of using TPA methods to do this. It will include a brief background on TPA, discuss the benefits of using TPA to compute virtual field data, and delve into the areas for future work that could make TPA more useful in this application.

This paper introduces a method for detecting stealthy false data injection attacks on the sensors of state of charge estimation algorithms used in battery management systems (BMSs). This method is based on sensor encoding, which is the active modification of sensor data streams. This method implements low-cost verification of the integrity of measurement data, allowing for the detection of stealthy additive attack vectors. It is considered that these attacks are crafted by malicious actors with knowledge of system models and who are capable of tampering with any number of measurements. The solution involves encoding all vulnerable measurements. The effectiveness of the method is demonstrated by simulations, where a stealthy attack on an encoded measurement vector captured by a BMS generates large residuals that trigger a chi-squared anomaly detector. Within the context of a defense-in-depth strategy, this method can be combined with other cybersecurity controls, such as encryption of data-in-transit, to equip cyberphysical systems with an additional line of defense against cyberattacks.

Significant research and development efforts are underway to ensure the cybersecurity of critical infrastructure and other operational technology (OT) systems. Given the high demand for safety and availability of OT systems, novel OT tools and systems must be designed and tested in a consequence-aware environment. This requires the seamless integration of high-fidelity physics simulations with emulations of OT devices and networks. This paper introduces a modular simulation environment for an oil and gas compressor station and its local networks. This environment will be used to support the development of novel tools to predict, detect, and mitigate cyber threats on critical infrastructure. This paper also describes future plans to expand the scale of the environment and its use cases.

Abstract not provided.

Measurements of the oxidation rates of various forms of carbon (soot, graphite, coal char) have often shown an unexplained attenuation with increasing temperatures in the vicinity of 2000 K, even when accounting for diffusional transport limitations and gas-phase chemical effects (e.g. CO2 dissociation). With the development of oxy-fuel combustion approaches for pulverized coal utilization with carbon capture, high particle temperatures are readily achieved in sufficiently oxygen-enriched environments. In this work, a new semi-global intrinsic kinetics model for high temperature carbon oxidation is created by starting with a previously developed 5-step mechanism that was shown to reproduce all major known trends in carbon oxidation, except for its high temperature kinetic falloff, and incorporating a recently discovered surface oxide decomposition step. The predictions of this new model are benchmarked by deploying the kinetic model in a steady-state reacting particle code (SKIPPY) and comparing the simulated results against a carefully measured set of pulverized coal char combustion temperature measurements over a wide range of oxygen concentrations in N2 and CO2 environments. The results show that the inclusion of the spontaneous surface oxide decomposition reaction step significantly improves predictions at high particle temperatures. Furthermore, the simulations reveal that O atoms released from the oxide decomposition step enhance the radical pool in the near-surface region and within the particle interior itself. Incorporation of literature rates for O and OH reactions with the carbon surface results in a reduction in the predicted radical pool concentrations and a very minor enhancement of the overall carbon oxidation rate.

Intermolecular Coulombic decay (ICD) in liquid water is a relatively novel type of nonlocal electronic decay mechanism, competing with the traditional mechanism of proton transfer between neighboring water molecules. Key features of ICD are its ultrafast non-radiative decay process and ultralong-range for excess energy transfer from the excited atom/molecule to its neighbors. Since detecting unambiguous ICD signatures in bulk liquid water is technically challenging, small water clusters have often been utilized to gain insights into ICD and other ionization processes in aqueous environment. Here, we present results from quantum mechanical calculations of the electronic structures of neutral to multiply-ionized water monomer, dimer, trimer, and tetramer. Core-level electrons of water are also considered here since recent studies demonstrated that emission site and energy of the electrons released during resonant-Auger-ICD cascade can be controlled by coupling ICD to resonant core excitation. Previous studies of ICD and electronic structures of neutral and ionized small water clusters and liquid water are briefly discussed.

A mesoscale model for the shock initiation of pentaerythritol tetranitrate (PETN) films has been utilized to elucidate changes in initiation thresholds due to aging conditions and surface roughness, as has been observed from a series of high-throughput initiation (HTI) experiments. The HTI experiment has generated a wealth of thin-pulse, sub-millimeter shock initiation data for vapor deposited PETN films with thicknesses of 67-125 μm and varying accelerated aging conditions. This is because the HTI experiment provides access to growth-to-detonation information for explosives that exhibit a shock-to-detonation transition (SDT) with length and time scales that are too short to be resolved by conventional experiments. Mesoscale modeling results using experimentally characterized PETN microstructures are able to capture the general trend observed in experiments, in that increasing flyer impact velocity increases reactions until full detonation is reached. Moreover, the varying degrees of surface roughness that were considered were found to provide only minor variances in the peak particle velocity at the explosive output. The model did not predict a shift in the initiation threshold due to aged microstructures alone, indicating that additional mesoscale model improvements are necessary.

Glass wedges are used increase the dimensionality of various optical measurements. Light refracted through the wedges can be focused to closely spaced points, lines or planes as shown in the applications herein.

Structural materials used in combustion or power generation systems need to have both environmental and temperature resistance to ensure long-term performance. As the energy sector transitions to hydrogen, there is a need to ensure compatibility of highly-alloyed austenitic steels and nickel-based alloys with hydrogen over a range of temperatures. Hydrogen embrittlement of these alloy systems is often considered most detrimental near ambient temperatures and low temperatures, although there is some evidence in the literature that hydrogen can affect creep behavior at elevated temperature. In the intermediate temperature range (e.g., 100-400C), it is uncertain whether hydrogen degradation of mechanical properties will be of concern. In this study, three alloys (304L, IN625, Hastelloy X) commonly used in power generation systems were thermally precharged with hydrogen and subsequently tensile tested to failure in air at temperatures ranging from 20°C to 200°C. At 20°C, the hydrogen-precharged condition for all materials exhibited loss in ductility with relative reduction of area ranging between 32% and 57%. The three alloys exhibited different trends with temperature but, in general, the relative reduction of area improved with increasing temperature tending towards noncharged behavior. Tests were performed at a nominal strain rate of 2 x 10-3 s-1 in order to minimize loss of hydrogen during elevated temperature testing. Hydrogen contents from the grip sections were measured both before and after testing and remained within 10% of starting content for 100°C tests and within 8-23% for 200°C tests.

Engineers are interested in the ability to compare dynamic environments for many reasons. Current methods of comparing environments compare the measured acceleration at the same physical point via a direct measurement during the two environments. Comparing the acceleration at a defined point only provides a comparison of response at that location. However, the stress and strain of the structure are defined by the global response of all the points in a structure. This chapter uses modal filtering to transform a set of measurements at physical degrees of freedom into modal degrees of freedom that quantify the global response of the structure. Once the global response of the structure is quantified, two environments can be more reliably and accurately compared. This chapter compares the response of an aerospace component in a service environment to the response of the same component in a laboratory test environment. The comparison first compares the mode shapes between the two environments. Once it is determined that the same mode shapes are present in both configurations, the modal accelerations are compared in order to determine the similarity of the global response of the component.

Photovoltaic modules undergoing laboratory hail tests were observed using high speed video to analyze the key characteristics of impact-induced glass fracture, including crack onset time, initiation location relative to the impact site, and propagation trends. Fifteen commercially representative glass-glass thin-film modules were recorded at 300,000 frames per second during hail impacts which happened to cause glass fracture. Images were processed to identify the time between impact and first plausible glass crack appearance (average 126 μs, standard deviation 59 μs) along with the time to a confirmed crack (average 158 μs, standard deviation 77μs), during the ice ball impacts which had a median kinetic energy of 47 J delivered by 55 mm diameter balls. Limiting factors for identifying glass crack timings were ice ball fragmentation obscuring the impact site and indistinct initial crack appearance, which were inherent to the images and not improved with processing. Computational simulations corresponding to each impact event showed that glass stresses were still localized to the impact site during times with definitively identifiable fracture, and even impacts which did not induce failure created local stress magnitudes exceeding stress levels associated with static glass fracture. These observations confirm that impact-induced glass failure is a time-and rate-dependent phenomena. Results from this study provide baseline metrics for developing a glass fracture criterion to predict module damage during hail impact events, which in turn allows for analysis of design features that may affect damage susceptibility.

Plenoptic background-oriented schlieren is a diagnostictechnique that enables the measure-ment of three-dimensional refractive gradients by a combination of background-oriented schlieren and a plenoptic light field camera. This plenoptic camera is a modification of a traditional camera via the insertion of an array of microlenses between the imaging lens and digital sensor. This allows the collection of both spatial and angular information on the incoming light rays and therefore provides three-dimensional information about the imaged scene. Background-oriented schlieren requires a relatively simple experimental configurationincludingonlyacameraviewing a patterned background through the density field of interest. By using a plenoptic camera to capture background-oriented schlieren images the optical distortion created by density gradients in three dimensions can be measured. This chapter is intended to review critical developments in plenoptic background-oriented schlieren imaging and provide an outlook for future applications of this measurement technique.

We present a novel design of a III-V-on-silicon heterogeneously integrated tunable ring laser, achieving >80 nanometers of tuning bandwidth, the widest conceived using only two rings, fostering many applications such as spectroscopy and beam steering.

Spatial navigation involves the formation of coherent representations of a map-like space, while simultaneously tracking current location in a primarily unsupervised manner. Despite a plethora of neurophysiological experiments revealing spatially-tuned neurons across the mammalian neocortex and subcortical structures, it remains unclear how such representations are acquired in the absence of explicit allocentric targets. Drawing upon the concept of predictive learning, we utilize a biologically plausible learning rule which utilizes sensory-driven observations with internally-driven expectations and learns through a contrastive manner to better predict sensory information. The local and online nature of this approach is ideal for deployment to neuromorphic hardware for edge-applications. We implement this learning rule in a network with the feedforward and feedback pathways known to be necessary for spatial navigation. After training, we find that the receptive fields of the modeled units resemble experimental findings, with allocentric and egocentric representations in the expected order along processing streams. These findings illustrate how a local and self-supervised learning method for predicting sensory information can extract latent structure from the environment.

I experimentally investigated and modeled a proposed frequency-domain method for detecting and tracking cislunar spacecraft and near-earth asteroids using heliostat fields at night. Unlike imaging, which detects spacecraft and asteroids by their streak in star-fixed long-exposure photographs, the proposed detection method oscillates the orientation of heliostats concentrating light from the stellar field and measures the light's photocurrent power spectrum at sub-milliHertz resolution. If heliostat oscillation repetitively traces out a closed loop fixed to the stars, light from spacecraft or asteroids moving along that loop produce photocurrent at a frequency slightly shifted from starlight. The frequency shift is proportional to the spacecraft or asteroid's apparent angular rate relative to sidereal. Relative phase corresponds to relative angular position, enabling tracking. Since heliostats are inexpensive compared to an astronomical observatory and otherwise unused at night, the proposed method may cost-effectively augment observatory systems such as NASA's Asteroid Terrestrial-impact Last Alert System (ATLAS).

The Explosive Destruction System (EDS) V31 containment vessel was procured by the US Army Recovered Chemical Materiel Directorate (RCMD) as a third-generation system used to destroy chemical munitions. It is the fifth individual 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 this 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, distributed evenly inside the vessel (totaling 19.2 lb of C-4, or 24 lb TNT equivalent). The ASME certification was based exclusively on the analytical predictions because the data required for certification cannot be obtained through testing. Strains through the thickness of the wall and on the inside surface of the cylinder were required and could only be obtained through analysis. Strain gages were placed on the outside of the vessel in three locations and the displacement of the door was measured using a Photonic Doppler Velocimetry (PDV) system. These measured values are compared to analytical predictions to help ensure the accuracy of the predicted strains and displacements throughout the rest of the model.

There has always been a desire to port high-fidelity reactive flow models from one code to another. For example, the AWE reactive burn model known as CREST has been or is being implemented in several of the U.S. Department of Energy hydrocodes. Those involved with reactive burn model implementation recognize the challenges immediately, e.g., Eulerian versus Lagrangian frameworks, the form of the equation of state, the closure relations, etc. In this work, we report the development of the CREST reactive burn model in CTH, a multidimensional, multi-material hydrocode developed by Sandia National Laboratories, following an earlier implementation shown at the last International Detonation Symposium. Results include code-to-code comparisons between CTH and the AWE hydrocode PERUSE, focusing on the simulated particle velocity histories during a shock-to-detonation transition, and corresponding to previous gas gun impact experiments as well as new model verification studies. Lessons learned are provided, including discussions of the numerical accuracy, in addition to the role of artificial viscosity and artificial viscous work. Finally, simulation results are shown to compare the Snowplough versus P-Alpha porosity model options.

Conceptual models of smectite hydration include planar (flat) clay layers that undergo stepwise expansion as successive monolayers of water molecules fill the interlayer regions. However, X-ray diffraction (XRD) studies indicate the presence of interstratified hydration states, suggesting non-uniform interlayer hydration in smectites. Additionally, recent theoretical studies have shown that clay layers can adopt bent configurations over nanometer-scale lateral dimensions with minimal effect on mechanical properties. Therefore, in this study we used molecular simulations to evaluate structural properties and water adsorption isotherms for montmorillonite models composed of bent clay layers in mixed hydration states. Results are compared with models consisting of planar clay layers with interstratified hydration states (e.g. 1W–2W). The small degree of bending in these models (up to 1.5 Å of vertical displacement over a 1.3 nm lateral dimension) had little or no effect on bond lengths and angle distributions within the clay layers. Except for models that included dry states, porosities and simulated water adsorption isotherms were nearly identical for bent or flat clay layers with the same averaged layer spacing. Similar agreement was seen with Na- and Ca-exchanged clays. While the small bent models did not retain their configurations during unconstrained molecular dynamics simulation with flexible clay layers, we show that bent structures are stable at much larger length scales by simulating a 41.6×7.1 nm2 system that included dehydrated and hydrated regions in the same interlayer.

This letter presents a one-step predictive run-to-run controller (R2R-MPC) for the automation of mechanical serial sectioning (MSS), a destructive material analysis process. To address the inherent uncertainty and disturbances in the MSS process, a robust closed-loop approach is presented. The robust R2R-MPC models the uncertainty of the MSS process using a linear differential inclusion. As an analytical model of the MSS process is unavailable, the differential inclusion is identified from historical data. The R2R-MPC is posed as an optimization problem that computes incremental changes to the control input which minimize the worst-case material removal errors. This optimization-based controller is combined with a run-to-run controller to provide integral action that rejects constant disturbances and tracks constant reference removal rates. To demonstrate the efficacy of our robust R2R-MPC, we present simulation results which compare the presented controller with a conventional non-robust R2R.

Neuromorphic computing uses brain-inspired concepts to accelerate and efficiently execute a wide range of applications, such as mimicking biological circuits, solving NPhard optimization problems and accelerating machine learning at the edge. In particular, neuromorphic architectures to efficiently execute Spiking Neural Networks (SNNs) have gained popularity. SNNs extend artificial neural networks (ANNs) by encoding information in time as either rates or delays between spiking events, shared between neurons via their weighted connections. SNN-based platforms are event-driven, resulting in naturally sparse, noise-tolerant and power-efficient computation. In this tutorial, we present the state-of-the-art in scalable digital and analog spiking neuromorphic system architectures, and discuss current research trends within the neuromorphic architecture field at the system level. We further introduce our SANA-FE tool for Simulation of Advanced Neuromorphic Architectures for Fast Exploration, which has been developed as part of a collaboration between the University of Texas at Austin and Sandia National Laboratories. SANA-FE allows for modeling and performance-power prediction of different spiking hardware architectures executing SNN applications to support rapid, early system-level design-space exploration, hardware-aware application development and system architecture co-design. The tutorial includes a hands-on component in which SANA-FE's capabilities are demonstrated and used to perform system design and application mapping case studies.