Publications

More than 90% of utility-scale photovoltaic (PV) power plants in the US use single-axis trackers (SATs) due to their potential for substantially higher power production over fixed-array systems. However, they are subject to software misconfigurations and mechanical failures, leading to suboptimal tracking accuracy. If failures are left undetected, the overall power yield of the PV power plant is reduced significantly. Robust detection and diagnosis of SAT faults is needed to minimize downtime and ensure continuous and efficient operation. This work presents analytic tools based on machine learning to detect deviations in SAT tracking performance and classify SAT faults.

Interim dry storage of spent nuclear fuel involves storing the fuel in welded stainless-steel canisters. Under certain conditions, the canisters could be subjected to environments that may promote stress corrosion cracking leading to a risk of breach and release of aerosol-sized particulate from the interior of the canister to the external environment through the crack. Research is currently under way by several laboratories to better understand the formation and propagation of stress corrosion cracks, however little work has been done to quantitatively assess the potential aerosol release. The purpose of the present work is to introduce a reliable generic numerical model for prediction of aerosol transport, deposition, and plugging in leak paths similar to stress corrosion cracks, while accounting for potential plugging from particle deposition. The model is dynamic (changing leak path geometry due to plugging) and it relies on the numerical solution of the aerosol transport equation in one dimension using finite differences. The model’s capabilities were also incorporated into a Graphical User Interface (GUI) that was developed to enhance user accessibility. Model validation efforts presented in this paper compare the model’s predictions with recent experimental data from Sandia National Laboratories (SNL) and results available in literature. We expect this model to improve the accuracy of consequence assessments and reduce the uncertainty of radiological consequence estimations in the remote event of a through-wall breach in dry cask storage systems.

The diesel-piloted dual-fuel compression ignition combustion strategy is well-suited to accelerate the decarbonization of transportation by adopting hydrogen as a renewable energy carrier into the existing internal combustion engine with minimal engine modifications. Despite the simplicity of engine modification, many questions remain unanswered regarding the optimal pilot injection strategy for reliable ignition with minimum pilot fuel consumption. The present study uses a single-cylinder heavy-duty optical engine to explore the phenomenology and underlying mechanisms governing the pilot fuel ignition and the subsequent combustion of a premixed hydrogen-air charge. The engine is operated in a dual-fuel mode with hydrogen premixed into the engine intake charge with a direct pilot injection of n-heptane as a diesel pilot fuel surrogate. Optical diagnostics used to visualize in-cylinder combustion phenomena include high-speed IR imaging of the pilot fuel spray evolution as well as high-speed HCHO* and OH* chemiluminescence as indicators of low-temperature and high-temperature heat release, respectively. Three pilot injection strategies are compared to explore the effects of pilot fuel mass, injection pressure, and injection duration on the probability and repeatability of successful ignition. The thermodynamic and imaging data analysis supported by zero-dimensional chemical kinetics simulations revealed a complex interplay between the physical and chemical processes governing the pilot fuel ignition process in a hydrogen containing charge. Hydrogen strongly inhibits the ignition of pilot fuel mixtures and therefore requires longer injection duration to create zones with sufficiently high pilot fuel concentration for successful ignition. Results show that ignition typically tends to rely on stochastic pockets with high pilot fuel concentration, which results in poor repeatability of combustion and frequent misfiring. This work has improved the understanding on how the unique chemical properties of hydrogen pose a challenge for maximization of hydrogen's energy share in hydrogen dual-fuel engines and highlights a potential mitigation pathway.

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.

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.

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.

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.

Neuromorphic computing platforms hold the promise to dramatically reduce power requirements for calculations that are computationally intensive. One such application space is scientific machine learning (SciML). Techniques in this space use neural networks to approximate solutions of scientific problems. For instance, the popular physics-informed neural network (PINN) approximates the solution to a partial differential equation by using a trained feed-forward neural network, and injecting the knowledge of the physics through the loss function. Recent efforts have demonstrated how to convert a trained PINN to a spiking network architecture. In this work, we discuss our approach to quantization and implementation required to migrate these spiking PINNs to Intel's Loihi 2 neuromorphic hardware. We explore the effect of quantization on the model accuracy, as well as the energy and throughput characteristics of the implementation. It is our intent that this serve as a starting point for additional SciML implementations on neuromorphic hardware.

With the advent of grid-forming inverters, the question of how they behave during and immediately following short-circuit faults has become of major importance in the choice of control strategy and inverter design. This problem has greatly stimulated activity in the study of power electronics and their effects on large-scale systems, as evidenced by the formation of the Universal Interoperability for Grid-Forming Inverters (UNIFI) Consortium. This paper investigates a particular droop-controlled grid-forming inverter model called REGFM-Al, recently ap-proved for use in system-wide studies by the WECC Modeling and Validation Subcommittee. We show that following 3-phase faults, inverters that behave according to the REGFM-Al specification may inject significant post-fault active power transients. We describe the mechanism by which this occurs and propose a state reset strategy for mitigating the amplitude of these transients. To illustrate the main concepts, we present simulation results for a two-area test system augmented with a 200MVA solar photovoltaic power plant represented as a single equivalent grid-forming inverter. The analysis described in this paper was performed in MATLAB using the Power and Energy Storage Systems Toolbox (PSTess). To validate the model implementation in PSTess, we compared simulation results against GE Vernova's Positive Sequence Load Flow (PSLF) software.

The primary goal of any laboratory test is to expose the unit-under-test to conservative realistic representations of a field environment. Satisfying this objective is not always straightforward due to laboratory equipment constraints. For vibration and shock tests performed on shakers over-testing and unrealistic failures can result because the control is a base acceleration and mechanical shakers have nearly infinite impedance. Force limiting and response limiting are relatively standard practices to reduce over-test risks in random-vibration testing. Shaker controller software generally has response limiting as a built-in capability and it is done without much user intervention since vibration control is a closed loop process. Limiting in shaker shocks is done for the same reasons, but because the duration of a shock is only a few milliseconds, limiting is a pre-planned user in the loop process. Shaker shock response limiting has been used for at least 30 years at Sandia National Laboratories, but it seems to be little known or used in industry. This objective of this paper is to re-introduce response limiting for shaker shocks to the aerospace community. The process is demonstrated on the BARBECUE testbed.

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).

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.

Operation and control of a galvanically isolated three-phase AC-AC converter for solid state transformer applications is described. The converter regulates bidirectional power transfer by phase shifting voltages applied on either side of a high-frequency transformer. The circuit structure and control system are symmetrical around the transformer. Each side operates independently, enabling conversion between AC systems with differing voltage magnitude, phase angle, and frequency. This is achieved in a single conversion stage with low component count and high efficiency. The modulation strategy is discussed in detail and expressions describing the relationship between phase shift and power transfer are presented. Converter operation is demonstrated in a 3 kW hardware prototype.

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.

In this work, we evaluate the usefulness of nonsmooth basis functions for representing the periodic response of a nonlinear system subject to contact/impact behavior. As with sine and cosine basis functions for classical Fourier series, which have C∞ smoothness, nonsmooth counterparts with C0 smoothness are defined to develop a nonsmooth functional representation of the solution. Some properties of these basis functions are outlined, such as periodicity, derivatives, and orthogonality, which are useful for functional series applied via the Galerkin method. Least-squares fits of the classical Fourier series and nonsmooth basis functions are presented and compared using goodness-of-fit metrics for time histories from vibro-impact systems with varying contact stiffnesses. This formulation has the potential to significantly reduce the computational cost of harmonic balance solvers for nonsmooth dynamical systems. Rather than requiring many harmonics to capture a system response using classical, smooth Fourier terms, the frequency domain discretization could be captured by a combination of a finite Fourier series supplemented with nonsmooth basis functions to improve convergence of the solution for contact-impact problems.

Additive manufacturing has ushered in a new paradigm of bottom-up materials-by-design of spatially non-uniform materials. Functionally graded materials have locally tailored compositions to provide optimized global properties and performance. In this letter, we propose an opportunity for the application of graded magnetic materials as lens elements for charged particle optics. A Hiperco50/Hymu80 (FeCo-2 V/Fe-80Ni-5Mo) graded magnetic alloy was successfully additively manufactured via Laser Directed Energy Deposition with spatially varying magnetic properties. The compositional gradient is then applied using computational simulations to demonstrate how a tailored material can enhance the magnetic performance of a critical, image-forming component of a transmission electron microscope.

Resonant plate shock testing techniques have been used for mechanical shock testing at Sandia for several decades. A mechanical shock qualification test is often done by performing three separate uniaxial tests on a resonant plate to simulate one shock event. Multi-axis mechanical shock activities, in which shock specifications are simultaneously met in different directions during a single shock test event performed in the lab, are not always repeatable and greatly depend on the fixture used during testing. This chapter provides insights into various designs of a concept fixture that includes both resonant plate and angle bracket used for multi-axis shock testing from a modeling and simulation point of view based on the results of finite element modal analysis. Initial model validation and testing performed show substantial excitation of the system under test as the fundamental modes drive the response in all three directions. The response also shows that higher order modes are influencing the system, the axial and transverse response are highly coupled, and tunability is difficult to achieve. By varying the material properties, changing thicknesses, adding masses, and moving the location of the fixture on the resonant plate, the response can be changed significantly. The goal of this work is to identify the parameters that have the greatest influence on the response of the system when using the angle bracket fixture for a mechanical shock test for the intent of tunability of the system.

Blind geothermal systems are believed to be common in the Basin and Range province and represent an underutilized source of renewable green energy. Their discovery has historically been by chance but more methodological strategies for exploration of these resources are being developed. One characteristic of blind systems is that they are often overlain by near-surface zones of low-resistivity caused by alteration of the overlying sediments to swelling clays. These zones can be imaged by resistivity-based geophysical techniques to facilitate their discovery and characterization. Here we present a side-by-side comparison of resistivity models produced from helicopter transient electromagnetic (HTEM) and ground-based broadband magnetotelluric (MT) surveys over a previously discovered blind geothermal system with measured shallow temperatures of ~100°C in East Hawthorne, NV. The HTEM and MT data were collected as part of the BRIDGE project, an initiative for improving methodologies for discovering blind geothermal systems. HTEM data were collected and modelled along profiles, and the results suggest the method can resolve the resistivity structure 300 - 500 m deep. A 61-station MT survey was collected on an irregular grid with ~800 m station spacing and modelled in 3D on a rotated mesh aligned with HTEM flight directions. Resistivity models are compared with results from potential fields datasets, shallow temperature surveys, and available temperature gradient data in the area of interest. We find that the superior resolution of the HTEM can reveal near-surface details often missed by MT. However, MT is sensitive to several km deep, can resolve 3D structures, and is thus better suited for single-prospect characterization. We conclude that HTEM is a more practical subregional prospecting tool than is MT, because it is highly scalable and can rapidly discover shallow zones of low resistivity that may indicate the presence of a blind geothermal system. Other factors such as land access and ground disturbance considerations may also be decisive in choosing the best method for a particular prospect. Resistivity methods in general cannot fully characterize the structural setting of a geothermal system, and so we used potential fields and other datasets to guide the creation of a diagrammatic structural model at East Hawthorne.

The development of multi-axis force sensing ca-pabilities in elastomeric materials has enabled new types of human motion measurement with many potential applications. In this work, we present a new soft insole that enables mobile measurement of ground reaction forces (GRFs) outside of a lab-oratory setting. This insole is based on hybrid shear and normal force detecting (SAND) tactile elements (taxels) consisting of optical sensors optimized for shear sensing and piezoresistive pressure sensors dedicated to normal force measurement. We develop polynomial regression and deep neural network (DNN) GRF prediction models and compare their performance to ground-truth force plate data during two walking experiments. Utilizing a 4-layer DNN, we demonstrate accurate prediction of the anterior-posterior (AP), medial-lateral (ML) and vertical components of the GRF with normalized mean absolute errors (NMAE) of <5.1 %, 4.1 %, and 4.5%, respectively. We also demonstrate the durability of the hybrid SAND insole construction through more than 20,000 cycles of use.

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.

Although fire events inside nuclear power plants (NPPs) are infrequent, when they occur, they can affect the safe operation of the plant if there is not sufficient protection addressing the risk. As mitigation for fire events, NPPs have comprehensive fire protection systems intended to reduce the likelihood of a fire event and the associated consequences. An electrical arcing fault involving components made of aluminum is one such hazard that could lead to a significant consequence. Because the original evaluation of high-energy arcing faults (HEAF) was performed on components made of copper, there is an interest in understanding the effects of aluminum in these incidents. The nuclear regulatory commission (NRC) has led a series of HEAF experiments at a facility near Philadelphia, PA, in conjunction with the national institute of standards and technology (NIST), European and Japanese partners, and Sandia National Laboratories (SNL). To capture a range of different HEAF events, Sandia has provided high-speed visible and IR videography from multiple angles during this series of experiments. One of the data products provided by Sandia is the combination and synchronization of infrared and visible data from the multiple cameras used in the tests. This multispectral fusion of information (visible, MWIR, and LWIR) allows the customer to visualize the tests and understand when different events happen in the 2 to 4 second duration of a test. The presentation will dissect three experiments and describe the different events occurring during their duration. The presentation will compare the behavior of equipment that contains aluminum components versus the ones containing copper or steel. Finally, data from a switchgear experiment will be presented to complement the bus duct data.

Single-axis solar trackers are typically simulated under the assumption that all modules on a given section of torque tube are at a single orientation. In reality, various mechanical effects can cause twisting along the torque tube length, creating variation in module orientation along the row. Simulation of the impact of this on photovoltaic system performance reveals that the performance loss resulting from torque tube twisting is significant at twists as small as fractions of a degree per module. The magnitude of the loss depends strongly on the design of the photovoltaic module, but does not vary significantly across climates. Additionally, simple tracker control setting tweaks were found to substantially reduce the loss for certain types of twist.

Anelastic strain recovery, the process of measuring the time dependent recovered strain after a core is cut at depth was utilized to make a measure of the in-situ properties stresses at depth at the FORGE (Frontier Observatory for Research in Geothermal Energy) site in Milford Utah. Core was collected from a region of well 16B at approximately 4860-4870 ft. Core was instrumented with strain gages within 10 hours of the core being cut. The relaxation of the cores was measured for approximately one month, and the results analyzed, which showed that the principal stresses were slightly off vertical, and magnitudes are close to equal.

A Marx generator module from the decommissioned RITS pulsed power machine from Sandia National Labs was modified to operate in an existing setup at Texas Tech University. This will ultimately be used as a testbed for laser triggered gas switching. The existing experimental setup at Texas Tech University consists of a large Marx tank, an oil-filled coaxial pulse forming line, an adjustable peaking gap, and load section along with various diagnostics. The setup was previously operated at a lower voltage than the new experiment, so electrostatic modeling was done to ensure viability and drive needed modifications. The oil tank will house the modified RITS Marx. This Marx contains half as many stages as the original RITS module and has an expected output of 1 MV. A trigger Marx generator consisting of 8 stages has been fabricated to trigger the RITS Marx. Charging and triggering of both Marx generators will be controlled through a fiber optic network. The output from the modified RITS Marx will be used to charge the oil-filled coaxial line acting as a low impedance pulse forming line (PFL). Once charged, the self-breaking peaking gap will close, allowing the compressed pulse to be released into the load section. For testing of the Marx module and PFL, a match 10 Ω water load was fabricated. The output pulsewidth is 55 nsec. Diagnostics include two capacitive voltage probes on either side of the peaking gap, a quarter-turn Rogowski coil for load current measurement, and a Pearson coil for calibrations purposes.

Before residential photovoltaic (PV) systems are interconnected with the grid, various planning and impact studies are conducted on detailed models of the system to ensure safety and reliability are maintained. However, these model-based analyses can be time-consuming and error-prone, representing a potential bottleneck as the pace of PV installations accelerates. Data-driven tools and analyses provide an alternate pathway to supplement or replace their model-based counterparts. In this article, a data-driven algorithm is presented for assessing the thermal limitations of PV interconnections. Using input data from residential smart meters, and without any grid models or topology information, the algorithm can determine the nameplate capacity of the service transformer supplying those customers. The algorithm was tested on multiple datasets and predicted service transformer capacity with >98% accuracy, regardless of existing PV installations. This algorithm has various applications from model-free thermal impact analysis for hosting capacity studies to error detection and calibration of existing grid models.

In general, multiple-input/multiple-output (MIMO) vibration testing utilizes a response-controlled test methodology where specifications are in the form of response quantities at various locations distributed on the device under test (DUT). There are some advantages to this approach, namely that DUT response could be measured in some field environment and directly used as MIMO specifications for subsequent MIMO vibration tests on similar DUTs. However, in some cases it may be advantageous to control the MIMO vibration test at the inputs rather than the responses. One such case is free-flight environments, where the DUT is unconstrained, and all loads come from aerodynamic pressures. In this case, the force-controlled test method is much more robust to system changes such as unit-to-unit variability as compared to a response-controlled test method. This could make force-controlled MIMO test specifications more generalizable and easier to derive. This is exactly akin to transfer path analysis, where pseudo-forces are applicable in special circumstances. This paper will explore the force-controlled test concept and demonstrate it with a numerical example, comparing performance under various conditions vs. the traditional response-controlled test method.

Abstract not provided.

Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices and emerged as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs). However, to extend SNNs to a Federated Learning (FL) setting involving collaborative model training, the communication between the local devices and the remote server remains the bottleneck, which is often restricted and costly. In this paper, we first explore the inherent robustness of SNNs under noisy communication in FL. Building upon this foundation, we propose a novel Federated Learning with Top-κ Sparsification (FLTS) algorithm to reduce the bandwidth usage for FL training. We discover that the proposed scheme with SNNs allows more bandwidth savings compared to ANNs without impacting the model's accuracy. Additionally, the number of parameters to be communicated can be reduced to as low as 6% of the size of the original model. We further improve the communication efficiency by enabling dynamic parameter compression during model training. Extensive experiment results demonstrate that our proposed algorithms significantly outperform the baselines in terms of communication cost and model accuracy and are promising for practical network-efficient FL with SNNs.

This experimental work investigates the flow field associated with a tandem expansion-compression geometry in the Sandia Trisonic Wind Tunnel. PIV measurements of the boundary layer before the expansion characterized properties of the incoming boundary layer. Schlieren and oil-flow experiments were conducted at Mach 1.5 and 2 for a range of stagnation pressures. Further PIV experiments were conducted across the expansion and compression corners to observe the post-expansion changes in the boundary layer and its influence on the shock/boundary-layer interaction at the compression corner. Velocity profiles qualitatively matched well with RANS simulations and showed rapid growth of the boundary layer following the expansion, tapering off to a slower rate of growth with distance. Turbulence intensity, exemplified by the streamwise component of turbulent normal stress, diminished substantially after the expansion corner leading to the belief that relaminarization processes were occurring. Comparison with analysis from the literature suggests that the distortion of the boundary layer due to expansion leads to a separation length 30% larger than for an equilibrium turbulent boundary layer.

Fault location, isolation, and service restoration of a self-healing, self-Assembling microgrid operating off-grid from distributed inverter-based resources (IBRs) can be a unique challenge because of the fault current limitations and uncertainties regarding which sources are operational at any given time. The situation can become even more challenging if data sharing between the various microgrid controllers, relays, and sources is not available. This paper presents an innovative robust partitioning approach, which is used as part of a larger self-Assembling microgrid concept utilizing local measurements only. This robust partitioning approach splits a microgrid into sub-microgrids to isolate the fault to just one of the sub-microgrids, allowing the others to continue normal operation. A case study is implemented in the IEEE 123-bus distribution test system in Simulink to show the effectiveness of this approach. The results indicate that including the robust partitions leads to less loss of load and shorter overall restoration times.

Multiple scattering is a common phenomenon in acoustic media that arises from the interaction of the acoustic field with a network of scatterers. This mechanism is dominant in problems such as the design and simulation of acoustic metamaterial structures often used to achieve acoustic control for sound isolation, and remote sensing. In this study, we present a physics-informed neural network (PINN) capable of simulating the propagation of acoustic waves in an infinite domain in the presence of multiple rigid scatterers. This approach integrates a deep neural network architecture with the mathematical description of the physical problem in order to obtain predictions of the acoustic field that are consistent with both governing equations and boundary conditions. The predictions from the PINN are compared with those from a commercial finite element software model in order to assess the performance of the method.

This paper develops a new domain-decomposition method for solving the KKT system with heat-equation constraints.

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.

Disposal of commercial spent nuclear fuel in a geologic repository is studied. In situ heater experiments in underground research laboratories provide a realistic representation of subsurface behavior under disposal conditions. This study describes process model development and modeling analysis for a full-scale heater experiment in opalinus clay host rock. The results of thermal-hydrology simulation, solving coupled nonisothermal multiphase flow, and comparison with experimental data are presented. The modeling results closely match the experimental data.

We present optoelectronic characterization of ntype silicon pixels with a suite of plasmonic designs intended to generate and detect electron-hole pairs from incident 1550 nm photons.

Deep neural networks (DNNs) achieve state-of-the-art performance in video anomaly detection. However, the usage of DNNs is limited in practice due to their computational overhead, generally requiring significant resources and specialized hardware. Further, despite recent progress, current evaluation criteria of video anomaly detection algorithms are flawed, preventing meaningful comparisons among algorithms. In response to these challenges, we propose (1) a compression-based technique referred to as Spatio-Temporal N-Gram Prediction by Partial Matching (STNG PPM) and (2) simple modifications to current evaluation criteria for improved interpretation and broader applicability across algorithms. STNG PMM does not require specialized hardware, has few parameters to tune, and is competitive with DNNs on multiple benchmark data sets in video anomaly detection.

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.

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.

Hail poses a significant threat to photovoltaic (PV) systems due to the potential for both cell and glass cracking. This work experimentally investigates hail-related failures in Glass/Backsheet and Glass/Glass PV modules with varying ice ball diameters and velocities. Post-impact Electroluminescence (EL) imaging revealed the damage extent and location, while high-speed Digital Image Correlation (DIC) measured the out-of-plane module displacements. The findings indicate that impacts of 20 J or less result in negligible damage to the modules tested. The thinner glass in Glass/Glass modules cracked at lower impact energies (-25 J) than Glass/Backsheet modules (-40 J). Furthermore, both module types showed cell and glass cracking at lower energies when impacted at the module's edges compared to central impacts. At the time of presentation, we will use DIC to determine if out-of-plane displacements are responsible for the impact location discrepancy and provide more insights into the mechanical response of hail impacted modules. This study provides essential insights into the correlation between impact energy, impact location, displacements, and resulting damage. The findings may inform critical decisions regarding module type, site selection, and module design to contribute to more reliable PV systems.

Uncertainty quantification (UQ) plays a vital role in addressing the challenges and limitations encountered in full-waveform inversion (FWI). Most UQ methods require parameter sampling which requires many forward and adjoint solves. This often results in very high computational overhead compared to traditional FWI, which hinders the practicality of the UQ for FWI. In this work, we develop an efficient UQ-FWI framework based on unsupervised variational autoencoder (VAE) to assess the uncertainty of single and multi-parameter FWI. The inversion operator is modeled using an encoder-decoder network. The input to the network is seismic shot gathers and the output are samples (distribution) of model parameters. We then use these samples to estimate the mean and standard deviation of each parameter population, which provide insights on the uncertainty in the inversion process. To speed up the UQ process, we carried out the reconstruction in an unsupervised learning approach. Moreover, we physics-constrained the network by injecting the FWI gradients during the backpropagation process, leading to better reconstruction. The computational cost of the proposed approach is comparable to the traditional autoencoder full-waveform inversion (AE-FWI), which is encouraging to be used to get further insight on the quality of the inversion. We apply this idea for synthetic data to show its potential in assessing uncertainty in multi-parameter FWI.

Frequency stability issues are common in low-inertia microgrids due to the dominance of power electronics converter-based resources and comparatively low levels of inertia of synchronous generation. While many methods have been proposed for frequency support in these systems, it is challenging to ensure both stability and an adequate level of support without directly modeling the system. Perturbation-based extremum-seeking control (PESC) is a model-free adaptive control strategy that optimally sets the system's performance measure without requiring a mathematical system model. However, PESC offers poor transient performance due to design limitations, such as an averaging theory. Some modifications have been made to address these limitations; nevertheless, the modified design of PESC operates more as a model-based control. The Quasi-Newton method is a popular class of numerical optimizers attributed with a design procedure similar to model-free control that uses the gradient and an approximate inverse Hessian of the performance measure to run an optimization loop. This paper presents the design and compares the performance of the Quasi-Newton method and a model-based PESC for frequency support of microgrids. The simulation results illustrate the comparable performance of both control schemes and show the model-free control capability of the numerical optimization method for a class of nonlinear dynamic systems.

We demonstrate evanescently coupled waveguide integrated silicon photonic avalanche photodiodes designed for single photon detection for quantum applications. Simulation, high responsivity, and record low dark currents for evanescently coupled devices are presented.

In low inertia grids, significant frequency deviations can occur as a result of changes in power (load, generation, etc.), These deviations may activate various protection schemes designed to safeguard the system, potentially leading to blackouts. Therefore, assessing the frequency stability of the power system is crucial. The Frequency Security Index (FSI) serves as a metric for evaluating system stability. However, computing the FSI for a specific load change necessitates actual load changes on the system, which is often impractical. This paper introduces a method for calculating the FSI without requiring load changes for all values. A mathematical expression for the FSI is derived, which uses the values of microgrid parameters (such as inertia and damping constant) to compute the FSI for any load change. Subsequently, the parameters that most significantly affect the FSI are identified. Then, the paper introduces a Moving Horizon Estimation (MHE)-based parameter estimation approach, which leverages small perturbations from an energy storage system to estimate the most influential parameters for the FSI. The results show that the FSI calculation with the estimated parameters is more accurate (compared to COI averaged parameters), enabling a more effective state of health monitoring of the microgrid.

Underground caverns in salt formations are promising geologic features to store hydrogen (H2) because of salt's extremely low permeability and self-healing behavior.Successful salt-cavern H2 storage schemes must maximize the efficiency of cyclic injection-production while minimizing H2 loss through adjacent damaged salt.The salt cavern storage community, however, has not fully understood the geomechanical behaviors of salt rocks driven by quick operation cycles of H2 injection-production, which may significantly impact the cost-effective storage-recovery performance.Our field-scale generic model captures the impact of combined drag and back stressing on the salt creep behavior corresponding to cycles of compression and extension, which may lead to substantial loss of cavern volumes over time and diminish the cavern performance for H2 storage.Our preliminary findings address that it is essential to develop a new salt constitutive model based on geomechanical tests of site-specific salt rock to probe the cyclic behaviors of salt both beneath and above the dilatancy boundary, including reverse (inverse transient) creep, the Bauschinger effect and fatigue.

In this work, the frequency response of a simplified shaft-bearing assembly is studied using numerical continuation. Roller-bearing clearances give rise to contact behavior in the system, and past research has focused on the nonlinear normal modes of the system and its response to shock-type loads. A harmonic balance method (HBM) solver is applied instead of a time integration solver, and numerical continuation is used to map out the system’s solution branches in response to a harmonic excitation. Stability analysis is used to understand the bifurcation behavior and possibly identify numerical or system-inherent anomalies seen in past research. Continuation is also performed with respect to the forcing magnitude, resulting in what are known as S-curves, in an effort to detect isolated solution branches in the system response.

Evaluation of the hydrogen storage capacity of porous rocks is crucial for underground hydrogen storage. Using 1H nuclear magnetic resonance (NMR) spectroscopy, we successfully characterized the hydrogen responses and identified storage mechanisms in Berea sandstone under varying water saturation. The results indicate that the injected hydrogen behaves as a free gas phase and is capable of occupying the empty pore volume regardless of the saturation state. No hysteresis was observed during injection and production cycles.

Our present electric power grid maximizes spinning inertia of fossil fuel generators (inherent energy storage) to meet stability and performance requirements. Our goal is to begin to investigate the replacement of the large spinning inertia of fossil fuel generators with energy storage systems (ESS) including information flow as a necessary part of the renewable energy sources (RES) and subject to certain criteria. General criteria metrics include: energy storage, information flow, estimation, communication links, central versus decentralized, etc. Our focus is on evaluating the Fisher Information Equivalency (FIE) metric as a multi-criteria trade-off cost function for the minimization of ESS options and information flow. This paper begins with a formal conceptual definition of an infinite bus. Then a simple example of a One Machine Infinite Bus (OMIB) system with a Unified Power Flow Controller (UPFC) to demonstrate the FIE-based approach to minimize the ESS. A second more detailed example of several spinning machines are included with representative power electronic and ESS for RES that are attached to the electric power grid. A simple trade-study begins to highlight requirements to support large penetration of RES. Keep in mind for a large scale high penetration of RES will require large investments in ESS which we want to minimize.

The impact of more extreme climate conditions under global warming on soil organic carbon (SOC) dynamics remains unquantified. Here we estimate the response of SOC to climate extreme shifts under 1.5 °C warming by combining a space-for-time substitution approach and global SOC measurements (0–30 cm soil). Most extremes (22 out of 33 assessed extreme types) exacerbate SOC loss under warming globally, but their effects vary among ecosystems. Only decreasing duration of cold spells exerts consistent positive effects, and increasing extreme wet days exerts negative effects in all ecosystems. Temperate grasslands and croplands negatively respond to most extremes, while positive responses are dominant in temperate and boreal forests and deserts. In tundra, 21 extremes show neutral effects, but 11 extremes show negative effects with stronger magnitude than in other ecosystems. Our results reveal distinct, biome-specific effects of climate extremes on SOC dynamics, promoting more reliable SOC projection under climate change.

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.