Publications

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.

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.

Polymeric materials are commonplace in the natural gas infrastructure as distribution pipes, coatings, seals, and gaskets. Under the auspices of the U.S. Department of Energy HyBlend program, one of the means to reduce greenhouse gas emissions is with replacing natural gas, either partially or completely, with hydrogen. This approach makes it imperative that we conduct near-term and long-term materials compatibility research in these relevant environments. Insights into the effects of hydrogen and hydrogen gas blends on polymer integrity can be gained through both ex-situ and in-situ analytical methods. Our work represented here highlights a study of the behavior of pipeline polyethylene (PE) materials, including HDPE (Dow 2490 and GDB50) and MDPE (Ineos and legacy Dupont Aldyl A), when exposed to hydrogen by means of in-situ X-ray scattering and ex-situ Raman spectroscopy techniques. These methods complement each other in analyzing polymer microstructure. Data collected revealed that the aforementioned polymers did not show significant changes in crystallinity or morphology under the exposure conditions tested. These studies help establish techniques to study real-time effects of hydrogen gas on polymer structure and chemistry, which is directly related to pipeline mechanical strength and longevity of service.

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.

Rotational testbeds are ubiquitous in the test and evaluation of inertial sensors and systems. However, the use of rotational testbeds is typically restricted to static states employed over long integration windows to allow for data aggregation. These methods ignore the transitions between states, and data aggregation masks potentially useful signals. In this paper, we discuss the development of modular equations for the description of the inertial inputs to a test sensor using any rotational testbed. Implementing our equations in software, specific force and angular rates can be computed from idealized table motion or measured encoder data. Results are presented using simulated data and measured data. The measured data was acquired using a three axis rate table and a MEMS IMU. The experimental results validate our model equations and demonstrate the benefits of modeling sensor inputs at the sensor rates to compensate for testbed errors.

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.

We consider the problem of decentralized control of reactive power provided by distributed energy resources for voltage support in the distribution grid. We assume that the reactance matrix of the grid is unknown and potentially time-varying. We present a decentralized adaptive controller in which the reactive power at each inverter is set using a potentially heterogeneous droop curve and analyze the stability and the steady-state error of the resulting system. The effectiveness of the controller is validated in simulations using a modified version of the IEEE 13-bus and a 8500-node test system.

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.

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.

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.

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.

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.

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.

In this paper, we develop a nested chi-squared likelihood ratio test for selecting among shrinkage-regularized covariance estimators for background modeling in hyperspectral imagery. Critical to many target and anomaly detection algorithms is the modeling and estimation of the underlying background signal present in the data. This is especially important in hyperspectral imagery, wherein the signals of interest often represent only a small fraction of the observed variance, for example when targets of interest are subpixel. This background is often modeled by a local or global multivariate Gaussian distribution, which necessitates estimating a covariance matrix. Maximum likelihood estimation of this matrix often overfits the available data, particularly in high dimensional settings such as hyperspectral imagery, yielding subpar detection results. Instead, shrinkage estimators are often used to regularize the estimate. Shrinkage estimators linearly combine the overfit covariance with an underfit shrinkage target, thereby producing a well-fit estimator. These estimators introduce a shrinkage parameter, which controls the relative weighting between the covariance and shrinkage target. There have been many proposed methods for setting this parameter, but comparing these methods and shrinkage values is often performed with a cross-validation procedure, which can be computationally expensive and highly sample inefficient. Drawing from Bayesian regression methods, we compute the degrees of freedom of a covariance estimate using eigenvalue thresholding and employ a nested chi-squared likelihood ratio test for comparing estimators. This likelihood ratio test requires no cross-validation procedure and enables direct comparison of different shrinkage estimates, which is computationally efficient.

For an Energy System to be truly equitable, it should provide affordable and reliable energy services to disadvantaged and underserved populations. Disadvantaged communities often face a combination of economic, social, health, and environmental burdens and may be geographically isolated (e.g., rural communities), which systematically limits their opportunity to fully participate in aspects of economic, social, and civic life.

The power grid, traditionally perceived as an independent physical network has undergone a significant transformation in recent years due to its integration with cyber communication networks and modern digital components. Cyber situations, including cyber-attacks and network anomalies, can directly affect the physical operation of the grid; therefore, studying this intricate relationship between the physical and cyber systems is pivotal for enhancing the resilience and security of modern power systems. In this digest, a novel Long Short-Term Memory (LSTM)-based Autoencoder (AE) model for cyber-physical data fusion and threat detection is proposed. The scenario under consideration includes the effective detection of a physical disturbance and a Denial-of-Service (DoS) attack, which obstructs control commands during the physical disturbance in the power grid. Detailed analysis and quantitative results regarding the LSTM-based AE model's training and evaluation phases is provided, which highlight its key operation features and benefits for guaranteeing security and resilience in the power grid.

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.

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.

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.

This paper proposes a planar aperiodic sparse transmitarray design for 6G with large inter-element spacings. Leveraging aperiodic arrays enhances beamforming and reduces feed network complexity, aligning with 6G demands. Numerical analysis confirms the feasibility and advantages of the design, paving the way for innovative 6G antenna array systems.

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.

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.

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.

Visualization of flow structures within post-detonation fireballs has been performed for benchmark validation of numerical simulations. Custom pressed PETN explosives with a 12-mm diameter hemispherical form factor were used to produce a spherically symmetric post-detonation flow with low soot yield. Hydroxyl-radical planar laser induce fluorescence (OH-PLIF) was employed to visualize the structure ranging from approximately 10μs to 35μs after shock breakout from the explosive pellet. Fireball simulations were performed using the HyBurn Computational Fluid Dynamics (CFD) package. Experimental OH-PLIF results were compared to synthetic OH-PLIF from post-processing of CFD simulations. From the comparison of experimental and synthetic OH-PLIF images, CFD is shown to replicate much of the flow structure observed in the experiments, revealing potential differences in turbulent length scales and OH kinetics. Results provide significant advancement in experimental resolution of these harsh turbulent combustion environments and validate physical models thereof.

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.

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.

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.

This paper presents the ongoing development of a chloride-based wireline tool designed to detect and quantify inflows from feed zones in geothermal wells. The tool aims to characterize stimulation events in EGS wells at Utah FORGE (Frontier Observatory for Research in Geothermal Energy) and other EGS sites. Successful development of the chloride tool would greatly improve production monitoring of the fractures and enable proactive prescription of additional stimulations over the life of the field, thus helping to improve EGS commercial feasibility. The recent developments of the chloride tool have focused on preparing for and conducting the field deployment at the Utah FORGE site. The field-scale tool assembly features a FORGE sensor package housing the Ion Selective Electrode (ISE), a pH electrode, and a reference electrode, as well as a Mitco PTS sensor package for secondary downhole measurements. A high-temperature logging tool has been developed and tested to capture and transmit data from the chemical sensors to the surface through a 7-conductor wireline cable. Alongside the development of the field-scale tool, flow experiments were carried out in the artificial well system at the Stanford Geothermal Lab. These experiments provided crucial insights into how the chemical tool responds to different variables, including the chloride concentration in the feed zone, its vertical positioning relative to the feed zone, and the presence of other chemical species in the feed zone fluid. The results highlight the tool's sensitivity to various parameters, underscoring the potential of using chloride concentration measurements as a method for inferring feed zone inflow rates in geothermal wells. The tool was successfully deployed at the Utah FORGE site using a wireline truck in the vertical well 58-32 and the directional production well 16B(78)-32.

A new Adaptive Mesh Refinement (AMR) keyword was added to the CTH¹ hydrocode developed at Sandia National Laboratories (SNL). The new indicator keyword, "ratec*ycle", allows the user to specify the minimum number of computational cycles before an AMR block is allowed to be un-refined. This option is designed to allow the analyst to control how quickly a block is un-refined to avoid introducing anomalous waves in their solution due to information propagating across mesh resolution changes. For example, in reactive flow simulations it is often desirable to accurately capture the expansion region behind the reaction front. The effect of this new option was examined using the XHVRB^{2, 3} model for XTX8003 to model the propagation of the detonation wave in explosives in small channels, and also for a simpler explosive model driving a steel case. The effect on computational cost as a function of this new option was also examined.

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.

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.

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).

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.

Gaseous hydrogen is known to embrittle most steels, including the steels used in natural gas pipelines. As injection of hydrogen into the existing natural gas infrastructure is considered globally by the pipeline industry, the structural integrity of pipelines transporting gaseous hydrogen must be investigated. Hydrogen Extremely Low Probability of Rupture (HELPR) is a publicly available and open-source probabilistic fatigue and fracture mechanics toolkit recently developed at Sandia National Laboratories. HELPR is intended to incorporate the influence of hydrogen into structural integrity assessments of natural gas transmission and distribution infrastructure. HELPR utilizes engineering models, such as those specified in ASME B31.12 and API 579, with relatively low computational costs to perform large sample ensembles, enabling estimation of performance distributions including low probability tail estimates. Leveraging the probabilistic capabilities built into HELPR, the sensitivity of fatigue and fracture calculations to specific modeling parameters on performance margins can be quantified. Through applying HELPR’s probabilistic capabilities to realistic scenarios, the impact of uncertainty in specific model parameter descriptions on performance margins, such as cycles to unstable crack growth or rupture in gaseous hydrogen, can be characterized; this same approach can then be used to assess the impact of reducing uncertainty sources on the resulting performance metrics, margins, and associated risks. A few industry-motivated scenarios are used to demonstrate this approach.

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.

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.

Over the past few years, advancements in closed-loop geothermal systems (CLGS), also called advanced geothermal systems (AGS), have sparked a renewed interest in these types of designs. CLGS have certain advantages over traditional and enhanced geothermal systems (EGS), including not requiring in-situ reservoir permeability, conservation of the circulating fluid, and allowing for different fluids, including working fluids directly driving a turbine at the surface. CLGS may be attractive in environments where water resources are limited, rock contaminants must be avoided, and stimulation treatments are not available (e.g., due to regulatory or technical reasons). Despite these advantages, CLGS have some challenges, including limited surface area for heat transfer and requiring long wellbores and laterals to obtain multi-MW output in conduction-only reservoirs. CLGS have been investigated in conduction-only systems. In this paper, we explore the impact of both forced and natural convection on the levels of heat extraction with a CLGS deployed in a hot wet rock reservoir. We bound potential benefits of convection by investigating liquid reservoirs over a range of natural and forced convective coefficients. Additionally, we investigate the effects of permeability, porosity, and geothermal temperature gradient in the reservoir on CLGS outputs. Reservoir simulations indicate that reservoir permeabilities of at least ~100 mD are required for natural convection to increase the heat output with respect to a conduction-only scenario. The impact increases with increasing reservoir temperature. When subject to a forced convection flow field, Darcy velocities of at least 10-7 m/s are required to obtain an increase in heat output.

The eXtended History Variable Reactive Burn (XHVRB) model is parameterized for hexanitrostilbene (HNS) and pentaerythritol tetranitrate (PETN) based on data collected 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 a variety of vapor deposited explosive films. This is because it 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. The XHVRB model was selected because previous work has shown that simpler, homogeneous reactive burn models (RBMs) were incapable of reproducing the particle velocity buildup observed in experiments with heterogeneous explosives. Therefore, calibrated XHVRB models are developed in an attempt to capture the heterogeneous behavior not captured previously and to assess the models’ ability to capture the SDT across multiple explosive film thicknesses.

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.

Closed-loop geothermal systems (CLGSs) rely on circulation of a heat transfer fluid in a closed-loop design without penetrating the reservoir to extract subsurface heat and bring it to the surface. We developed and applied numerical models to study u-shaped and coaxial CLGSs in hot-dry-rock over a more comprehensive parameter space than has been studied before, including water and supercritical CO2 (sCO2) as working fluids. An economic analysis of each realization was performed to evaluate the levelized cost of heat (LCOH) for direct heating application and levelized cost of electricity (LCOE) for electrical power generation. The results of the parameter study, composed of 2.5 million simulations, combined with a plant and economic model comprise the backbone of a publicly accessible web application that can be used to query, analyze, and plot outlet states, thermal and mechanical power output, and LCOH/LCOE, thereby facilitating feasibility studies led by potential developers, geothermal scientists, or the general public (https://gdr.openei.org/submissions/1473). Our results indicate competitive LCOH can be achieved; however, competitive LCOE cannot be achieved without significant reductions in drilling costs. We also present a site-based case study for multi-lateral systems and discuss how our comprehensive single-lateral analyses can be applied to approximate multi-lateral CLGSs. Looking beyond hot-dry-rock, we detail CLGS studies in permeable wet rock, albeit for a more limited parameter space, indicating that reservoir permeability of greater than 250 mD is necessary to significantly improve CLGS power production, and that reservoir temperatures greater than 200 °C, achieved by going to greater depths (∼3–4 km), may significantly enhance power production.

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.

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.

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.

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.

This paper provides a summary of planning work for experiments that will be necessary to address the long-term model validation needs required to meet offshore wind energy deployment goals. Conceptual experiments are identified and laid out in a validation hierarchy for both wind turbine and wind plant applications. Instrumentation needs that will be required for the offshore validation experiments to be impactful are then listed. The document concludes with a nominal vision for how these experiments can be accomplished.

As deep learning networks increase in size and performance, so do associated computational costs, approaching prohibitive levels. Dendrites offer powerful nonlinear "on-The-wire"computational capabilities, increasing the expressivity of the point neuron while preserving many of the advantages of SNNs. We seek to demonstrate the potential of dendritic computations by combining them with the low-power event-driven computation of Spiking Neural Networks (SNNs) for deep learning applications. To this end, we have developed a library that adds dendritic computation to SNNs within the PyTorch framework, enabling complex deep learning networks that still retain the low power advantages of SNNs. Our library leverages a dendrite CMOS hardware model to inform the software model, which enables nonlinear computation integrated with snnTorch at scale. By leveraging dendrites in a deep learning framework, we examine the capabilities of dendrites via coincidence detection and comparison in a machine learning task with a SNN. Finally, we discuss potential deep learning applications in the context of current state-of-The-Art deep learning methods and energy-efficient neuromorphic hardware.

In many applications, one can only access the inexact gradients and inexact hessian times vector products. Thus it is essential to consider algorithms that can handle such inexact quantities with a guaranteed convergence to solution. An inexact adaptive and provably convergent semismooth Newton method is considered to solve constrained optimization problems. In particular, dynamic optimization problems, which are known to be highly expensive, are the focus. A memory efficient semismooth Newton algorithm is introduced for these problems. The source of efficiency and inexactness is the randomized matrix sketching. Applications to optimization problems constrained by partial differential equations are also considered.

Network interface controllers (NICs) with general-purpose compute capabilities ('SmartNICs') present an opportunity for reducing host application overheads by offloading non-critical tasks to the NIC. In addition to moving computation, offloading requires that associated data is also transferred to the NIC. To meet this need, we introduce a high-performance, general-purpose data movement service that facilitates the of-floading of tasks to SmartNICs: The SmartNIC Data Movement Service (SDMS). SDMS provides near-line-rate transfer band-widths between the host and NIC. Moreover, SDMS's In-transit Data Placement (IDP) feature can reduce (or even eliminate) the cost of serializing data on the NIC by performing the necessary data formatting during the transfer. To illustrate these capabilities, we provide an in-depth case study using SDMS to offload data management operations related to Apache Arrow, a popular data format standard. For single-column tables, SDMS can achieve more than 87% of baseline throughput for data buffers that are 128 KiB or larger (and more than 95% of baseline throughput for buffers that are 1 MiB or larger) while also nearly eliminating the host and SmartNIC overhead associated with Arrow operations.

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.

Traditional electronics assemblies are typically packaged using physically or chemically blown potted foams to reduce the effects of shock and vibration. These potting materials have several drawbacks including manufacturing reliability, lack of internal preload control, and poor serviceability. A modular foam encapsulation approach combined with additively manufactured (AM) silicone lattice compression structures can address these issues for packaged electronics. These preloaded silicone lattice structures, known as foam replacement structures (FRSs), are an integral part of the encapsulation approach and must be properly characterized to model the assembly stresses and dynamics. In this study, dynamic test data is used to validate finite element models of an electronics assembly with modular encapsulation and a direct ink write (DIW) AM silicone FRS. A variety of DIW compression architectures are characterized, and their nominal stress-strain behavior is represented with hyperfoam constitutive model parameterizations. Modeling is conducted with Sierra finite element software, specifically with a handoff from assembly preloading and uniaxial compression in Sierra/Solid Mechanics to linear modal and vibration analysis in Sierra/Structural Dynamics. This work demonstrates the application of this advanced modeling workflow, and results show good agreement with test data for both static and dynamic quantities of interest, including preload, modal, and vibration response.