Publications

Accurate measurement of frequency response functions is essential for system identification, model updating, and structural health monitoring. However, sensor noise and leakage cause variance and systematic errors in estimated FRFs. Low-noise sensors, windowing techniques, and intelligent experiment design can mitigate these effects but are often limited by practical considerations. This chapter is a guide to implementation of local modeling methods for FRF estimation, which have been extensively researched but are seldom used in practice. Theoretical background is presented, and a procedure for automatically selecting a parameterization and model order is proposed. Computational improvements are discussed that make local modeling feasible for systems with many input and output channels. The methods discussed herein are validated on a simulation example and two experimental examples: a multi-input, multi-output system with three inputs and 84 outputs and a nonlinear beam assembly. They are shown to significantly outperform the traditional H1 and HSVD estimators.

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.

Modal characterization of a structure is necessary to inform predictive simulation models. Unfortunately, cost and schedule limitations tend to prioritize other dynamic tests, which can lead to inadequate or nonexistent modal testing. To utilize the dynamic test data that is acquired, analysts can extract operational deflection shapes (ODS) which can then be used as a substitute for modal data in model updating and structure characterization. However, extremely high levels of excitation during vibration testing may introduce nonlinear behavior that distorts the ODS prediction. This chapter investigates the reliability of using ODS as a replacement for traditional modal testing on an academic structure designed to respond with intermittent impact. This chapter calculates ODS from responses at several input excitation levels, and the influence of nonlinear impact on the resulting operating modes is discussed.

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.

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).

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

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

This paper builds upon previous research in developing a SiC Drift Step Recovery Diode (DSRD) Model in Silvaco Victory Device. For this research, the DSRD is based on an N-type substrate for improved manufacturability. The model described in this paper was developed by characterizing DSRD devices under DC and transient conditions. The details of the pulsed power testbed developed for the transient characterization is outlined in this paper. The goal of this model is to allow the rapid development of future pulsed power systems and for further device structure optimization.

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.

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.

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

This article aims at discovering the unknown variables in the system through data analysis. The main idea is to use the time of data collection as a surrogate variable and try to identify the unknown variables by modeling gradual and sudden changes in the data. We use Gaussian process modeling and a sparse representation of the sudden changes to efficiently estimate the large number of parameters in the proposed statistical model. The method is tested on a realistic dataset generated using a one-dimensional implementation of a Magnetized Liner Inertial Fusion (MagLIF) simulation model, and encouraging results are obtained.

This study introduces the Progressive Improved Neural Operator (p-INO) framework, aimed at advancing machine-learning-based reduced-order models within geomechanics for underground resource optimization and carbon sequestration applications.The p-INO method transcends traditional transfer learning limitations through progressive learning, enhancing the capability of transferring knowledge from many sources.Through numerical experiments, the performance of p-INO is benchmarked against standard Improved Neural Operators (INO) in scenarios varying by data availability (different number of training samples).The research utilizes simulation data reflecting scenarios like single-phase, two-phase, and two-phase flow with mechanics inspired by the Illinois Basin Decatur Project.Results reveal that p-INO significantly surpasses conventional INO models in accuracy, particularly in data-constrained environments.Besides, adding more priori information (more trained models used by p-INO) can further enhance the process.This experiment demonstrates p-INO's robustness in leveraging sparse datasets for precise predictions across complex subsurface physics scenarios.The findings underscore the potential of p-INO to revolutionize predictive modeling in geomechanics, presenting a substantial improvement in computational efficiency and accuracy for large-scale subsurface simulations.

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.

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.

Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints – non-negative weights and sparse coding of optic flow. While the CNNs and NNMF models both accurately estimate the observer's self-motion from purely translational or rotational optic flow, NNMF and the CNNs with nonnegative weights yield substantially less accurate estimates than the other CNNs when tested on more complex optic flow that combines observer translation and rotation. Despite its poor accuracy, NNMF gives rise to tuning properties that align more closely with those observed in primate MSTd than any of the accuracy-optimized CNNs. This work offers a step toward a deeper understanding of the computational properties and constraints that describe the optic flow tuning of primate area MSTd.

Existing natural gas (NG) pipeline infrastructure can be used to transport gaseous hydrogen (GH2) or blends of NG and hydrogen as low carbon alternatives to NG. Pipeline steels exhibit accelerated fatigue crack growth rates and reduced fracture resistance in the presence of GH2. The hydrogen-assisted fatigue crack growth (HAFCG) rates and hydrogen assisted fracture (HAF) resistance for pipeline steels depend on the hydrogen gas pressure. This study aims to correlate and compare the HAFCG rates of pipeline steels tested in two different gaseous environments at different pressures; high-purity hydrogen (99.9999 % H2) and a blend of nitrogen with 3% hydrogen gas (N2+3%H2). K-controlled FCG tests were performed using compact tension (CT) samples extracted from a vintage X52 (installed in 1962) and a modern X70 (2021) pipeline steel in the different gaseous environments. Subsequently, monotonic fracture tests were performed in the GH2 environment. The HAFCG rates increased with increasing GH2 pressure for both steels, in the ΔK range explored in this study. Nearly identical HAFCG rates were observed for the steels tested in different environments with equivalent fugacity (34.5 bar pure GH2 and 731 bar Blend with 3%H2). The fracture resistance of pipeline steels was significantly reduced in the presence of GH2, even at pressure as low as 1 bar. The reduction in HAF resistance tends to saturate with increasing GH2 pressure. While the fracture resistance of modern steel is substantially higher than vintage steel in air, in high pressure GH2, the HAF resistance is comparable. Similar HAF resistance values were obtained for the respective steels in the pure and blended GH2 environment with similar fugacity. This study confirms that fugacity parameter can be used to correlate HAFCG and HAF behavior of different hydrogen blends. The fracture surface features of the pipeline steels, tested in the different environments are compared to rationalize the observed behavior in GH2.

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.

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

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.

We outline a uniform taxonomy for photovoltaic (PV) system operations and maintenance (O&M) data. For example, such data include time-series of power, voltage, and meteorological measurements, records of instrument cleaning and calibration, records of O&M-related activities such as inspections, and records of events such as device failures. The described taxonomy provides structured models for all these data using consistent terms and classifications. A uniform taxonomy is enables analytics spanning across systems and portfolios of systems.

National Security Presidential Memorandum-20 defines three tier levels for launch approval of space nuclear systems. The two main factors determining the tier level are the total quantity and type of radioactive sources and the probability of any member of the public receiving doses above certain thresholds. The total quantity of radioactive sources is compared with International Atomic Energy Agency transportation regulations. The dose probability is determined by the product of three terms: 1) the probability of a launch accident occurring; 2) the probability of a release of radioactive material given an accident; and 3) the probability of exceeding the dose threshold to any member of the public given a release. This paper provides a methodology for evaluating these values and applies this methodology to an example mission as a demonstration. For the example mission, a preliminary tier determination of Tier III was concluded.

Abstract not provided.

Geogenic gases often reside in intergranular pore space, fluid inclusions, and within mineral grains. In particular, helium-4 (4He) is generated by alpha decay of uranium and thorium in rocks. The emitted 4He nuclei can be trapped in the rock matrix or in fluid inclusions. Recent work has shown that releases of helium occur during plastic deformation of crustal rocks above atmospheric concentrations that are detectable in the field. However, it is unclear how rock type and deformation modalities affect the cumulative gas released. This work seeks to address how different deformation modalities observed in several rock types affect release of helium. Axial compression tests with granite, rhyolite, tuff, dolostone, and sandstone - under vacuum conditions - were conducted to measure the transient release of helium from each sample during crushing. It was found that, when crushed up to 97500 N, each rock type released helium at a rate quantifiable using a helium mass spectrometer leak detector. For plutonic rock like granite, helium flow rate spikes with the application of force as the samples elastically deform until fracture, then decays slowly until grain breakdown comminution begins to occur. Both the rhyolite and tuff do not experience such large spikes in helium flow rate, with the rhyolites fracturing at much lower force and the tuffs compacting instead of fracturing due to their high porosity. Both rhyolite and tuff instead experience a lesser but steady helium release as they are crushed. The cumulative helium release for the volcanic tuffs varies as much as two orders of magnitude but is fairly consistent for the denser rhyolite and granite tested. The results indicate that there is a large degassing of helium as rocks are elastically and inelastically deformed prior to fracturing. For more porous and less brittle rocks, the cumulative release will depend more on the degree of deformation applied. These results are compared with known U/Th radioisotopes in the rocks to relate the trapped helium as either produced in the rock or from secondary migration of 4He.

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.