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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data-driven, multi-moment fluid modeling of Landau damping

Deriving governing equations of complex physical systems based on first principles can be quite challenging when there are certain unknown terms and hidden physical mechanisms in the systems. In this work, we apply a deep learning architecture to learn fluid partial differential equations (PDEs) of a plasma system based on the data acquired from a fully kinetic model. Here, the learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effect such as Landau damping. Based on the learned fluid closure, the data-driven, multi-moment fluid modeling can well reproduce all the physical quantities derived from the fully kinetic model. The calculated damping rate of Landau damping is consistent with both the fully kinetic simulation and the linear theory. The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve the fluid closure and reduce the computational cost of multi-scale modeling of global systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Harnessing deep reinforcement learning to construct time-dependent optimal fields for quantum control dynamics

Here, we present an efficient deep reinforcement learning (DRL) approach to automatically construct time-dependent optimal control fields that enable desired transitions in dynamical chemical systems. Our DRL approach gives impressive performance in constructing optimal control fields, even for cases that are difficult to converge with existing gradient-based approaches. We provide a detailed description of the algorithms and hyperparameters as well as performance metrics for our DRL-based approach. Our results demonstrate that DRL can be employed as an effective artificial intelligence approach to efficiently and autonomously design control fields in quantum dynamical chemical systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Co-op at Sandia National Laboratories (July - December 2019, Final Report)

This co-op was performed at Sandia National Laboratories (SNL) within the Weapon Surety Engineering 11 Department (9435), which is a customer-focused organization that provides engineering and system analysis expertise to ensure a safe deterrent throughout the entire weapon lifecycle. Engineers in 9435 develop a deep knowledge in both component and system function to support rigorous evaluation and mitigation of potential failure modes. They operate across multiple engineering disciplines (mechanical, electrical, computer, materials and systems engineering) with opportunities to develop crossdisciplinary capability. The team supports a variety of stockpile systems and is part of the product teams during development, design and production, as well as advanced concept and exploratory efforts. 9435 engineers use their extensive component and system knowledge, as well as experimental data and modeling, to ensure products function as intended with assured safety. Members of the department learn the function of multiple components, how those components integrate within the system and apply surety principles and processes to ensure a safe, secure, reliable, and effective U.S. nuclear deterrent.

42 ENGINEERING↗
DOE OSTI2024

Microbial Analysis of 100-N Borehole Samples: Estimation of Diesel and BTEX Degrading Bacterial Populations

Historical unplanned releases at the UPR-100-N-17 166-N Diesel Oil Supply Line Leak waste site located within the 100-NR-1 OU resulted in diesel fuel contamination of the unsaturated vadose zone and site groundwater. Enhanced in situ bioremediation of the deep vadose zone was the final remedy decision for the 100-NR-1 Operable Unit (OU) and the full-scale bioventing demo began in 2012. For this remedy, oxygen is actively supplied to the deep vadose zone to stimulate aerobic hydrocarbon degrading microorganisms to metabolize petroleum contamination. Degradation rate calculations based on vadose zone respirometry measurements, however, have indicated declining activity over recent years, though the precise reason for this apparent decline has not been determined. In this investigation 100-NR-1 OU sediment cores were analyzed for total microbial biomass, as well as enumeration of diesel and BTEX degrading bacteria to determine if these microbial populations have declined relative to previous measurements taken in 2009.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Infrared Observations of 2I/Borisov near Perihelion

As the first interstellar comet, 2I/Borisov provides a unique opportunity to study the surface composition of a comet from another stellar system, particularly whether it has water ice. In order to investigate the nature of 2I/Borisov, we conducted infrared observations close to perihelion. The water ice, if present, is expected to be revealed by absorption features at 1.5 and 2 micron. We therefore used FLAMINGOS-2 mounted on the Gemini south telescope, to carry out deep imaging on 2019 November 30 UT and spectroscopy on 2019 December 7 UT. At first glance, our imaging did not reveal an apparent coma or a cometary tail. This is due to the bright sky background and our short exposure times. Nevertheless we were able to put an upper limit of the size of the nucleus, as well as provide high-precision astrometry that can be used to investigate nongravitational acceleration in the future. Our infrared spectra showed a negative slope, contrary to the results by Yang et al. It is not unheard of for a comet to show a negative slope, and to progressively exhibit a spectrum with decreasing slope. Possible causes of the decreasing slope are an increase in water ice and/or decrease in dust size. Given the fact that our observations were carried out close to perihelion, it is likely that both factors contribute to the decreasing, negative slope of the infrared spectrum.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Topological Machine Learning Methods for Power System Responses to Contingencies

While deep learning tools, coupled with the emerging machinery of topological data analysis, are proven to deliver various performance gains in a broad range of applications, from image classification to biosurveillance to blockchain fraud detection, their utility in areas of high societal importance such as power system modeling and, particularly, resilience quantification in the energy sector yet remains untapped. To provide fast acting synthetic regulation and contingency reserve services to the grid while having minimal disruptions on customer quality of service, we propose a new topology-based system that depends on a neural network architecture for impact metric classification and prediction in power systems. This novel topology-based system allows one to evaluate the impact of three power system contingency types, in conjunction with transmission lines, transformers, and transmission lines combined with transformers. We show that the proposed new neural network architecture equipped with local topological measures facilitates more accurate classification of unserved load as well as the amount of unserved load. In addition, we are able to learn more about the complex relationships between electrical properties and local topological measurements on their simulated response to contingencies for the NREL-SIIP power system.

contingency analysis↗
DOE OSTI2024

Reconstruction of unstable heavy particles using deep symmetry-preserving attention networks

Abstract Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to the underlying partons. An approach based on a generalized attention mechanism, symmetry preserving attention networks (SPA-NET), has been previously applied to top quark pair decays at the Large Hadron Collider which produce only hadronic jets. Here we extend the SPA-NET architecture to consider multiple input object types, such as leptons, as well as global event features, such as the missing transverse momentum. In addition, we provide regression and classification outputs to supplement the parton assignment. We explore the performance of the extended capability of SPA-NET in the context of semi-leptonic decays of top quark pairs as well as top quark pairs produced in association with a Higgs boson. We find significant improvements in the power of three representative studies: a search for$$t\bar{t}H$$ t t ¯ H , a measurement of the top quark mass, and a search for a heavy$${Z}^{{\prime} }$$ Z ′ decaying to top quark pairs. We present ablation studies to provide insight on what the network has learned in each case.

Physics↗
DOE OSTI2020

Sampling and resolution characteristics in reduced order models of shallow water equations: Intrusive vs nonintrusive

We investigate the sensitivity of reduced order models (ROMs) to training data spatial resolution as well as sampling rate. In particular, we consider proper orthogonal decomposition (POD), coupled with Galerkin projection (POD-GP), as an intrusive reduced order modeling technique. For non-intrusive ROMs, we consider two frameworks. The first is using dynamic mode decomposition (DMD), and the second is based on artificial neural networks (ANNs). For ANN, we utilized a residual deep neural network, and for DMD we have studied two versions of DMD approaches; one with hard thresholding and the other with sorted bases selection. Also, we highlight the differences between mean-subtracting the data (centering) and using the data without mean-subtraction. We tested these ROMs using a system of 2D shallow water equations for four different numerical experiments, adopting combinations of sampling rates and spatial resolutions. For these cases, we found that the DMD basis obtained with hard threshodling is sensitive to sampling rate. The sorted DMD algorithm helps to mitigate this problem and yields more stabilized and converging solution. Furthermore, we demonstrate that both DMD approaches without mean subtraction provide significantly more accurate results than DMD with mean-subtracting the data. On the other hand, POD is relatively insensitive to sampling rate and yields better representation of the flow field. Meanwhile, spatial resolution has little effect on both POD and DMD performances. Here, the numerical results reveal that an ANN on POD subspace (POD-ANN) performs remarkably better than POD-GP and DMD in capturing system dynamics, even with a small number of modes.

42 ENGINEERING↗
DOE OSTI2022

Gravity duals for defect quivers in the Veneziano limit

We construct gravity duals to supersymmetric gauge theories in the presence of unquenched flavor hypermultiplets in the fundamental representation of the gauge group living on the (1+1)-dimensional defect. This configuration is given by the intersection of two sets of D3-branes. Working in the Veneziano limit with large number of colors and flavors we are able to find a closed set of equations describing the dual geometry. We briefly discuss the corresponding solutions for massless flavors as well as in the small flavor limit of massive hypermultiplets. Interestingly, the $\frac{1}{16}$-BPS supergravity solutions, preserving two Poincaré supercharges, are generically only asymptotically anti de-Sitter and the dilaton does not vary with the holographic radial coordinate. This implies that the classical Type IIB supergravity solutions remain trustworthy descriptions for the gauge theories from the deep IR to the far UV.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Rechargeable metal-metal alkaline batteries: Recent advances, current issues and future research strategies

Over the past few decades, remarkable advancement has been attained in the field of rechargeable metal–metal alkaline batteries (RABs). In terms of safety, energy density, charge-discharge capacity, and long-term storage capability, metal-metal RABs (e.g., Ni–Zn, Ni–Fe, Ni–Bi, Ni–MH, Ag–Zn, Co–Zn, Cu–Zn, and Bi–Zn systems) are contemplated as the promising energy storage devices for the applications in electric vehicles (EVs), hybrid EVs, grid-scale energy storage, as well as various implantable and wearable electronic devices. Especially, Ni-MH batteries become competitive with Li-ion batteries for EVs and hybrid EVs applications due to their high tolerance against mechanical abuse, stability under wide temperature ranges, and considerable charge/discharge capacity. Meanwhile, earlier works reviewed only specific topics, so, as a rapidly growing research topic, providing a deep understanding on metal–metal RABs is timely and worthwhile. So here, in this work, we discuss the electrochemistry of all metal-metal RABs, then full cell designing with their performance will be discussed thoroughly. Further, issues associated with the existing metal–metal RABs and corresponding improvement approaches will be provided for future advances in high-performance and reliable metal-metal RABs.

25 ENERGY STORAGE↗
DOE OSTI2024

Impacts of Hydrogen Bond Donor Structures in Phenolic Aldehyde Deep Eutectic Solvents on Pretreatment Efficiency

As a green solvent for biomass processing, deep eutectic solvents (DESs) have shown effectiveness in biomass processing. Here, in this study, phenolic aldehydes with different numbers of methoxy groups, including 4-hydroxybenzaldehyde (HBA, no methoxy), vanillin (VA, monomethoxy), and syringaldehyde (SA, dimethoxy) were employed to synthesize DESs with choline chloride (ChCl). The presence of methoxy groups in the hydrogen bond donor structure affected DES properties, as well as biomass pretreatment performance. The high thermal stability of phenolic aldehyde DESs was shown with over 225 °C onset temperature. The hydrogen bond donor with one aldehyde and one hydroxyl group at the para position without a methoxy group (ChCl-HBA) showed the highest xylan removal and delignification, reaching 59.3 and 88.0%, respectively, leading to the highest enzymatic hydrolysis yield. Sonication after pretreatment further enhanced the hydrolysis yields, achieving 83.3% glucan conversion and 50.1% xylan conversion. In the lignin-rich fraction, the recovered lignin showed a low weight–average molecular weight under 2100 g/mol with a relatively uniform molecular weight dispersity below 1.5. This study provides insights into how the chemical structure of hydrogen bond donors in DESs affects biomass processing and paves the way for designing effective lignin-derived DES in future biorefinery processes.

09 BIOMASS FUELS↗
DOE OSTI2020

Machine-Learning-Based Analysis of the Guy-Greenbrier, Arkansas Earthquakes: A Tale of Two Sequences

Here, we revisited the June 2010 to October 2011 Guy-Greenbrier earthquake sequence in central Arkansas using PhaseNet, a deep neural network trained to pick P and S arrival times. We applied PhaseNet to continuous waveform data and used phase association and hypocenter relocation to locate nearly 90,000 events. Our catalog suggests that the sequence consists of two adjacent earthquake sequences on the same fault and that the second sequence may be associated with the wastewater disposal well to the west of the Guy-Greenbrier Fault, rather than the wells to the north and the east that were previously implicated. We find that each sequence is composed of many small clusters that exhibit diffusion along the fault at shorter timescales. Our study demonstrates that machine-learning-based earthquake catalog development is now feasible and will yield new insights into earthquake behavior.

58 GEOSCIENCES↗
DOE OSTI2021

Thermal Experiments for Fractured Rock Characterization: Theoretical Analysis and Inverse Modeling

Abstract Field‐scale properties of fractured rocks play a crucial role in many subsurface applications, yet methodologies for identification of the statistical parameters of a discrete fracture network (DFN) are scarce. We present an inversion technique to infer two such parameters, fracture density and fractal dimension, from cross‐borehole thermal experiments data. It is based on a particle‐based heat‐transfer model, whose evaluation is accelerated with a deep neural network (DNN) surrogate that is integrated into a grid search. The DNN is trained on a small number of the heat‐transfer model runs and predicts the cumulative density function of the thermal field. The latter is used to compute fine posterior distributions of the (to be estimated) parameters. Our synthetic experiments reveal that fracture density is well constrained by data, while fractal dimension is harder to determine. Adding nonuniform prior information related to the DFN connectivity improves the inference of this parameter.

Zhou, Zitong↗
DOE OSTI2025

Optimizing Alabama’s CO 2 Storage in Shelby County (Project OASIS): Task 4.0 Deliverable – Geologic Analysis Report

Project OASIS (Optimizing Alabama’s CO 2 Storage in Shelby County) is a geologic and reservoir characterization study designed to evaluate deep saline formations for potential long-term carbon dioxide (CO 2 ) storage in central Alabama near the National Carbon Capture Center (NCCC) and Alabama Power’s Plant Gaston. The project centers on the Cambro-Ordovician Knox Group and underlying strata such as the Conasauga and Rome Formations, which were investigated through the drilling of two stratigraphic test wells to obtain electronic well logs, core, and sidewall core plugs. These data provide direct measurements of porosity, permeability, and lithologic variability critical for reservoir characterization. Complementing the well program, a limited 2D seismic survey was acquired to help select the site for Westover #2, and a more regional Seismic Exchange (SEI) seismic survey was licensed and interpreted to define structural and stratigraphic frameworks in a new Static Earth Model (SEM), map reservoir continuity, and identify potential sealing intervals. Integrated with geologic and reservoir modeling, these datasets form the basis for evaluating storage capacity, injectivity, and containment.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2026

Electron Neutrino Selection for Neutrino Oscillation Searches in the SBN Program

The Short- Baseline Neutrino (SBN) Pprogram at Fermilab employs a near detector, SBND, and a far detector, ICARUS, to search for baseline-dependent changes to the measure muon neutrino disappearance and electron neutrino appearance and disappearance from the muon neutrino-dominated Booster Neutrino Beam flux. The use of the same liquid argon time projection chamber (LArTPC) technology in both detectors and the same neutrino beam enables a systematically well-constrained oscillation measurement, by leveraging the high statistics of O(10 million) neutrino interactions expected at SBND through 2027 to constrain flux and cross-section systematics. In this poster, we present high-statistics, high-purity electron neutrino selections in SBND and ICARUS and related electron shower energy calibration measurements in SBND enabled by the SPINE deep learning-based particle reconstruction framework. Furthermore, we discuss how these samples can enable high sensitivity to '3+1' sterile neutrino oscillations in a combined fit accounting for systematic uncertainties and correlations.

Oza, Nupur [Nevis Labs, Columbia U.; Columbia U.]↗
DOE OSTI2022

Comparing Deep Learning Approaches for Understanding Genotype × Phenotype Interactions in Biomass Sorghum

We explore the use of deep convolutional neural networks (CNNs) trained on overhead imagery of biomass sorghum to ascertain the relationship between single nucleotide polymorphisms (SNPs), or groups of related SNPs, and the phenotypes they control. We consider both CNNs trained explicitly on the classification task of predicting whether an image shows a plant with a reference or alternate version of various SNPs as well as CNNs trained to create data-driven features based on learning features so that images from the same plot are more similar than images from different plots, and then using the features this network learns for genetic marker classification. We characterize how efficient both approaches are at predicting the presence or absence of a genetic markers, and visualize what parts of the images are most important for those predictions. We find that the data-driven approaches give somewhat higher prediction performance, but have visualizations that are harder to interpret; and we give suggestions of potential future machine learning research and discuss the possibilities of using this approach to uncover unknown genotype × phenotype relationships.

Zhang, Zeyu↗
DOE OSTI2020

Cigeo Design Options, Risks and Opportunities Management: A Continuous Project Management Goal - 20015

Andra is currently completing the detailed engineering phase of the stepwise design development of Cigeo, the French HLW and IL-LLW Deep Geological Repository. If licensed in the coming years, Cigeo's progressive construction, industrial operation and closure will last over more than a century. Such a duration, associated with the requirements in terms of reversibility, lead to the choice of an incremental design that allows, for next operator generations, to integrate improvements derived from scientific and technical progress as well as from experience feedback from its operation. This incremental design approach, associated with a permanent risks and opportunities management process, will continue after licensing of Cigeo as a tool for the governance of the reversibility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Shallow- and deep-convection characteristics in the greater Houston, Texas, area using cell tracking methodology

The convective lifecycle, from initiation to maturity and dissipation, is driven by a combination of kinematic, thermodynamic, microphysical, and radiative processes that are strongly coupled and variable in time and space. Weather radars have been traditionally used to provide various convective-cloud characteristics. Here, we analyzed climatological convective-cell radar characteristics to obtain and assess the diurnal cycles of three convective-cell types – shallow, modest deep, and vigorous deep convective cells – that formed in the greater Houston area, using the National Weather Service radar from Houston, Texas, and a multi-cell identification and tracking algorithm. The examined dataset spans 4 years (2018–2021) and covers the warm-season months (June to September) in those years. The analysis showed clear diurnal cycles in cell initiation (CI) consistent with the sea breeze circulation and showed diurnal and normalized lifetime relationships in cell evolution parameters (e.g., maximum reflectivity, echo-top height, Geostationary Operational Environmental Satellite-16 (GOES-16) channel 13 brightness temperature, and the height of maximum reflectivity). The cell evolution is well represented by relationships between (1) the height and value of the maximum radar reflectivity, (2) the minimum GOES-16 channel 13 brightness temperature and the maximum vertically integrated liquid, (3) the maximum reflectivity and columnar-average reflectivity, and (4) the echo-top ascent rate and cell lifetime. The relationships presented herein help to identify the cell lifecycle stages such as early shallow convection, vigorous vertical development, anvil development, and convective core dissipation. GOES-16 Aerosol Optical Depth values are also used as a proxy for cell initiation aerosol concentrations to investigate any potential relationships between initiation location and aerosol concentration. Overall, no significant relationships between initiation location and aerosol concentration were found for the three cell types investigated, but there are some minor differences in the pre-CI aerosol optical depth for vigorous deep convective cells.

54 ENVIRONMENTAL SCIENCES↗