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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Initial Findings from US Department of Energy’s Better Plants Virtual in-Plant Training on 50001 Ready

Manufacturing facilities use about 35% of the domestic energy in the United States every year. Implementing an effective energy management system (EnMS) is one of the most important approaches to improve energy efficiency. However, the implementation of EnMS is low for many countries (including the US) and even for energy-intensive sectors. The reasons for the low implementation rate of energy management systems had been investigated by multiple researchers, but very few studies have focused on the barriers and challenges of implementing ISO 50001-based energy management systems. To contribute to this understudied area, this paper discusses the implementation and outcomes of the first Better Plants 50001 Ready Virtual In-plant Training. This paper first provides an overview of 50001 Ready and the 50001 Ready Navigator Tool. Then, it provides details on this training event and its outcomes. Finally, it discusses findings from the responses to 40 live polling questions about the status of the 25 tasks of the 50001 Ready Navigator for participating companies, key components of the participating manufacturing companies’ energy management systems, and challenges and barriers that these companies are facing. The findings suggest that although many companies understood the importance of an effective energy management system, about half of them do not understand the required resources for building energy management systems, and most of them have only just begun establishing these systems and need more assistance and resources in multiple areas. More specifically, more assistance is necessary for the following: (1) improving corporate management’s understanding of the time and resources needed to build an EnMS as well as the benefits; (2) creating linear regression models for more accurate energy performance tracking; (3) understanding energy use, collecting and analyzing energy performance data; (4) optimizing equipment operational controls, and creating action plans.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

The Impact of Void-finding Algorithms on Galaxy Classification

We explore how the definition of a void influences the conclusions drawn about the impact of the void environment on galactic properties using two void-finding algorithms in the Void Analysis Software Toolkit: Voronoi Voids (V 2 ), a Python implementation of ZOnes Bordering On Voidness (ZOBOV); and VoidFinder, an algorithm that grows and merges spherical void regions. Using the Sloan Digital Sky Survey Data Release 7, we find that galaxies found in VoidFinder voids tend to be bluer and fainter and to have higher (specific) star formation rates than galaxies in denser regions. Conversely, galaxies found in V 2 voids show less significant differences when compared to galaxies in denser regions, less consistent with the large-scale environmental effects on galaxy properties expected from both simulations and previous observations. These results align with previous simulation results that show V 2 -identified voids “leak” into the dense walls between voids because their boundaries extend up to the density maxima in the walls. As a result, when using ZOBOV-based void-finders, galaxies likely to be part of wall regions are instead classified as void galaxies, a misclassification that can be critical to our understanding of galaxy evolution.

cosmic web↗
DOE OSTI2025

On Finding Black Holes in Photometric Microlensing Surveys

There are expected to be millions of isolated black holes in the galaxy resulting from the deaths of massive stars. Measuring the abundance and properties of this remnant population would shed light on the end stages of stellar evolution and the evolution paths of black hole systems. Detecting isolated black holes is currently only possible via gravitational microlensing, which has so far yielded one definitive detection. The difficulty in finding microlensing black holes lies in having to choose a small subset of events, based on characteristics of their light curves, to allocate expensive and scarce follow-up resources to confirm the identity of the lens. Current methods either rely on simple cuts in parameter space without using the full distribution information or are only effective on small subsets of events. In this paper, we present a new lens classification method. The classifier takes in posterior constraints on light-curve parameters and combines them with a Galactic simulation to estimate the lens class probability. This method is flexible and can be used with any set of microlensing light-curve parameters, making it applicable to large samples of events. We make this classification framework available via the popclass Python package. We apply the classifier to ~10,000 microlensing events from the Optical Gravitational Lensing Experiment survey and find 23 high-probability black hole candidates. Our classifier also suggests that the only known isolated black hole is an observational outlier, according to current Galactic models, and the allocation of astrometric follow-up on this event was a high-risk strategy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Finding a contaminant source in a volume of flowing fluid

A method includes: mapping a boundary of a volume of flowing fluid; partitioning the volume by a computational mesh; finding a contaminant location at a first sensor that is disposed within the volume; obtaining a measured velocity of the flowing fluid within the volume; generating a reversed velocity vector field within the mesh, in response to the measured velocity; time stepping the contaminant location from the first sensor along the reversed velocity vector field until the contaminant location intersects the boundary of the volume; and finding a contaminant source at the intersection of the time stepped contaminant location with the boundary of the volume.

Lopez-Marrero, Vanessa↗
DOE OSTI2024

A new track finding algorithm based on a multi-dimensional extension of the Hough Transform

We introduce a new pattern recognition algorithm for track finding in High Energy Physics Experiments based on an extension of the Hough Transform to multiple dimensions. A remarkable property of this algorithm is that the execution time is simply proportional to the total number of the hits to be processed, making it particularly attractive for high occupancy situations. The algorithm needs to be trained using a sufficiently large set of simulated tracks. The same track finding algorithm can be used for very different detector geometries and only the set of simulated tracks used for training needs to be changed. The particular structure of the algorithm also lends itself naturally to parallel hardware implementations which, combined with its intrinsic flexibility, should provide a most powerful tool for triggering at future colliders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Finding Real Uncertainties From Physical Simulations

Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Report Series: Finding of Effect and Mitigation Documentation for Building 23-W10, Mercury, Area 23, Nevada National Security Site, Nye County, Nevada

Finding of Effect: The U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) plans to demolish Building 23-W10 in Mercury (Nevada State Historic Preservation Office [SHPO] Resource No. B15228), which is on the Nevada National Security Site (NNSS) in Nye County, Nevada (Figure 1). The purpose of the undertaking is related to the modernization of Mercury for future mission needs. The NNSA/NFO will implement this undertaking in accordance with the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA. Building 23-W10, a supply warehouse, was installed in Mercury in 1962 (NNSS GIS Database) and served as a support facility for nuclear testing throughout much of the Cold War. It was likely produced during World War II (WWII) and previously installed at Camp Desert Rock. The town of Mercury and the immediate surrounding area have been formally determined eligible for listing in the National Register of Historic Places (National Register, NRHP) as the Mercury Historic District (MHD, SHPO Resource #D230) under Criteria A and C for their importance in supporting nuclear testing and scientific research from 1951 through 1992. Building 23-W10 was identified as a contributing element to the MHD in a 2018 architectural survey of the district (Reno et al. 2018) and recorded on a Nevada Architectural Resource Assessment (ARA) form (Reno et al. 2017). Building 23-W10 was also identified in Appendix C of the Mercury PA as a Category II contributing element. Category II properties are those that have several representatives in the MHD, such as warehouses, but may possess different engineering or architectural characteristics that distinguish them from other classes of similar elements. Building 23-W10 is a historic property for the purposes of compliance with Section 106 of the National Historic Preservation Act (NHPA) and subject to the stipulations of the Mercury PA. Mitigation: The purpose of this letter report is to support the mitigation of the demolition of Building 23-W10 (Nevada State Historic Preservation Office [SHPO] Resource No. B15228) in the Mercury Historic District (MHD, SHPO Resource #D230) at the Nevada National Security Site (NNSS) in Nye County, Nevada. The warehouse is considered contributing to the significance of the district both for its historic importance in relation to nuclear testing under Criterion A and as a part of the distinctive design and construction of the district under Criterion C. This submission is intended to comply with the stipulations in the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Report Series: Finding of Effect and Mitigation Documentation for Building 23-153, Mercury, Area 23, Nevada National Security Site, Nye County, Nevada

Finding of Effect: The U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) plans to demolish Building 23-153, the Mechanical Calibration Laboratory (Calibration Lab), in Mercury (Nevada State Historic Preservation Office [SHPO] Resource No. B15271), which is on the Nevada National Security Site (NNSS) in Nye County, Nevada. The purpose of the undertaking is related to the modernization of Mercury for future mission needs. The NNSA/NFO will implement this undertaking in accordance with the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA. Building 23-153 was constructed in Mercury in 1982 as the Mechanical Calibration Laboratory and served as a support facility for nuclear testing throughout the last decade of the Cold War. The town of Mercury and the immediate surrounding area have been formally determined eligible for listing in the National Register of Historic Places (NRHP) as the Mercury Historic District (MHD, SHPO Resource No. D230) under Criteria A and C for their importance in supporting nuclear testing and scientific research from 1951 through 1992. Building 23-153 was identified as a contributing element to the MHD in a 2018 architectural survey of the district (Reno et al. 2018) and recorded on a Nevada Architectural Resource Assessment (ARA) form (Reno et al. 2017). Building 23-153 was also identified in Appendix C of the Mercury PA as a Category II contributing element. Category II properties are those that have several representatives in the MHD but may possess different engineering or architectural characteristics that distinguish them from other classes of similar elements. Building 23-153 is a historic property for the purposes of compliance with Section 106 of the National Historic Preservation Act (NHPA) and subject to the stipulations of the Mercury PA. Mitigation: The purpose of this letter report is to submit documentation related to the mitigation of the demolition of Building 23-153, the Mechanical Calibration Laboratory (Calibration Lab, Nevada State Historic Preservation Office [SHPO] Resource No. B15271), in the Mercury Historic District (MHD, SHPO Resource No. D230) at the Nevada National Security Site (NNSS) in Nye County, Nevada. The Calibration Lab is considered contributing to the significance of the district both for its historic importance in relation to nuclear testing under Criterion A and as a part of the distinctive design and construction of the district under Criterion C. This submission is intended to comply with the stipulations in the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Finding of No Adverse Effect for the Façade Alteration of Building 23-117, Administration Building, Mercury, Area 23, Nevada National Security Site, Nye County, Nevada

The U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) plans to update the façade of Building 23-117 in the town of Mercury (Nevada State Historic Preservation Office [SHPO] Resource No. B15256), which is on the Nevada National Security Site (NNSS) in Nye County, Nevada. The purpose of the undertaking is to incorporate Building 23-117 into the new Mercury campus in accordance with the master plan for the modernization of Mercury. The NNSA/NFO will implement this undertaking in accordance with the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA. Building 23-117 was built in 1982 as the architect-engineer Administration Building1 for Holmes & Narver, a government contractor who helped design and engineer the town of Mercury and other areas on the NNSS from its inception until the termination of their contract in 1990. The building continued to be used by subsequent government contractors and is currently still in use by MSTS. The town of Mercury and the immediate surrounding area have been formally determined eligible for listing in the National Register of Historic Places (National Register, NRHP) as the Mercury Historic District (MHD, SHPO Resource No. D230) under Criteria A and C for its importance in supporting nuclear testing and scientific research from 1951 through 1992. Building 23-117 was identified as a contributing element to the MHD in a 2018 architectural survey of the district (Reno et al.) and recorded on a Nevada Architectural Resource Assessment (ARA) form (Reno et al. 2017). It is a historic property for the purposes of compliance with Section 106 of the National Historic Preservation Act (NHPA) and is subject to the stipulations of the Mercury PA. The NNSA/NFO requested that Desert Research Institute (DRI), cultural resource subject matter experts, analyze the effects of the proposed project on historic properties in the Area of Potential Effect (APE) and make a recommended finding for the undertaking in accordance with Section 106 of the NHPA and the Mercury PA. The purpose of this letter report is to submit documentation related to the mitigation of the façade alteration of Building 23-117 (Nevada State Historic Preservation Office [SHPO] Resource No. B15256) in the Mercury Historic District (MHD, SHPO Resource No. D230). This submission is intended to comply with the stipulations in the Programmatic Agreement between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site at Mercury in Nye County, Nevada, hereafter referred to as the Mercury PA.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Finding Hidden Patterns in High Resolution Wind Flow Model Simulations

Wind flow data is critical in terms of investment decisions and policy making. High resolution data from wind flow model simulations serve as a supplement to the limited resource of original wind flow data collection. Given the large size of data, finding hidden patterns in wind flow model simulations are critical for reducing the dimensionality of the analysis. In this work, we first perform dimension reduction with two autoencoder models: the CNN-based autoencoder (CNN-AE) [1], and hierarchical autoencoder (HIER-AE) [2], and compare their performance with the Principal Component Analysis (PCA). We then investigate the super-resolution of the wind flow data. By training a Generative Adversarial Network (GAN) with 300 epochs, we obtained a trained model with 2× resolution enhancement. We compare the results of GAN with Convolutional Neural Network (CNN), and GAN results show finer structure as expected in the data field images. Also, the kinetic energy spectra comparisons show that GAN outperforms CNN in terms of reproducing the physical properties for high wavenumbers and is critical for analysis where high-wavenumber kinetics play an important role.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A phase transition for finding needles in nonlinear haystacks with LASSO artificial neural networks

To fit sparse linear associations, a LASSO sparsity inducing penalty with a single hyperparameter provably allows to recover the important features (needles) with high probability in certain regimes even if the sample size is smaller than the dimension of the input vector (haystack). More recently learners known as artificial neural networks (ANN) have shown great successes in many machine learning tasks, in particular fitting nonlinear associations. Small learning rate, stochastic gradient descent algorithm and large training set help to cope with the explosion in the number of parameters present in deep neural networks. Yet few ANN learners have been developed and studied to find needles in nonlinear haystacks. Driven by a single hyperparameter, our ANN learner, like for sparse linear associations, exhibits a phase transition in the probability of retrieving the needles, which we do not observe with other ANN learners. To select our penalty parameter, we generalize the universal threshold of Donoho and Johnstone (Biometrika 81(3):425–455, 1994) which is a better rule than the conservative (too many false detections) and expensive cross-validation. In the spirit of simulated annealing, we propose a warm-start sparsity inducing algorithm to solve the high-dimensional, non-convex and non-differentiable optimization problem. We perform simulated and real data Monte Carlo experiments to quantify the effectiveness of our approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Effect of particle size and moisture on flow performance of loblolly pine anatomical fractions: Experimental findings and model predictions

The rising energy demand has highlighted biomass as a promising next-generation energy source. However, commercializing biomass-derived energy faces challenges, particularly in handling biomass feedstock. Factors like particle size, shape, moisture content, and surface roughness significantly impact biomass flowability. This study addresses a crucial knowledge gap by examining the effects of particle size and moisture content on the flow behavior and shear properties of different anatomical fractions of loblolly pine (Pinus taeda). The bulk shear behavior was examined using a Schulze ring shear tester, while flow performance was tested through gravity-driven flow experiments in a variable wedge-shape hopper. Results were incorporated into empirical and machine learning-based flow prediction models to evaluate their accuracy and limitations. The study found that samples with higher moisture content show higher unconfined yield strength. The critical arching distance increased with particle size, e.g., from approximately 13 and 33 mm for 2- and 6-mm whole chips, respectively at a 32-degree inclination angle. Conversely, the flow rate decreased for a given hopper opening as particle size increased. For instance, at a 60-mm hopper opening and a 32-degree inclination angle, the mass flow rates for 2- and 6-mm whole chips were 7.83 and 6.42 tonne/h, respectively. The empirical model consistently overpredicted the mass flow rate for all anatomical fractions, while the machine learning model more accurately predicted the central tendency of flow rate but was insensitive to varying tissue proportions. These novel findings provide comprehensive characterization of anatomical fractions, reveal significant combined effects of particle size and moisture content on biomass flow behavior, and demonstrate a better predictive accuracy of a machine learning model, all of which are useful for optimizing material handling strategies and biomass utilization technologies in the industry.

09 - BIOMASS FUELS↗
DOE OSTI2023

Finding inputs that trigger floating-point exceptions in heterogeneous computing via Bayesian optimization

Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and produce unreliable numerical answers. The state-of-the-art to test for floating-point exceptions in heterogeneous systems is quite limited and solutions require the application’s source code, which precludes their use in accelerated libraries where the source is not publicly available. We present an approach to find inputs that trigger floating-point exceptions in black-box CPU or GPU functions, i.e., functions where the source code and information about input bounds are unavailable. Our approach is the first to use Bayesian optimization (BO) to identify such inputs and uses novel strategies to overcome the challenges that arise in applying BO to this problem. Here, we implement our approach in the XSCOPE framework and demonstrate it on 58 functions from the CUDA Math Library and 81 functions from the Intel Math Library. XSCOPE is able to identify inputs that trigger exceptions in about 73% of the tested functions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

What can data science tell us about finding new superconductors?

Can data science guide researchers toward understanding superconductivity or discover new superconductors? We examine this question in light of a study in this issue of Patterns by Liu et al., who find that the superconducting transition temperature and certain computed energy intervals of the valence band are correlated.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Finding gaps in the national electric vehicle charging station coverage of the United States

Abstract The United States federal government has invested $7.5 billion into charging infrastructure, including the National Electric Vehicle Infrastructure Program, to build fast charging stations along designated highways for long-distance car travel. We develop a consecutive coverage metric to compute the percent of United States roads (traffic-weighted) that are consecutively accessible within 500 miles of each county. We answer (1) what the state of consecutive coverage is in each county and (2) what the increase in coverage is when designated highways receive fast chargers. In 2023, 10% of counties had at least 75% minimum viable coverage. We find that if all designated highways receive fast-charging stations, 94% of United States counties will reach at least 75% fast charger coverage. However, the remaining counties are rural. This demonstrates that federal funding for fast chargers will help connect most—but not all—counties to the national network of continuously accessible charging stations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Finding a needle in a haystack: quantitative HERFD-XRF imaging and HERFD-XANES characterization of trace platinum in gold solidi from the Late Roman and Byzantine Empires

High-Energy Resolution Fluorescence Detection X-Ray Fluorescence (HERFD-XRF) imaging and HERFD X-ray Absorption Near Edge Structure (XANES) spectroscopy are used to quantify and characterize trace platinum (Pt) in gold solidi from the Late Roman and Byzantine Empires. Historically, the elemental analysis of coins has been pivotal in distinguishing authentic artifacts from forgeries, elucidating minting practices, and understanding economic shifts. Notably, a new gold source with high platinum content appeared in the fourth century CE, transforming the Roman economy. Traditional methods struggled to detect platinum due to the overwhelming gold matrix. Here, this study demonstrates the effectiveness of HERFD techniques in resolving this challenge. Three gold solidi, minted between 654 and 659 CE, were analyzed alongside reference gold materials with known Pt concentrations. The HERFD-XRF imaging revealed spatial distributions of platinum, highlighting non-uniformities within the coins. Additionally, HERFD-XANES spectroscopy identified the oxidation states and chemical speciation of platinum. Results demonstrate that platinum in the solidi primarily exists as metallic Pt, with some surface oxidation. The findings align with previous measurements but reveal higher Pt concentrations and significant inhomogeneities. This research confirms the reliability of HERFD methods for quantifying trace elements and provides new insights into the raw material sources and minting techniques of ancient gold coins. The non-destructive nature of this approach allows for extensive analyses, offering valuable data for historical, economic, and archaeological studies. This innovative application of HERFD-XRF imaging and XANES in cultural heritage research underscores the potential for detailed material characterization and conservation, enhancing our understanding of ancient economies and trade patterns.

Van Loon, Lisa L.↗
DOE OSTI2023

Electronic specific heat capacities and entropies from density matrix quantum Monte Carlo using Gaussian process regression to find gradients of noisy data

In this work, we present a machine learning approach to calculating electronic specific heat capacities for a variety of benchmark molecular systems. Our models are based on data from density matrix quantum Monte Carlo, which is a stochastic method that can calculate the electronic energy at finite temperature. As these energies typically have noise, numerical derivatives of the energy can be challenging to find reliably. In order to circumvent this problem, we use Gaussian process regression to model the energy and use analytical derivatives to produce the specific heat capacity. From there, we also calculate the entropy by numerical integration. We compare our results to cubic splines and finite differences in a variety of molecules in which Hamiltonians can be diagonalized exactly with full configuration interaction. We finally apply this method to look at larger molecules where exact diagonalization is not possible and make comparisons with more approximate ways to calculate the specific heat capacity and entropy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗