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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

ESnet Data and AI Workshop Report

In February 2025, the DOE user facility Energy Sciences Network (ESnet) held a three-day Data and AI Workshop in Berkeley, California. The objective of the workshop was to identify challenges within ESnet that could be addressed through data-driven methods, to help define ESnet’s data-analysis requirements, and to shape its AI strategy, guiding data-stewardship efforts and the direction of AI research and AIOps exploration for ESnet7, the next iteration of ESnet’s network. This report summarizes the multi-faceted discussions and findings and presents a set of recommendations for next steps.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Addressing Investment Barriers by Improving Documentation of Sustainable Biomass Resources (Workshop Report)

On May 8, 2025, Oak Ridge National Laboratory (ORNL), in collaboration with IEA Bioenergy Task 43 and the Biofuture Platform, convened an international workshop in Vancouver, Canada to improve the Global Biomass Resource Assessment. This effort addresses investment barriers in the global bioeconomy by improving the transparency, consistency, and usability of biomass supply data. The workshop gathered 38 participants from 11 countries, representing government agencies, academia, and industry. Participants reviewed the status of the biomass dataset, tested the data-sharing platform, and provided direct input on priorities for improvement.

09 BIOMASS FUELS↗
DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Advancing Additive Manufacturing Through Artificial Intelligence–Powered, High-Throughput, Nondestructive Characterization and Process Optimization

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and ZEISS Industrial Metrology has demonstrated the transformative potential of artificial intelligence (AI)-enabled x-ray computed tomography (XCT) to accelerate the qualification and certification of additively manufactured (AM) parts. At the core of this effort is Simurgh, an AI-powered XCT reconstruction framework jointly advanced by ORNL and ZEISS that integrates computer-aided design (CAD) models, physics-based simulations, and deep learning to overcome the long-standing challenges of metal artifact correction, long scan durations, and limited flaw detectability in dense and geometrically complex components. Simurgh enables high-throughput, high-quality 3D reconstruction from sparse and fast scans, which reduces XCT acquisition times by more than an order of magnitude and simultaneously improves defect detection limits by up to fourfold compared with industry-standard approaches. This capability reduces scan costs by more than 50%, lowers labor overhead, and makes XCT characterization economically viable for routine industrial use. By enabling reliable flaw detection in minutes rather than hours, Simurgh facilitates real-time feedback loops for process parameter optimization, which was highlighted in a recent npj Computational Materials (a Nature journal) issue. In the published study, more than 100 alloy coupons were characterized within a single day. This work represents a tenfold acceleration in the development of novel AM alloys and processes compared with conventional workflows. The ZEISS collaboration has also demonstrated the scalability of Simurgh to diverse application domains, including aerospace, nuclear, automotive, and biomedical components; in these applications, ensuring structural integrity is paramount. By drastically reducing barriers to XCT adoption, this partnership has laid the foundation for digital twins and data-driven certification pipelines and directly addressed bottlenecks in qualifying new materials and designs. Together, ORNL and ZEISS have shown that Simurgh advances the state of the art in nondestructive evaluation and aligns with the broader mission of enabling Industry 4.0 manufacturing ecosystems, in which intelligent, cost-effective, rapid quality assurance is integral to accelerating innovation and ensuring safety in critical applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

NREL - GE 1.5MW SLE Wind Turbine / Reviewed Data

This dataset is intended to be a public resource for anyone conducting wind turbine noise research. It contains noise spectra and equivalent sound pressure levels at 11 measurement stations. The dataset also contains wind turbine operational data and meteorological data from a met mast directly upstream of the wind turbine.

17 WIND ENERGY↗
DOE OSTI2021

Shedding Light on Primary Donors in Photosynthetic Reaction Centers

Chlorophylls (Chl)s exist in a variety of flavors and are ubiquitous in both the energy and electron transfer processes of photosynthesis. The functions they perform often occur on the ultrafast (fs–ns) time scale and until recently, these have been difficult to measure in real time. Further, the complexity of the binding pockets and the resulting protein-matrix effects that alter the respective electronic properties have rendered theoretical modeling of these states difficult. Recent advances in experimental methodology, computational modeling, and emergence of new reaction center (RC) structures have renewed interest in these processes and allowed researchers to elucidate previously ambiguous functions of Chls and related pheophytins. This is complemented by a wealth of experimental data obtained from decades of prior research. Studying the electronic properties of Chl molecules has advanced our understanding of both the nature of the primary charge separation and subsequent electron transfer processes of RCs. In this review, we examine the structures of primary electron donors in Type I and Type II RCs in relation to the vast body of spectroscopic research that has been performed on them to date. Further, we present density functional theory calculations on each oxidized primary donor to study both their electronic properties and our ability to model experimental spectroscopic data. This allows us to directly compare the electronic properties of hetero- and homodimeric RCs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Field Epidemiology Training Program’s Contribution to Essential Public Health Functions in Seven National Public Health Institutes

Objective: This study explores how Field Epidemiology Training Programs (FETP) whose National Public Health Institutes (NPHI) are supported by U.S. Centers for Disease Control and Prevention (CDC) have contributed to strengthening essential public health functions. Methods: We conducted 96 semi-structured interviews with public health experts including NPHI staff, non-NPHI government staff, and staff from non-governmental and international organizations in Cambodia, Colombia, Liberia, Mozambique, Nigeria, Rwanda, and Zambia. We managed data using MAXQDA and employed direct content analysis to derive themes. Results: Three overarching themes emerged in relation to FETPs’ role within the NPHIs’ public health functions. These themes included contribution to improving country surveillance systems, role in providing leadership in outbreak responses, and strengthening countries’ and the NPHIs’ surveillance workforce capacity. Participants also shared challenges around FETPs’ implementation and suggestions for improvement. Conclusion: The results demonstrate the value of FETPs in strengthening public health systems through building workforce capacity and improving surveillance systems. By identifying the successes of FETPs in contributing to essential public health functions, our findings might inform current and future FETP implementation and its integration into NPHIs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Is Nematicity in Cuprates Real?

In La 2-x Sr x CuO 4 (LSCO), a prototype high-temperature superconductor (HTS) cuprate, a nonzero transverse voltage is observed in zero magnetic fields. This is important since it points to the breaking of the rotational symmetry in the electron fluid, the so-called electronic nematicity, presumably intrinsic to LSCO (and other cuprates). An alternative explanation is that it arises from extrinsic factors such as the film’s inhomogeneity or some experimental artifacts. We confront this hypothesis with published and new experimental data, focusing on the most direct and sensitive probe—the angle-resolved measurements of transverse resistivity (ARTR). The aggregate experimental evidence overwhelmingly refutes the extrinsic scenarios and points to an exciting new effect—intrinsic electronic nematicity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Improving the Accuracy of Nearest-Neighbor Classification Using Principled Construction and Stochastic Sampling of Training-Set Centroids

A conceptually simple way to classify images is to directly compare test-set data and training-set data. The accuracy of this approach is limited by the method of comparison used, and by the extent to which the training-set data cover configuration space. Here we show that this coverage can be substantially increased using coarse-graining (replacing groups of images by their centroids) and stochastic sampling (using distinct sets of centroids in combination). We use the MNIST and Fashion-MNIST data sets to show that a principled coarse-graining algorithm can convert training images into fewer image centroids without loss of accuracy of classification of test-set images by nearest-neighbor classification. Distinct batches of centroids can be used in combination as a means of stochastically sampling configuration space, and can classify test-set data more accurately than can the unaltered training set. On the MNIST and Fashion-MNIST data sets this approach converts nearest-neighbor classification from a mid-ranking- to an upper-ranking member of the set of classical machine-learning techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Baryonic Content of Galaxies Mapped by MaNGA and the Gas Around Them

We analyze the cool gas in and around 14 nearby galaxies (at z < 0.1) mapped with the Sloan Digital Sky Survey IV MaNGA survey by measuring absorption lines produced by gas in spectra of background quasars/active galactic nuclei at impact parameters of 0–25 effective radii from the galactic centers. Using Hubble Space Telescope/Cosmic Origins Spectrograph, we detect absorption at the galactic redshift and measure or constrain column densities of neutral (H I, N I, O I, and Ar I), low-ionization (Si II, S II, C II, N II, and Fe II), and high-ionization (Si III, Fe III, N v, and O vI) species for 11 galaxies. We derive the ionization parameter and ionization-corrected metallicity using cloudy photoionization models. The H i column density ranges from ~1013 to ~1020 cm-2 and decreases with impact parameter for r ≳ Re. Galaxies with higher stellar mass have weaker H I absorption. Comparing absorption velocities with MaNGA radial velocity maps of ionized gas line emissions in galactic disks, we find that the neutral gas seen in absorption corotates with the disk out to ~10 R e . Sight lines with lower elevation angles show lower metallicities, consistent with the metallicity gradient in the disk derived from MaNGA maps. Higher-elevation angle sight lines show higher ionization, lower H I column density, supersolar metallicity, and velocities consistent with the direction of galactic outflow. Our data offer the first detailed comparisons of circumgalactic medium (CGM) properties (kinematics and metallicity) with extrapolations of detailed galaxy maps from integral field spectroscopy; similar studies for larger samples are needed to more fully understand how galaxies interact with their CGM.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Raptor

Raptor is an efficient Python-based tool for predicting the formation and morphology of stochastic lack of fusion defects in metal AM processes. A major obstacle for the qualification and certification of additively manufactured parts in critical applications continues to be performance variability caused in part by porosity-related defects. High-fidelity process models that could predict these defect features are currently too computationally expensive for component-level analysis. To address this, Raptor employs a high-performance geometric method to model the dynamic melt pool rather than relying on computationally intensive thermal fluid dynamics. This allows Raptor to rapidly identify regions of unmelted material that correspond to lack of fusion pores. The efficiency of this approach significantly reduces the time and resources needed for generating 3D defect predictions, which enables users to conduct large-scale parameter studies and evaluate how process variations affect part quality. The framework offers operational flexibility; users can execute simulations through a simple command line interface or integrate core functions as a library within larger computational workflows. Simulation outputs include 3D porosity maps for visualization and tools for quantitative morphological analysis. These results are suitable for direct comparison with experimental characterization data from methods such as X-ray computed tomography and can be used for statistical process optimization.

Subraveti, Vamsi [Vanderbilt Univ., Nashville, TN ↗
DOE OSTI2020

Scenario Creation and Power-Conditioning Strategies for Operating Power Grids with Two-Stage Stochastic Economic Dispatch: Preprint

A significant difficultly associated with the use of stochastic programming to solve optimal power flow problems on a 5-minute timescale is the quality of renewable energy scenarios input by the user. This is especially true when considering power systems with high penetrations of renewable energy, e.g. wind power. This paper introduces the use of stochastic programming to solve the DCOPF problem with scenarios drawn directly from high-fidelity data sets. Hence, the proposed method avoids the problem of lost physics by finding high-fidelity analogs that can describe future states of the system. Furthermore, this method can be simply extended to output multi-period scenarios to the stochastic program. We demonstrate the effectiveness of this technique on simulated dispatch operations of the RTS-GMLC over a week.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Scenario Creation and Power-Conditioning Strategies for Operating Power Grids with Two-Stage Stochastic Economic Dispatch

A significant difficulty associated with the use of stochastic programming to solve optimal power flow problems on a 5-minute timescale is the quality of renewable energy scenarios input by the user. This is especially true when considering power systems with high penetrations of renewable energy, e.g. wind power. This paper introduces the use of stochastic programming to solve the DC optimal power flow problem with scenarios drawn directly from high-fidelity data sets. Hence, the proposed method avoids the problem of lost physics by finding high-fidelity analogs that can describe future states of the system. Furthermore, this method can be simply extended to output multi-period scenarios to the stochastic program. We demonstrate the effectiveness of this technique by simulating dispatch operations on a synthetic test system over the course of a week.

analog forecast↗
DOE OSTI2021

Modernization of ORIGEN Library Creation in the SCALE Computer Code

The modernization of SCALE features has been a target for recent SCALE releases. ORIGEN, the activation and depletion code in SCALE, is one such legacy code that has been targeted for modernization. A part of these modernization efforts has been to develop capabilities within ORIGEN to directly read ENDF-formatted data files. In addition, the input format for the COUPLE library creation utility was rewritten to a format more accessible to newer SCALE users. Finally, the ability of COUPLE to create ORIGEN libraries without sublibrary data has been added.KEYWORDS: ORIGEN, depletion, SCALE, API

Hart, Shane↗
DOE OSTI2024

GeoBridge: Connecting Communities to Geothermal Information and Opportunities: Preprint

The geothermal community is well established with long-standing events, organizations, and tools that are known across the geothermal community. But many of these tools and resources are located behind pay walls, require memberships, or are otherwise difficult to find, especially for people looking to join the geothermal community. These barriers to access can prevent outsiders from discovering valuable geothermal resources, limiting the geothermal community's potential for collaboration with other communities, such as clean energy entrepreneurs looking to expand into geothermal energy. The Department of Energy's (DOE) GeoBridge serves to bring these communities together by acting as a single, publicly accessible, searchable portal that facilitates easy access to available geothermal knowledge and information. It works to expand and diversify the pool of geothermal stakeholders by providing in-roads to geothermal information and community resources. It helps build a stronger geothermal community; one inclusive of individuals and groups from a variety of different backgrounds, including potential investors and start-up companies looking to accelerate innovation in geothermal technologies. By linking communities to geothermal information, analysis and expertise, GeoBridge serves as a launch point, directing interested parties to existing data and tools, events, educational resources, STEM programs, permitting and regulatory information, and other resources that can be used to evaluate, promote, and discover geothermal opportunities.

access↗
DOE OSTI2024

EVALS: Enhanced VALidation of Advanced Battery Supply Chains

This is an overview of EVALS (Enhanced VALidation of advanced battery Supply chains), funded by the U.S. Department of Energy Vehicle Technologies Office (VTO) and some initial data from a transformational laboratory directed research and development (LDRD) program project funded at NREL. EVALS aims to accelerate the process to bring domestic and allied primary sources of battery materials online, from production to deployment.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

GeoBridge: Connecting Communities to Geothermal Information and Opportunities

The geothermal community is well established with long-standing events, organizations, and tools that are known across the geothermal community. But many of these tools and resources are located behind pay walls, require memberships, or are otherwise difficult to find, especially for people looking to join the geothermal community. These barriers to access can prevent outsiders from discovering valuable geothermal resources, limiting the geothermal community's potential for collaboration with other communities, such as clean energy entrepreneurs looking to expand into geothermal energy. The Department of Energy's (DOE) GeoBridge serves to bring these communities together by acting as a single, publicly accessible, searchable portal that facilitates easy access to available geothermal knowledge and information. It works to expand and diversify the pool of geothermal stakeholders by providing in-roads to geothermal information and community resources. It helps build a stronger geothermal community; one inclusive of individuals and groups from a variety of different backgrounds, including potential investors and start-up companies looking to accelerate innovation in geothermal technologies. By linking communities to geothermal information, analysis and expertise, GeoBridge serves as a launch point, directing interested parties to existing data and tools, events, educational resources, STEM programs, permitting and regulatory information, and other resources that can be used to evaluate, promote, and discover geothermal opportunities.

access↗
DOE OSTI2023

Hugoniot properties of porous stainless steel: Insights from molecular dynamics simulations

Here, we present a systematic study of Hugoniot properties of porous 316L stainless steel using both a simple interpolation scheme and direct shock simulations in order to probe pore collapse kinetics as well as final thermodynamic states. Both methods indicate that equilibrated Hugoniot properties depend on pore density only and not on the pore distribution or size. We then create a simple porous equation of state model that is shown to be accurate for a range of validation data. This allows us to extend our simulations to make direct comparison to experimental data that have generally significantly larger system sizes and durations. In addition, our direct shock simulations indicate that the relaxation time after hotspot formation is system size dependent and can reach nanosecond timescales for the largest pores investigated in our study, thereby possibly having a measurable effect on fast dynamic loading experiments.

36 MATERIALS SCIENCE↗