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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Deer Isle and Stonington Energy Reliability Opportunities

The towns of Deer Isle and Stonington, located on the island of Deer Isle in Maine, partnered with the U.S. Department of Energy's Energy Technology Innovation Partnership Project (ETIPP) to examine options to enhance energy resilience, reduce dependence on imported fuels, and mitigate high energy costs. With technical support from the National Laboratory of the Rockies (NLR), Lawrence Berkeley National Laboratory (LBNL), and regional partner the Island Institute, the project focused on evaluation of the local electricity distribution infrastructure, examination of ongoing activities to reduce the frequency of outages, review of the applicable policy and regulatory environment, and identification of potential on-site energy solutions. Key findings revealed the island's reliance on a single distribution line, and limited hosting capacity for additional distributed energy resources. Solar photovoltaics (PV) technology, with and without battery storage, was found to be the most viable technology given economic, technical, and social considerations. While also technically feasible, wind energy faces more difficult siting and less public acceptance compared to PV. Marine energy technologies were determined to be infeasible due to technology immaturity and lengthy permitting timelines. Modeling of potential building-scale PV plus storage systems resulted in successful grant applications for installations at two local facilities. Analysis of community scale energy generation and storage options resulted the local utility, Versant, submitting a grant application for a proposed community-scale battery storage system to increase grid resilience and hosting capacity. This initiative has strengthened the communities' energy planning capabilities and laid the groundwork for future clean energy development.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Hierarchical Bayesian Modeling for Cosmology: Can NPE reliably replace MCMC?

Hierarchical neural posterior estimation has its place Hierarchical Bayesian Modeling (HBM) combined with MCMC algorithms has been shown to provide more robust and accurate inference for real-world phenomena in which nature takes a nested form. However, MCMC-based inference can be computationally expensive, and its performance often suffers for complex posterior geometries. These costs are especially pertinent for HBM. Studies have recently demonstrated the potential for a flexible, expressive, and amortized hierarchical neural posterior estimator (HNPE) built on Normalizing Flows. These studies have mostly been performed on simple datasets, or they focus on a single parameter from each level of the hierarchy. A systematic study analyzing how both hierarchical methods compare for more complex and realistic datasets is necessary before applying HNPE for scientific measurements. Here, we re-explore the theory behind HNPE and conduct comparative numerical experiments of HNPE and MCMC-based HBM methods on real and synthetic data, including strong gravitational lensing simulations. In particular, we use a suite of diagnostics to show trade-offs in terms of accuracy, precision, time to train or sample, reproducibility, and the need for expert domain knowledge. Especially for higher dimensional and complex posteriors, HNPE is expected to drastically improve on time for inference, accuracy, and precision with an upfront training time cost.

Hur, Rachel [Chicago U.] (ORCID:000900089890445X)↗
DOE OSTI2025

Reliable Integration of AI Data Centers at Scale – Analysis, Modeling and Synthetic Data Generation

This report analyzes the power consumption of large dynamic digital loads using the open-source MIT supercloud and SURF datasets. With an emphasis on the MIT data, we calculate important power consumption characteristics to help system operators improve generation planning and resource allocation. We also introduce a rudimentary model for generating synthetic load profiles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Scalable Membrane Electrode Assembly Stack Production for Reliable Thermal Electrochemical Converters (SMART)

This Final Close Out Report documents all activities, progress, and results accomplished under the SMART project, funded by the Department of Energy (DOE), during Budget Period 1 (BP1), covering 10/01/2023 to 07/31/2025. Although official preaward approval was granted on 05/10/2024, contracting delays shifted the award and formal project start to 10/01/2024. In the absence of an official kick-off meeting, early interactions focused on planning for key technical challenges, particularly the design and logistics for the JTEC device test platform.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

BIL GRID-READY WIND: Reliable and Economical Grid Services Design, Implementation, and Demonstration at the Great Pathfinder Wind Power Plant

The GRID-READY WIND project aimed to demonstrate the ability of utility-scale wind power plants (WPPs) to deliver traditional and emerging grid services (GSs) in both grid-following (GFL) and grid-forming (GFM) modes through a centralized control framework, supplemented by local control adjustments at the wind turbine generator as needed. Throughout the project, the team offered recommendations on how the grid services demonstrated with WPPs could be applied to other inverter-based resources (IBRs), such as photovoltaic (PV) systems and battery energy storage systems (BESS), while considering the unique dynamics of these resources arising from their energy generation characteristics. The project sought to enhance the confidence of system operators (SOs) and planners in utilizing WPPs, alongside other IBRs such as PV plants and BESS, to provide essential grid services over extended periods under diverse operating conditions, thereby supporting their integration into the bulk power system.

17 WIND ENERGY↗
DOE OSTI2026

Performance and Reliability Assessment of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) Data Advisor (ADA)

The Atmospheric Radiation Measurement (ARM) User Facility provides one of the world's largest openly accessible repositories of atmospheric observations through the ARM Data Discovery platform. Although the repository contains more than three decades of measurements collected from permanent observatories, mobile facilities, aircraft campaigns, and field experiments, identifying appropriate datasets can be challenging, particularly for new users unfamiliar with ARM instrumentation and datastream organization. To improve data accessibility, the ARM Data Center developed the ARM Data Advisor (ADA), an artificial intelligence-powered assistant designed to facilitate scientific data discovery, dataset interpretation, and user guidance. This report evaluates ADA's performance as a domain-specific scientific assistant using realistic atmospheric science workflows. The evaluation examines five key capabilities: data retrieval and curation efficiency, hallucination resistance, scientific reasoning, response to ambiguous queries, and content retention and session continuity. Representative prompts were developed to simulate typical interactions between researchers and the ARM Data Discovery platform, and ADA's responses were assessed for retrieval completeness, scientific accuracy, consistency, and practical usefulness. In these representative tests, ADA reduced the complexity of discovering and accessing ARM datasets by recommending appropriate datastreams, explaining instrumentation, interpreting metadata, and assisting with data processing workflows. ADA also exhibits strong domain knowledge of atmospheric science terminology and generally resists hallucination by acknowledging unavailable datasets and requesting clarification when appropriate. Overall, the results indicate that ADA represents a promising advancement in scientific data discovery within the ARM User Facility and has considerable potential to improve researcher productivity, particularly for new users and interdisciplinary scientists seeking efficient access to ARM observations.

Salvador, Christian [ORNL] (ORCID:0000000283287777↗
DOE OSTI2022

Best Practices in PEC Water Splitting: How to Reliably Measure Solar-to-Hydrogen Efficiency of Photoelectrodes

Photoelectrochemical (PEC) water splitting, which utilizes sunlight and water to produce hydrogen fuel, is potentially one of the most sustainable routes to clean energy. One challenge to success is that, to date, similar materials and devices measured in different labs or by different operators lead to quantitatively different results, due to the lack of accepted standard operating procedures and established protocols for PEC efficiency testing. With the aim of disseminating good practices within the PEC community, we provide a vetted protocol that describes how to prepare integrated components and accurately measure their solar-to-hydrogen (STH) efficiency (η STH ). This protocol provides details on electrode fabrication, η STH test device assembly, light source calibration, hydrogen evolution measurement, and initial material qualification by photocurrent measurements under monochromatic and broadband illumination. Common pitfalls in translating experimental results from any lab to an accurate STH efficiency under an AM1.5G reference spectrum are discussed. A III–V tandem photocathode is used to exemplify the process, though with small modifications, the protocol can be applied to photoanodes as well. Dissemination of PEC best practices will help those approaching the field and provide guidance for comparing the results obtained at different lab sites by different groups.

08 HYDROGEN↗