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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

DOE’s National Solar Thermal Test Facility Operations and Maintenance

This report details operations and maintenance (O&M) activities performed across Fiscal Years 2022 through 2024 in support of the continued capabilities of the National Solar Thermal Testing Facility (NSTTF) at Sandia National Laboratories. The NSTTF O&M project is funded by the U.S. Department of Energy Solar Energy Technologies Office (SETO) to support research activities and testing on behalf of external customers at the facility under award number CPS 38491. During the project period, the NSTTF made progress in the areas of site metrics, site maintenance and utilization tracking, and customer engagement. The O&M project also supported special initiatives including procurement of a heat exchanger for particle concentrating solar thermal processes and a scoping and cost study for refurbishment and repair of component in the NSTTF heliostat field.

14 SOLAR ENERGY↗
DOE OSTI2025

Recertification of the DOE 9978 Type B Package: Lessons L

The history and limited use of the 9978 Type B package history represents a complex case story. Unprecedented delays (from Covid-19) caused the annual maintenance of nine loaded 9978 packages to expire before the actual shipments were initiated. Additionally, with the delays, the content amendment authorizing the Pu and Am standards as authorized content expired, hence additionally requiring a full SARP revision prior to making the shipments

Ketusky, Edward T. [Savannah River National Labora↗
DOE OSTI2025

Direct Radiative Effects of Aerosols at the ARM SGP and TWP Sites (DOE ASR Final Report)

This effort has examined and quantified aerosol direct radiative effects (DREs) and associated uncertainties at the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plain (SGP) and Tropical Western Pacific (TWP) sites under both clear and all sky conditions by taking advantage of the advanced ARM long-term comprehensive measurements of aerosol, cloud, radiation, and atmospheric state. This effort has filled the knowledge gap in aerosol DREs at the SGP and TWP under all sky conditions based on the ARM observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Report for the DOE Office of Science Workshop on Envisioning Frontiers in AI and Computing for Biological Research

Artificial intelligence (AI), machine learning (ML), and high-performance computing (HPC) are poised to transform biological research, spurring innovation in biotechnology and biosystems design. "is transformation will bring an explosion of new capabilities to control the expression of genomic information in living organisms and harness that information to invent new biobased technologies (Jinek et al. 2012; NASEM 2025).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

DOE SCGSR Final Report - Project Accomplishments and Additional Materials

The major accomplishments of my project were successfully developing and testing the laser interferometry diagnostic on the Plasma Liner Experiment (PLX) at Los Alamos National Laboratory (LANL). This diagnostic helped advance my research by providing a simpler way to obtain electron density measurements of magnetized plasmas compared to other electron density diagnostics such as triple Langmuir probes. The laser used was a 561 nm continuous wave (CW) diode-pumped solid state (DPSS) laser. The PLX laser interferometry diagnostic consists of two parts. These include the launching and receiving sides of the laser diagnostic. The launching side consists of the main laser beam from the DPSS laser being split into five chords(probe beams) and a reference beam which are then directed with fiber optic couplers into fiber optic cables which transmit the probe beamsto the PLX vacuum chamber. The probe beams then pass through the plasma in the chamber and into the receiving side optics where they are again directed through fiber optic cables to be combined with the reference beam. The combined beams are transmitted via multi-mode fiber optic cables to photodiodes to convert the light signals into electrical signals. Before the electrical signals get digitized, they pass through bandpass and low pass filters to eliminate electromagnetic noise and unwanted frequencies. The electrical signals are then processed by IQ demodulators to determine phase angle difference between the probe and reference beams for each chord in order to calculate line-integrated electron density.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

An Advanced Machine Learning and Artificial Intelligence System for Demonstrating Radiation Regulatory Compliance in DOE Accelerator Facilities

In this Phase II proposal, Applied Research LLC (ARLLC), Thomas Jefferson National Accelerator Facility (Jefferson Lab), and Old Dominion University (ODU) propose the combination of domain knowledge (beam characteristics, fixed structural shielding, earthen burden (the soil and foliage added to the dome of the experimental halls as additional shielding), etc.), machine learning (ML) and/or artificial intelligence (AI) to correlate a variety of multi-modal onsite signals and the radiation fields seen in accessible areas of the accelerator site and the site boundary. The ML/AI will consider the complex influence of environmental parameters affecting the radon contribution of the measurements, focusing on actual data obtained from Jefferson Lab. In Phase I, the coded beam and location data were fed into a deep learning model to predict doses at several designated locations in Jefferson Lab’s facility. Moreover, a dense radiation map was generated using only a sparse collection of the samples in a facility. In Phase II, we will develop a software prototype containing a radiation prediction algorithm, dense radiation map algorithms, and background noise prediction algorithms, with actual data used to evaluate the prototype. This work will provide a framework for evaluation of radiation measurement results around the site based on learned responses. In addition, the proposed approach allows more granular mapping of radiation levels. Better understanding and communication of these levels is related to the overall approach in keeping doses to personnel ALARA.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Classification of Cloud Particle Imagery and Thermodynamics (COCPIT): A New Databasing Tool for the Characterization of Cloud Particle Images Captured During DOE Field Campaigns

The Department of Energy for decades has explored the earth system and atmosphere through research and deployment of in-situ and remote sensing platforms during field campaigns. Among these datasets exists a vast supply of cloud particle images that provide visual insight into the complex microphysics in the clouds that span our globe. The millions of images collected over decades of deployments provides a unique opportunity to further our understanding of our atmosphere down to the crystal size. This work over the past 5 years has sought to organize these images into digestible datasets that can then be used by scientists to further our understanding of microphysics. A machine learning model was developed that categorizes over 1.5 million images across 11 weather events with over 90% accuracy according to particle type. The database was then extended to include dimensional characteristics of the particle as well as co-location of environmental properties, such as temperature and water content. Then, to initialize the connection between these data and our understanding of how crystals form and grow, weather research and forecasting simulations were run to generate the growth histories of the classified crystals. This research culminates with 2 databases per event: (1) a database of all classified crystals and their dimensional and environmental properties and (2) simulated growth histories of each crystal. Finally, a user interface was created to allow researchers to explore data statistics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Design, Optimization, and Control of a 100 kW Electric Traction Motor Meeting or Exceeding DOE 2025 Targets

The overall objective of the electric motor portion of the Electric Drives Technology consortium is to research, develop, and test electric motors for use in electric vehicle applications capable of a peak power greater than 100 kW, power density greater than or equal to 50 kW/l, and a cost less than 3.3 $/kW. To meet the electric traction motor power density and cost targets a number of approaches were pursued simultaneously throughout the course of this project which address all of the major volumetric power density variables. The specific research thrusts at the Illinois Institute of Technology (IIT) are the following: multiphysics design for increased power density through maximum utilization of active materials, synthesis of electric machine windings and PM flux barriers with controlled space harmonics, high slot fill windings for increased current loadings or efficiency, aggressive cooling strategies, and design studies and prototype construction of candidate electric machines.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Enhancing the Design of Photocathodes with 90% polarization and QE > 1% for DOE NP (Final Report)

This project’s 3 major goals were the following: 1. Fabricate state-of-the-art spin-polarized photocathodes using MOCVD and develop knowledge of fabrication parameters. 2. Enhance the design of 2 structures of spin-polarized photocathodes (strained-superlattice and strained-superlattice with distributed Bragg reflector) to assess paths to the next generation of spin-polarized photocathodes. 3. Provide a supply of state-of-the-art spin-polarized photocathodes to both Brookhaven National Laboratory and the Thomas Jefferson National Accelerator Facility.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Mauka Energy FEVER Tool DOE SBIR Phase 1 Final Scientific/Technical Report

This report is on the Forestry Electric Vehicle Energy Routing (FEVER) Tool, a novel software system developed to support heavy-duty electric vehicle (EV) operations in remote, forested, and mountainous regions. The Phase I project aimed to demonstrate the feasibility of modeling EV energy consumption using terrain elevation, road conditions, and route features specific to forestry logistics. The tool combines geographic information systems (GIS), electric motor physics, and vehicle-specific data to calculate feasible, energy-efficient routes. Collaborations with Oregon State University’s Research Forests and Titan Freight Systems enabled collection and validation of GPS and elevation-based trip data. The FEVER Tool offers substantial opportunities for the efficient management of medium- and heavy-duty electric vehicles in sectors like forestry, agriculture, mining, defense and waste management—areas which are beginning to adopt HDEVs. The project demonstrated technical feasibility and lays the groundwork for commercial development and deployment in other industries and environmental conditions in Phase II.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

DoE as a “Digital Innovation” Sponsor of the WCRP OSC2023 (Final Report)

The WCRP Open Science Conference (https://wcrp-osc2023.org/) was a once-in-a-decade opportunity to jointly explore the transformative actions urgently needed to ensure a sustainable future. Held in Kigali, Rwanda on October 23 -27, 2023, it showcased advances in climate science, helped identify gaps and opportunities, and provided a forum for communities to jointly develop future activities. Scientists, practitioners, politicians, policy makers, intergovernmental agencies and NGOs showcased their work, learned from each other, and explored new ways to work together.

54 ENVIRONMENTAL SCIENCES↗