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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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DOE OSTI2020

Panel Session 68: Key EFCOG Actions: Risk Communication, Supply Chain Management, Human Capital and More

Overview on the Energy Facility Contractor Group's (EFCOG) most recent work in partnership with DOE to improve performance and EFCOG's mission is to ensure safety, security, and quality across the US DOE complex. Best practices were highlighted in discussions with US DOE/ NNSA leaders and key project executives. EFCOG's key priorities for 2020 were featured in the panel discussions to include: addressing human capital issues to ensure future mission needs can be met; addressing supply chain issues for nuclear projects; improving the data quality and performance assurance; and improving risk communication and stakeholder relations. EFCOG accomplishes its mission through working groups that provide forums to address common challenges and exchange proven techniques and other management and technical information among member contractors. Panelists with presentations: WM EFCOG Panel Slides (Morgan Smith); EFCOG DOE Supply Chain Initiative (SCI) - Enabling The DOE Mission - (Darrell Graddy); Data Quality and Performance Assurance (JD Dowell); Risk Communication - Different Events - Very Different Responses from the Community (Rick McLeod); Current State of Cybersecurity (Liz Porter)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The NREL Sensor Laboratory: Hydrogen Leak Detection for Large Scale Deployments: Preprint

The NREL Hydrogen Sensor Laboratory was commissioned in 2010 as a resource for sensor developers, end-users, and regulatory agencies within the national and international hydrogen community. The Laboratory continues to provide as its core capability the unbiased verification of hydrogen sensor performance to assure sensor availability and their proper use. However, the mission and strategy of the NREL Sensor Laboratory has evolved to meet the needs of the growing hydrogen market. The Sensor Laboratory program has expanded to support research in conventional and alternative detection methods as hydrogen use expands to large-scale markets as envisioned by the DOE National Clean Hydrogen Strategy and Roadmap. Current research encompasses advanced methods of hydrogen leak detection including stand-off and wide area monitoring approaches for large scale and distributed applications. In addition to safety applications, low-level detection strategies to support the potential environmental impacts of hydrogen and hydrogen product losses along the value chain are being explored. Many of these applications utilize detection strategies that supplement and may supplant the use of traditional point sensors. The latest results of the hydrogen detection strategy research at NREL will be presented.

detection↗
DOE OSTI2026

Cooperative Support of the Fermilab Site’s Natural Areas and Related Programs (CRADA Final Report)

Under the prime contract with the Department of Energy, Fermi Research Alliance, LLC (FRA) is responsible for effective stewardship of the Fermi National Accelerator Laboratory (Fermilab) 6,800-acre site, to assure that Fermilab is renewed and enhanced to meet future mission needs. As the steward of the Fermilab site, FRA is responsible for maintaining the natural areas for the site (e.g., prairie restoration sites, woodlands, etc.), which are essential for protecting the health and safety of personnel working on or visiting the site, while supporting the scientific and educational missions of Fermilab. Fermilab Natural Areas (FNA) is a not-for-profit corporation organized and existing under the laws of the State of Illinois. FNA was founded in 2006 and incorporated in 2008 for the purposes of studying, conserving, and restoring the Fermilab Site’s natural areas in a manner consistent with the science mission of Fermilab and enhancing the awareness and visibility of the natural amenities of Fermilab to neighboring communities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Waste Glass Property Database and Data Qualification Plan

The U.S. Department of Energy vitrification facilities currently employ glass property-composition models to ensure processability of waste streams and acceptability of the final glass products. The efficiency (e.g., process flexibility, reduced down time, and reduced cost) of waste vitrification is directly tied to the size of the waste processing envelope. To increase the size of the processing envelope, a larger database was developed. The database will improve the prediction accuracy of glass properties, leading to higher waste loadings, higher waste throughput, broader process flexibility, and reduced mission life. Some of the data in this expanded database has insufficient quality assurance (QA) rigor for use in nuclear facility operation and glass qualification. A plan was developed to qualify high-value data under the appropriate QA rigor for use at the Hanford Waste Treatment and Immobilization Plant’s High-Level Waste (HLW) Facility. This qualification plan first compares the data coverage over the composition region of interest to Hanford HLW by property. Those studies that significantly improve the data coverage in the Hanford HLW glass composition region were prioritized for qualification activities. Qualification methods including QA equivalence, peer review, data corroboration, and confirmatory testing have been assigned to each high-priority study.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Characterization of the Sulfur-Saturated Melt Versions of the HFG1 Study Glasses

The U.S. Department of Energy (DOE) is responsible for building the Hanford Tank Waste Treatment and Immobilization Plant (WTP) at the Hanford Site in Washington to remediate 55 million gallons of radioactive waste that is temporarily stored in 177 underground tanks. The Office of River Protection (ORP) has requested that the Savannah River National Laboratory (SRNL) contribute to current glass studies to support successful startup of the WTP, due to SRNL’s recognized capabilities and expertise for glass waste form development. As part of this effort, the Pacific Northwest National Laboratory (PNNL) is developing, batching, and fabricating simulated high-level waste (HLW) glasses to generate the associated property data needed to validate or identify areas of potential characterization improvements in the current glass property/ composition models. Currently, the models cover only a small fraction of the waste compositions projected in the Hanford tank farm; therefore, the models must be expanded to include high fluoride compositions to successfully complete the WTP mission. SRNL support of this work is defined in the Task Technical and Quality Assurance Plan (TTQAP). This report provides results from the chemical analyses of a series of sulfur-saturated melt (SSM) versions of simulated nuclear waste glasses fabricated at PNNL. The glasses were selected as part of a broader study of the influence of glass composition on chemical durability, sulfur retention, and other properties. The glasses were designated the High Fluoride Glasses-1 (HLG1).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Product Consistency Test Results for the HFG1 Glasses

The U.S. Department of Energy (DOE) is responsible for building the Hanford Tank Waste Treatment and Immobilization Plant (WTP) at the Hanford site in Washington to remediate 55 million gallons of radioactive waste that is temporarily stored in 177 underground tanks. The Office of River Protection (ORP) has requested that the Savannah River National Laboratory (SRNL) contribute in areas of recognized capabilities and expertise for glass waste form development to support successful startup of the WTP. As part of this effort, the Pacific Northwest National Laboratory (PNNL) is developing, batching, and fabricating simulated high-level waste (HLW) glasses to generate the associated property data needed to validate or identify areas of potential characterization improvement in the current glass property/composition models. Currently, the models cover only a small fraction of the waste compositions projected in the Hanford tank farm; therefore, the models must be expanded to include high fluoride compositions to successfully complete the WTP mission. SRNL support of this work is defined in the Task Technical and Quality Assurance Plan (TTQAP). This report provides the results of the Product Consistency Test (PCT) leachates from the High Fluoride Glasses-1 (HFG1), a series of simulated nuclear waste glasses fabricated at PNNL. The series included quenched (Q) and canister-centerline cooled (CCC) versions of the glasses. The glasses were selected as part of a broader study of the influence of glass composition on chemical durability, sulfur retention, and other properties. These data will be used to validate or identify areas of potential characterization improvements in the current glass property/composition models.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
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 OSTI2023

Cyber-Resilient Distributed Autonomous Energy Grid

The DARPA Assured Micropatching (AMP) program focuses on creating the capability for rapid patching of legacy binaries in mission critical systems leveraging the field of formal methods. NREL's Cyber Resilient TLDRD effort, focuses on leveraging the field of formal methods to develop tools and methods for formally verifiable implementations of security architectures such as Zero Trust Architecture. The presentation at the DARPA AMP PI meeting is primarily inspired by common interests and ongoing work on leveraging Formal Methods for OT cybersecurity.

cybersecurity↗
DOE OSTI2021

The NREL Sensor Laboratory: Status and Future Directions for Hydrogen Detection

The NREL Hydrogen Sensor Laboratory was commissioned in 2010 as a resource for the national and international hydrogen community to ensure the availability and proper use of hydrogen sensors. Since then, the Sensor Laboratory has provided unbiased verification of hydrogen sensor performance for sensor developers, end-users, and regulatory agencies and has also provided active support for numerous code and standards development organizations. Although sensor performance assessment remains a core capability, the mission of the NREL Sensor Laboratory has expanded toward a more holistic approach regarding the role of hydrogen detection and its implementation strategy for both assurance of facility safety as well as for process control applications. Active monitoring for detection of unintended releases has been identified by the NFPA 2 Storage Task Group as a viable approach for improving facility safety and lowering setbacks. The current research program for the Sensor Laboratory addresses both conventional and advanced developing detection strategies in response to the emerging large-scale markets, such as those envisioned by H2@Scale. These emerging hydrogen applications may require alternative detection strategies that supplement and may ultimately supplant the use of traditional sensors for monitoring hydrogen releases. Research focus areas for the NREL Sensor Laboratory now encompasses the characterization of released hydrogen behavior to optimize detection strategies for both indoor and outdoor applications, advanced methods of hydrogen leak detection such as hydrogen wide area monitoring approaches for large scale and distributed applications, the incorporation of active monitoring as a risk reduction strategy to improve safety at hydrogen facilities, and continuing support of hydrogen safety codes and standards. In addition to assurance of safety, detection will be critical for process control applications, such as hydrogen fuel quality verification for fuel cell vehicle applications and for monitoring and controlling of hydrogen-natural gas blend composition.

codes and standards↗
DOE OSTI2022

Operational and Mission Highlights A Monthly Summary of Top Achievements: September 2022

Associate Laboratory Director for Plutonium Infrastructure Mark Anthony and a procurement team visited three glovebox fabricators the week of Aug. 23, 2022, to discuss equipment procurement options and assess the vendors’ production capability, factory conditions, and safety records. The team determined that all three fabricators can produce complex gloveboxes and meet Nuclear Quality Assurance-1 standards.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Lab Scale Mercury Dissolution Testing

Researchers at the Savannah River National Laboratory were requested by Savannah River Mission Completion to perform laboratory testing and modeling designed to understand why the Defense Waste Processing Facility (DWPF) is not collecting elemental mercury (Hg 0 ) despite long boiling times designed to recover Hg 0 . In response to a Technical Task Request, a Task Technical and Quality Assurance Plan was written and approved to authorize this work.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Data Qualification Report: SRNL Glass Composition-Properties (ComPro) Database

The Savannah River National Laboratory Glass Composition-Properties (ComPro) database is an extensive database containing pertinent composition and durability data to support the accelerated clean-up mission at the Defense Waste Processing Facility. The activities described in this data qualification report were performed to support the information contained in the database. There were two objectives of the original data qualification process. The first objective was to review supporting documentation to determine if DOE/RW-0333P Quality Assurance Requirements and Description had been implemented during the original work. If the DOE/RW-0333P Quality Assurance Requirements and Description had not been directly implemented during the original work, the second objective was to determine if the controls that were used were adequate to meet the intent of the DOE/RW-0333P Quality Assurance Requirements and Description. The results of these two objectives and the activities performed to support these decisions are described in this document. An assessment of each dataset was made to determine if the data were RW-0333P Compliant, RW-0333P Equivalent or Non-RW-0333P Compliant. The original data qualification was performed in accordance with E7, Conduct of Engineering Manual, Procedure 3.70, Revision 4, Qualification of Data. The specific method that was used was Equivalent Controls as described in E7, 3.70. Revision 2 of this document adds supporting information for the RW-0333P Compliant datasets added to Revision 3 of the database.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Watching the Watchers with Verified Formal-Assurance Tools (Abbreviated Final Report)

The “Watching the Watchers” project studied the problem of establishing assurance cases for tools that are used to assure other things. Specifically, we were interested in understanding the tools and techniques one could apply to software to build an assurance case to evaluate their applicability, difficulty, level of assurance provided, and scalability. To do so we chose a set of use cases of relevance to LLNL and our various DOE and non-DOE partners and developed demonstrators to perform this evaluation. Our key focal point was around additive manufacturing problems and assurance gaps that we identified in the additive manufacturing workflow from start to completion. We also explored other areas related to AI, data analysis, and concurrent programming. Follow-on research is planned to take our prototypes from this project and adapt and mature them to fit LLNL mission applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

EarthShine: Observing our world as an exoplanet from the surface of the Moon

NASA’s return to the Moon coincides with explosive growth in exoplanet discovery. Missions are being formulated to search for habitable planets orbiting other stars, making this the ideal time to deploy an instrument suite to the lunar surface to help us recognize a habitable exoplanet when we see it. We present EarthShine, a technically mature, three-instrument suite to observe the whole Earth from the Moon as an exoplanet proxy. EarthShine data will validate and improve models critical for designing missions to image and characterize exoplanets, thus informing observing strategies for flagship missions to directly image exoplanets. EarthShine will answer interconnected questions in Earth and lunar science, exoplanets, and astrobiology, related to the credo “follow the water.” EarthShine can take advantage of current NASA programs to conduct science from the Moon with low-cost, mature space hardware to reduce risk and assure success. Like the 1968 Apollo Earthrise image of our home planet, lonely in the black sky, the appeal of EarthShine to a multidisciplinary array of researchers in Earth Science, Planetary Science, and astrophysics will maximize both its scientific impact and its impact on the general public.

79 ASTRONOMY AND ASTROPHYSICS↗