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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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

U.S. Department of Energy/Argonne National Laboratory Training Courses for Packaging, Transportation, and Storage of Radioactive Material - 17269

The U.S. Department of Energy (DOE) Packaging Certification Program (PCP), Office of Packaging and Transportation, Office of Environmental Management, has sponsored a suite of training courses that have been conducted since the early 1990s. Of these training courses, Argonne National Laboratory (Argonne) conducts three courses: (1) Quality Assurance (QA) for Radioactive Material Packaging, (2) American Society for Mechanical Engineers (ASME) Pressure Vessel Code for Nuclear Transport and Storage, and (3) Nuclear and Other Radioactive Materials Transport Security. Argonne has been working with the University of Nevada, Reno, to develop an accredited graduate-level program in nuclear packaging. The Graduate Certificate in Nuclear Packaging (GCNP) program provides an applied knowledge-based curriculum in packaging for nuclear and radioactive materials that complements other graduate programs at the University (for additional information, see https://www.unr.edu/degrees/nuclear-packaging/certificate). (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

SRNL Training Courses for Packaging, Transportation, and Storage of Radioactive Material - 17597

The U.S. Department of Energy (DOE) Packaging Certification Program (PCP), Office of Packaging and Transportation has sponsored a suite of training courses that have been conducted since the early 1990s. Of these training courses, the Savannah River National Laboratory (SRNL) is currently leading the effort for two courses: (1) Management of SARP Preparation, and (2) Nuclear and Other Radioactive Materials Transport Security and Leak Testing. This paper provides an overview of both courses. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

A Simplified Fission Product Poison Model for Operator Training

Accurate but simple models of xenon and samarium concentration variation with reactor power changes are desirable to support development of reactor theory fundamentals training material for power or research reactor operators. Generation of plots of xenon and samarium concentrations with time can be made without reliance on the nuclear properties of a particular reactor core if the differential equations governing the time-dependent behavior of xenon and samarium and their precursors are recast in terms of equilibrium values. There is currently no method available for generating realistic plots of fission product behavior during power transients that is accessible to Operations training instructors other than hand drawings or adaption of existing commercial material. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

Refining Absorber Shroud Geometry to Maximize Power Output and Reduce Power Peaking in ATF Test Train

In the wake of the Fukushima Daiichi nuclear power plant accident in 2011, the accident tolerant fuels (ATF) program was initiated to enhance the safety of light-water reactor fuels, placing significant emphasis on cladding. A crucial step for the broad implementation of ATF in commercial reactors involves irradiation testing of the fuel designs. ATF-2D, the latest experiment in the ATF series, is slated to undergo irradiation in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). ATF-2D is a joint effort of the INL with industry partners General Electric Global Research; Framatome; General Atomics; the Japan Atomic Energy Agency; Hitachi-GE Nuclear Energy, Ltd; Global Nuclear Fuel-Japan Co., Ltd; Nippon Nuclear Fuel Development Co., Ltd; and Mitsubishi Heavy Industries, Ltd. The ATF-2D test train design consists of four tiers, each housing six rodlets. The device will be inserted in Loop 2A within the central flux trap of the ATR, and is anticipated to undergo irradiation throughout three 60-day cycles. Typical pressurized water reactor conditions will be emulated during the irradiation. The objective of the work presented here is to dimension neutron-absorbing hafnium (Hf) components surrounding the fuel rodlets, such that the axial power profile is flattened, while simultaneously ensuring that the total fission power output of the entire test train remains below 200 kW.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Refining Absorber Shroud Geometry to Maximize Power Output and Reduce Power Peaking in ATF Test Train

In the wake of the Fukushima Daiichi nuclear power plant accident in 2011, the accident tolerant fuels (ATF) program was initiated to enhance the safety of light-water reactor fuels, placing significant emphasis on cladding. A crucial step for the broad implementation of ATF in commercial reactors involves irradiation testing of the fuel designs. ATF-2D, the latest experiment in the ATF series, is slated to undergo irradiation in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). ATF-2D is a joint effort of the INL with industry partners General Electric Global Research; Framatome; General Atomics; the Japan Atomic Energy Agency; Hitachi-GE Nuclear Energy, Ltd; Global Nuclear Fuel-Japan Co., Ltd; Nippon Nuclear Fuel Development Co., Ltd; and Mitsubishi Heavy Industries, Ltd. The ATF-2D test train design consists of four tiers, each housing six rodlets. The device will be inserted in Loop 2A within the central flux trap of the ATR, and is anticipated to undergo irradiation throughout three 60-day cycles. Typical pressurized water reactor conditions will be emulated during the irradiation. The objective of the work presented here is to dimension neutron-absorbing hafnium (Hf) components surrounding the fuel rodlets, such that the axial power profile is flattened, while simultaneously ensuring that the total fission power output of the entire test train remains below 200 kW.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Accelerating Resonance Searches via Signature-Oriented Pre-training

The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. We introduce a novel experimental method, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), which leverages deep learning to cover an extensive number of boosted final states. Pre-trained on the comprehensive JetClass-II dataset, the Sophon model learns intricate jet signatures, ensuring the optimal constructions of various jet tagging discriminates and enabling high-performance transfer learning capabilities. We show that the method can not only push widespread model-specific searches to their sensitivity frontier, but also greatly improve model-agnostic approaches, accelerating LHC resonance searches in a broad sense.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Leveraging Military Veteran Talent Through SkillBridge Training: Recommendations for the Solar Industry

Solar Energy International (SEI), in partnership with IREC, has published a white paper, Leveraging Military Veteran Talent Through SkillBridge Training: Recommendations for the Solar Industry. This resource presents SkillBridge as a key strategy for solar employers to attract, develop, and retain qualified veteran talent, and provides model training frameworks aligned with four key occupations, including solar installation, operations and maintenance, technical sales, and solar design.

Howes, Megan↗
DOE OSTI2024

Recruiting Military Talent through Solar Ready Vets Training Partnerships Webinar

SkillBridge training programs provide opportunities for service members to gain valuable civilian work experience through career training prior to separation. Employers are invited to join this webinar hosted by Solar Energy Industries Association to learn more about these programs, featuring success stories from across the industry.

Cohen Hall, E'Lon↗
DOE OSTI2025

Development of a New Criticality Safety Training Program for College Students

Nuclear criticality safety (NCS) expertise remains a crucial workforce need within the US Department of Energy (DOE) laboratory complex. To address this challenge, a novel university/laboratory-based nuclear criticality training certificate program is being developed through a collaborative effort between the Georgia Institute of Technology, Texas A&M University, and Oak Ridge National Laboratory. This comprehensive program implements a two-tiered certification approach that combines online theoretical coursework with hands-on experimental training to create a sustainable pipeline of nuclear criticality specialists. The program specifically targets undergraduate and graduate students in engineering, physics, and mathematics disciplines across the United States. Through integration of fundamental nuclear physics principles, practical safety applications, and experiential learning opportunities, this initiative aims to establish a standardized pathway for developing the next generation of NCS professionals.

K-Effective↗
DOE OSTI2026

Lessons Learned from Open-Source Software Training Toward Expanding the Fusion Workforce

Within the United States (U.S.), the Fusion Innovation Research Engine (FIRE) Collaboratives seek to accelerate fusion technology development through wide-ranging community-driven research activities that bring together industry, laboratories, research institutes, and academia. Increasing the maturity of key components and systems for future fusion power plants (FPPs) toward commercialization, will, by necessity, increase the need for knowledgeable engineers, designers, and researchers in industry capable of integrating and further improving these technologies within industry FPP concepts. Further, various modeling and simulation packages support these programs and are under-development within them to drive design iteration and the development of FPP digital twins. To derive the greatest benefit from model output and capabilities, training these same specialists will be vital. Thus, the development of effective and accessible software onboarding and professional development opportunities focused on fusion will be key to keeping the pace of growth high in the coming decade and beyond. A similar need exists within the advanced fission reactor community, driven by an ever-growing need for carbon-free baseload power for industrial and data center applications. Within the U.S. and around the world, several open-source packages and frameworks exist to support this endeavor, and two we will highlight here are the Multiphysics Object Oriented Simulation Environment (MOOSE) framework and OpenMC. These packages, notably, are also being utilized in the FIRE Collaboratives program. In this presentation, we will highlight the lessons learned from over 30 years of combined software development and training experience, focused on developing strong technical software foundations within the nuclear workforce, from students to professional engineers & scientists. We will connect this experience to present and emerging needs in the fusion energy community, and, finally, will outline possible paths forward to develop a large, robust, global fusion workforce.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Creating a Training Dataset for Semantic Segmentation of Canal Networks for Irrigation Modernization

Canal infrastructure has provided critical irrigation water to the western United States for over a century. To continue providing vital water resources to the semi-arid West, irrigation systems must undergo maintenance and modernization. Many canal companies are resource-constrained, and because funding opportunities often require detailed knowledge of existing infrastructure, they can struggle to secure financial capital. We address this problem by creating training data for a semantic segmentation deep learning model to map canal networks throughout the western United States. To create a diverse and robust training dataset, we labelled 1-m NAIP imagery with the locations of no canals, wet canals, and dry/vegetated canals. Since creating these datasets is time consuming, we first developed a preprocessing methodology to identify canals within our four study areas. We used NAIP imagery and provided canal centerline data to buffer, standardize, and cluster the imagery, automating the labeling process as much as possible. However, this still required manual cleaning and manual classification of canal type. Challenges arose when canals were interrupted (e.g., road culverts or piped sections) or when nearby features shared similar characteristics (e.g., irrigated fields, trees, and shadows). Combining automated preprocessing with manual refinement produced four detailed canal masks to be used in the semantic segmentation model developed by Richard Tapia.

13 - HYDRO ENERGY↗
DOE OSTI2021

DNTTD (Distributed Non-Negative Tensor Train Decomposition)

The era of exascale computing opens new venues for innovations and discoveries in many scientific, engineering, and commercial fields. However, with the exa flops also come the extra-large high-dimensional data generated by high performance computing. High-dimensional data is presented as multidimensional arrays, aka tensors. The presence of latent (not directly observable) structures in the tensor allows a unique representation and compression of the data by classical tensor factorization techniques. However, the classical tensor methods are not always stable or they can be exponential in their memory requirements, which makes them not suitable for high-dimensional tensors. Tensor train (TT) is a state-of-the-art tensor network introduced for factorization of high-dimensional tensors. TT transforms the initial high-dimensional tensor in a network of three-dimensional tensors that requires only a linear storage. Many real-world data, such as, density, temperature, population, probability, etc., are non-negative and for an easy interpretation, the algorithms preserving non-negativity are preferred. Here, we introduce a distributed non-negative tensor-train and demonstrate its scalability and the compression on synthetic and real world big datasets.

Bhattarai, Manish↗
DOE OSTI2021

General Employee Radiological Training Refresher 47968

This training is designed to refresh knowledge of basic radiological principles and terms, the risks from exposure to ionizing radiation, LANL’s radiological hazards and postings, ALARA methods, your responsibilities during emergencies, and how to obtain your personal radiological dose records.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2024

The Continuum from Energy Codes to Advanced Technologies: A New Approach to Training

In July of 2020, the unamended 2018 IECC became the statewide energy code for the state of Nebraska. This represented a significant energy code advancement over the previous code – the 2009 IECC. To support the implementation of the new code, the Midwest Energy Efficiency Alliance (MEEA), along with in-state partners, including the Nebraska Energy Office and the Nebraska Code Officials Association, applied for and received a FOA award for an integrated and innovative training and education program.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗