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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 OSTI2024

Fusion Materials Research at Oak Ridge National Laboratory in Fiscal Year 2023

The materials science challenge of providing a suite of suitable materials to satisfy the technology to achieve fusion energy is addressed in this ORNL program. The inability of currently available materials and components to withstand the harsh fusion nuclear environment requires development of new materials, and an understanding of their response to the fusion environment. The overarching goal of the ORNL Fusion Materials program is to provide the applied materials science support and materials understanding to underpin the ongoing DOE Office of Science—Fusion Energy Sciences program, in parallel with developing the materials for fusion power systems. In this effort the program continues to be integrated both with the larger U.S. and international fusion materials communities and with the U.S. and international fusion design and technology communities. The excitement of this program comes from the priorities given to this subject in the two recent fusion reviews, by the FESAC and NAS committees. An important element of those recommendations is the support for pivoting the national R&D emphasis to the Fusion Materials and Technologies (FM&T), the long-advocated Fusion Prototypic Neutron Source, and for the Fusion Pilot Plant study that will help focus program direction and efforts. Furthermore, the surge of venture capital investment into the private fusion industry start-ups over the last few years is anticipated to help accelerate all aspects of the fusion energy development. This twelfth annual report of the ORNL (Oak Ridge National Laboratory) Fusion Reactor Materials Program summarizes the accomplishments in Fiscal Year 2023 (FY2023). The year was the first to return to full post-COVID-restriction operations, with students and international assignees no longer impacted by COVID restrictions, as in FY20-21-22. Following the pattern of planning used in this program, work for the year FY2023 focused on having the data and productivity to support a strong presence at the International Conference on Fusion Reactor Materials (ICFRM) 21, organized by Spain and occurred in October 2023. Twenty-nine ORNL-led abstracts were submitted, with all accepted. Four were invited presentations, nine contributed oral, fourteen posters, and two withdrawn due to unforeseen circumstances. Additionally, nine external abstracts with ORNL contributing authors were presented. These will be reported in the FY24 report next year.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Computation of Richardson number and entrainment in a turbulent plume using DNS

This presentation will be presented by Pierre Carlotti (French foreign national) at the 1st European Fluid Dynamics Conference (EFDC1) in Aachen, Germany in mid-September. The work focuses on fundamental questions pertaining to entrainment processes in plumes. I am included as a co-author because Pierre used my previously published data to further develop theoretical estimates.

Carlotti, Pierre↗
DOE OSTI2025

Optimizing Facility Operations by Applying Machine Learning to the Army Reserve Enterprise Building Control System (Final Report)

Thousands of U.S. Department of Defense (DoD) buildings have building automation systems (BASs) and/or advanced meters. Although these systems have a wealth of data, performance optimization requires time and expertise to review and act on that information. Machine learning (ML) can provide automated and actionable insights to controls operators. This demonstration implemented proven ML methods on the Army Reserve Enterprise Building Control System. ML refers to algorithms that “learn” from data and improve their performance on a given task over time. In the buildings domain these tasks range from predicting future energy consumption, to identifying operational issues before faults occur, to optimizing control decisions. To learn, ML requires input data, which – for buildings – typically consists of instrument data such as energy consumption data and subsystem controls information such as set-point temperatures, and context data consisting of information such as the physical location of the building, the area of the building, and the weather. ML models use the relationships learned from the input data to make predictions with new, previously unseen, data. The team was able to investigate and successfully implement the following ML use cases: labeling consumption data as anomalous or non-anomalous; baseline whole-building load prediction (unknown fault status); fault detection (validation not possible); and site prioritization for energy-related projects. Due to the constraints of the project, interventions were not able to be implemented during the demonstration; therefore, assessments of operational cost savings and maintenance avoided could not be performed. The project has been presented at two leading national building conferences and two additional publications to peer-reviewed journals are currently in preparation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

WHOLESCALE - Water & Hole Observations Leverage Effective Stress Calculations And Lessen Expenses (Final Technical Report 2020 - 2024)

The WHOLESCALE acronym stands for Water & Hole Observations Leverage Effective Stress Calculations and Lessen Expenses. The goal of the WHOLESCALE project is to simulate the spatial distribution and temporal evolution of stress in the geothermal system at San Emidio in Nevada, United States. To reach this goal, the WHOLESCALE team has developed a methodology to incorporate and interpret data from four methods of measurement into a multi-physics model that couples thermal, hydrological, and mechanical (T H-M) processes. The WHOLESCALE team has applied this methodology at the San Emidio geothermal field, located ~100 km north of Reno, Nevada in the northwestern Basin and Range province. The WHOLESCALE team includes 30 individuals working at two universities, two national laboratories, and one industry partner. Two master-degree students and five post-doctoral researchers have gained professional experience and earned partial financial support via the WHOLESCALE project. The WHOLESCALE team has taken advantage of the perturbations created by changes in pumping operations during planned shutdowns in 2016, 2021, and 2022 to infer temporal changes in the state of stress in the geothermal system at San Emidio, Nevada, U.S. The WHOLESCALE results support the working hypothesis that increasing pore-fluid pressure reduces the effective normal stress acting across fault zones. During normal operations, pumping in deep production wells decreases fluid pressures and thus increases the effective normal stresses on faults, reducing microseismicity. During planned shutdowns, the cessation of production increases pore-fluid pressure and reduces effective normal stress. The WHOLESCALE products generated during the 4-year period between 2020 and 2024 include: three articles published in the open-access, peer-reviewed scientific literature, two master’s theses, 20 presentations or papers at scientific conferences, and 17 data sets available on public repositories. The WHOLESCALE project has been completed in two phases that included three performance periods separated by two Go/No-go Stage Gate Reviews. Tasks were classified by data type (i.e., Geologic Structure, Borehole, Geodesy, Hydrology, Seismology, and Modeling). The first phase of the project started July 31, 2020 and included ongoing project coordination (Task 1), a project kickoff (Task 2), analysis of existing data (Task 3), development of the initial stress model & deployment design (Task 4), and Go/No-go Decision Point #1 (Task 5). Phase II began with implementing the 2022 deployment (Task 6), followed by Go/No-go Decision Point #2 (Task 7) The remainder of Phase II consisted of analyzing data collected during deployment (Task 8), calibration of the stress model on all observations (Task 9), and the Final Review (August 23, 2024) & Reporting (Task 10).

15 GEOTHERMAL ENERGY↗