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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

LLNL FY22 Aging and Lifetimes Exit Criteria (Milestone 8118, GC#5)

This report introduces a novel characterization tool that can rapidly collect in-situ time-dependent oxide thickness and hydride growth data together with spatial data using while-light interferometry. White-light interferometry is a non-destructive and non-contact optical surface profiling technique for materials characterization that offers excellent lateral and vertical resolution. A depleted uranium sample was used to assess the ability of white-light interferometry to provide quantitative corrosion reaction rates at metal surfaces for by hydriding and oxidation and compare this technique to existing characterization tools for capturing early-stage hydride and oxide growth kinetics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Transactive Campus Energy Systems: An R&D Testbed for Renewables, Integration, Efficiency, and Grid Services (CRADA 356 / Amendment 1)

The Clean Energy and Transactive Campus (CETC) work described in this report was done as part of Amendment 1 to Campus Cooperative Research and Development Agreement (CRADA) 356, the Transactive Campus CRADA with the Washington State Department of Commerce (Commerce) between the U.S. Department of Energy’s (DOE’s), Pacific Northwest National Laboratory (PNNL) and the Commerce through the Clean Energy Fund (CEF). The original project team consisted of PNNL, the University of Washington (UW) and Washington State University (WSU), to connect the PNNL, UW, and WSU campuses to construct and operate the testbed as both a regional flexibility resource and as a platform for research and development (R&D) for buildings/grid integration. Building on the foundational transactive system established by the Pacific Northwest Smart Grid Demonstration (PNWSGD), the purpose of the project was to construct the testbed as both a regional flexibility resource and as a platform for R&D on buildings/grid integration and information-based energy efficiency. The testbed supports the integration of renewables and other regional needs, using the flexibility provided by building loads, energy storage, and smart inverters for batteries and photovoltaic (PV) solar systems, at four physical scales: multiple campuses, campus, microgrid and building.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Development of Integrated Education, Training and Research Program with Interdisciplinary Applications II

Plasma models have been formulated including realistic spatial profiles of both flow and radio frequency induced ponderomotive force. With these inclusions the picture of stability of various plasma and fluid instabilities, as expected, changed drastically with ground-breaking consequences. The inhomogeneous parallel flow and the radio frequency waves can actually shown to stabilize turbulence. This is different from the prevalent notion that both parallel flow shear and radio frequency waves are responsible for the excitation (destabilization) of plasma turbulence. This has several ground-breaking consequences:- (1) the stabilization by parallel flow clearly goes against the conventional notion of the origin of ionospheric oscillation which invokes parallel flow destabilization as the origin, (2) the stabilization by parallel flow opens us a new avenue for improved mode formation in fusion devices - which mostly rely on the perpendicular flow shear stabilization for improved mode formation but the perpendicular flow is damped in a tokamak - so the improved mode formed by the parallel flow can sustain longer and has more prospect for ignition, (3) the complete stabilization of the ITG mode (and consequent suppression of transport) not only explain many unknown phenomena in the space physics but it also raises a prospect for transport barrier formation by the RF waves but not by the RF induced flow (as most works suggest) which is never observed in a tokamak of that magnitude to create a barrier. These are indeed ground-breaking consequences.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Project Assessment for Biological and Environmental Research: Report from the BER Advisory Committee

The construction, operation, and stewardship of large-scale scientific user facilities and cutting edge capabilities have been integral to the mission of the U.S. Department of Energy (DOE) Office of Science from its earliest days. To help identify and prioritize new or upgraded facilities critical to scientific innovation over the next 10 years, the Office of Science director issued a charge to the federal advisory committees of six of its program offices in December 2023, including the Biological and Environmental Research (BER) program. The charge letter (see p. ii) asked the advisory committees to: 1. Consider what new or upgraded facilities will be necessary to position the Office of Science at the forefront of scientific discovery. 2. Deliver a short letter report describing each facility in terms of two criteria: (a) the potential to contribute to world-leading science in the next decade and (b) the readiness for construction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Coastal-Urban-Rural Atmospheric Gradient Experiment (CoURAGE) Science Plan

Understanding the mechanisms governing the urban atmospheric environment is critical for informing urban populations regarding the impacts of climate change and associated mitigation and adaptation measures. Earth system (climate and weather) models have not yet been adapted to provide accurate predictions of climate and weather variability within cities, nor do they provide well-tested representations of the impacts of urban systems on the atmospheric environment. These limitations are largely due to limited field data available for testing and development of these models. We will deploy the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s first Mobile Facility (AMF1) to the mid-Atlantic region surrounding the city of Baltimore for the Coast-Urban-Rural Atmospheric Gradient Experiment (CoURAGE). This deployment will create a four-node regional atmospheric observatory network including Baltimore and its three primary surrounding environments – rural, urban, and bay. CoURAGE investigators will study the interactions among the Earth’s surface, the atmospheric boundary layer, aerosols and atmospheric composition, clouds, radiation, and precipitation at each site, and examine how the spatial gradients across the region interact to create the climate conditions in Baltimore. This study will determine the degree to which Baltimore’s atmospheric environment depends on interactive feedbacks in the atmospheric system and the degree to which conditions in Baltimore depend on the surrounding environment. Some topics of interest include how urban land management exacerbates heat waves, the impact of regional mesoscale winds (nocturnal jet, bay breeze) on urban air pollution and cloud cover, and the impact of the urban heat island and aerosol production on heavy precipitation events. Understanding this integrated coast-urban-rural system quantitatively and with good accuracy and precision is critical to informing climate adaptation and mitigation efforts in the city of Baltimore. The understanding gained should be applicable to many similar coastal, mid-latitude urban centers. Another important objective of CoURAGE is to improve the representation of the climate of coastal cities in Earth systems models (ESMs). CoURAGE investigators will use the observations to test current ESMs, identify weaknesses and work towards improved simulations of this complex environment. The ARM core facility will be deployed in the city of Baltimore, complementing the Baltimore Social-Environmental Collaborative (BSEC), a DOE urban integrated field laboratory (UIFL). Ancillary sites will be deployed to rural Maryland northwest of Baltimore, and to the southern end of Kent Island within Chesapeake Bay. The fourth node will be a long-term atmospheric observatory operated in Beltsville, Maryland by Howard University and the Maryland Department of the Environment. Measurements will be conducted for one year, starting in December of 2024. There will be two intensive operational periods (IOPs), one in summer and one in winter, when the ancillary sites will be enhanced with additional balloon launches, tethered balloon system (TBS) operation, and added atmospheric composition measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Hardware Specification and Reference Design for the Low-Cost, Interoperable, User-Centric, Supervisory Controller Kit for Small and Medium Size Commercial Buildings

Commercial buildings are responsible for approximately 20 percent of the total United States energy consumption and greenhouse gas emissions. Over 85 percent of these buildings lack building automation systems to manage the various building systems they have. Many of these buildings are small (< 50,000 square feet), underserved, and use rooftop units (RTUs) for heating, ventilation, and air-conditioning needs. Because these buildings lack proper control systems, they have several operational deficiencies that lead to excess energy consumption. Studies have shown that managing the RTU’s heating and cooling setpoints, schedules, setbacks, and optimal start times can result in 20 to 25 percent reduction in electricity consumption in small and medium commercial buildings (SMBs). In addition, improving the demand flexibility of these buildings will result in additional cost savings for the building owner. To address the needs of the SMBs, the Department of Energy’s Building Technologies Office jointly funded Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) to design, develop, test, and validate a low-cost, interoperable, user-centric, retrofit supervisory controller kit (SC-SMB) that can be used to continuously optimize energy consumption and deliver demand flexibility of SMBs, including all-electric buildings, and provide a means for maximizing decarbonization benefits. The team also includes industry partners Edo and Intellimation. Previously, the team drafted an SC-SMB system specification document (Goodman et al. 2023). This document describes a reference design of a low-cost supervisory control system for SMBs, including the various hardware components, software system, and an example implementation of the reference design. The final release of the reference design is planned for December 2025. Section 2.0 of the report documents the relevant building types that the SC-SMB system is suitable for. Section 3.0 documents the various hardware components, their function, cost, whether they are off-the-shelve, support standard communication, etc., and the software system. An example deployment in a 10,000-sf building with six rooftop units, a hot water heater, solar, and storage is described in Section 4.0. The planned next steps are described in Section 5.0, and references are listed in Section 6.0.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Design Requirements and Hardware Specification for the Low-Cost, Interoperable, User-Centric, Supervisory Controller Kit for Small and Medium Size Commercial Buildings

Commercial buildings are responsible for approximately 20 percent of the total United States energy consumption and greenhouse gas emissions. Over 85 percent of these buildings lack building automation systems to manage the various building systems they have. Many of these buildings are small (< 50,000 square feet), underserved, and use rooftop units (RTUs) for heating, ventilation, and air-conditioning needs. Because these buildings lack proper control systems, they have several operational deficiencies that lead to excess energy consumption. Studies have shown that managing the RTU’s heating and cooling setpoints, schedules, setbacks, and optimal start times can result in 20 to 25 percent reduction in electricity consumption in small and medium commercial buildings (SMBs). In addition, improving the demand flexibility of these buildings will result in additional cost savings for the building owner.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Analyzing the Impact of Future Weather Data on Energy Consumption in Weatherization Assistant

This study supports the mission of the U.S. Department of Energy’s Weatherization Assistance Program (WAP), which aims to increase the energy efficiency of dwellings and reduce their total residential expenditures. Specifically, we examine how projected future climate conditions may affect residential building energy performance by integrating future weather data into the National Energy Audit Tool (NEAT). Since WAP evaluates the cost-effectiveness of retrofit measures over lifespans of up to 30 years, accounting for evolving climate conditions is increasingly important. To reflect future household energy demands, this study replaces historically based Typical Meteorological Year (TMY3) weather inputs with Future Typical Meteorological Year (fTMY) datasets derived from global climate model (GCM) projections. A simulation-based framework was established to enable NEAT analysis under future weather conditions. This workflow involves converting EPW-format weather files into JSON inputs compatible with NEAT and generating degree-hour metrics needed for load calculations. The fTMY dataset used in this study was developed by Oak Ridge National Laboratory through downscaling of six GCMs under different emission scenarios and covers the period from 2020 to 2100. In contrast, the TMY3 dataset is based on historical weather data from 1961 to 1990. Simulations were conducted for benchmark single-family prototype buildings across ASHRAE climate zones 1–7, which cover all regions of the U.S. except the subarctic Zone 8 in northern Alaska, evaluating both heating and cooling loads under TMY3 and fTMY conditions. Four foundation types were tested, while heating systems were standardized, as NEAT does not differentiate thermal energy load by HVAC system type in its load calculations. Results show that fTMY weather input consistently yield lower heating loads and higher cooling loads across most locations, aligning with expected climate warming trends. Notably, colder regions such as zones 6A, 6B, and 7 experience marked reductions in heating load, while warmer and transitional zones, such as 2A (Lufkin, TX) and 3C (San Francisco, CA), have substantial increases in cooling loads. Although this study does not directly assess the performance of retrofit measures under future climate conditions, it provides a critical foundation for doing so. By quantifying shifts in baseline (i.e., pre-retrofit case) energy loads between historical and future weather files, the study highlights the importance of integrating climate-responsive data into audit tools. These findings will inform future efforts to evaluate the long-term effectiveness and cost-effectiveness of weatherization measures under changing climate conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Uncertainty based Online Ensemble on Non-Stationary Data for Fusion Science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior due to drifts in the data. The drifts can arise from both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with non-stationary data streams.Online learning can be used to continuously adapt the models with new data as it is acquired. However, traditional online learning can suffer from short-term performance degradation, as ground truth are not available before making the prediction. To address this challenge, we propose uncertainty aware ensemble approach for online learning. We use Deep Gaussian Process Approximation (DGPA) technique for calibrated uncertainty estimation and use the uncertainty values to guide a meta-algorithm that produces predictions based on ensemble of learners. Moreover, DGPA also provides uncertainty estimation along with the predictions for decision makers. This paper demonstrates that the proposed method outperforms traditional online learning approach, and a naive ensemble without uncertainty guidance by about 7% and 6%, respectively, on B-coil deflection prediction at DIII-D Fusion Facility.

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts.

Schram, Malachi [Thomas Jefferson National Acceler↗
DOE OSTI2020

Forecasting Commercial Building Electricity Consumption, Zone Airflow and Zone Temperature: Update - Development of a Generalized Machine Learning Approach

The U.S. power grid is being transformed to make it smarter, more efficient, and cleaner. This transformation is leading to the addition of a significant of energy generated by distributed, variable, and renewable resources. Because of the variable nature of renewable generation, the short- and long-term supply and demand imbalances are less predictable, and conventional approaches to mitigating the imbalances will be less efficient or cost effective. To address this challenge and to support the mission and the vision of the U.S. Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) Building Technologies Office has developed a Grid-Interactive Efficient Building Strategy. The strategy focuses on simultaneously improving building energy efficiency and supporting reliability and resilience of the electric grid more efficiently and at a lower cost. In addition, EERE and DOE’s Office of Electricity created an initiative led by DOE and supported by the national laboratories under the Grid Modernization Lab Consortium structure to enhance grid modernization. The work reported in this document is part of the first set of projects funded under the initiative to design, develop, and validate scalable transactive control technologies for the commercial buildings sector. Transactive controls requires the ability of individual end-use loads to express flexibility as a function of a transactive signal (e.g., price). Empirical grey- and black-box models have been widely used to express flexibility. Although this approach is generally easy to construct and simple to use, it does not capture non-linear behavior that some end-use loads represent. Therefore, Pacific Northwest National Laboratory (PNNL) with support from Western Washington University conducted this research to explore the use of deep machine learning (ML) techniques. The work reported in this document is limited to forecasting whole building electricity consumption, the zone airflow and the zone temperature predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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

Continual Learning for Production-Level Machine Learning in Particle Accelerators

Particle accelerators operate in complex environments where data distribution can change dynamically, leading to data drifts that significantly challenge Machine Learning (ML) models. These non-stationary conditions often cause ML models to deteriorate in performance, making it difficult to maintain reliable predictions in operation. The primary sources of data drifts are changes in accelerator settings and changes in equipment performance which cannot be measured directly. To bridge this gap between ML development and long-term deployment in operational settings, we identify key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. We will provide a practical guide on selecting the appropriate method given resource constraints and desired stability plasticity trade offs. As a concrete example, we will present a real-world use case for anomaly detection to predict errant beams at the Spallation Neutron Source accelerator, where continual learning has been employed to demonstrate stable performance on drifting data streams. We will present practical challenges, lessons learned, and the results from the deployed ML model.

Rajput, Kishansingh [Thomas Jefferson National Acc↗