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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

End-Use Savings Shapes Upgrade Package Documentation: Wall and Roof Insulation, New Windows, LED Lighting, HP-RTU and ASHP-Boiler

Building on the successfully completed effort to calibrate and validate the U.S. Department of Energy’s ResStock™ and ComStock™ models over the past 3 years, the objective of this work is to produce national data sets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of analysis questions. The goal of this work is to develop energy efficiency, electrification, and demand flexibility enduse load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) upgrade measures, or upgrades. “Measures” refers to energy efficiency variables that can be applied to buildings during modeling. An end-use savings shape is the difference in energy consumption between a baseline building and a building with an energy efficiency, electrification, or demand flexibility upgrade applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

OpenCSP Camera Calibration: Document Version 1.0

This report provides instructions on how to perform an OpenCSP camera lens calibration, which characterizes the inherent distortion in camera-lens systems. This calibration is necessary for many OpenCSP photogrammetry calculations and should be performed individually for every new camera or after adjusting lens settings.

42 ENGINEERING↗
DOE OSTI2024

VA EDH Data Curation Documentation FY24-Q4

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. Health outcomes, including suicide, are typically influenced by both genetics and environmental factors, such as air quality, transportation access, food availability, homelessness, and more. Mental health outcomes are associated with various stressors across socioeconomic, economic, and physical environments. Analyzing the connections between these stressors, covariates, and health outcomes relies on standardized data, which can be integrated into models like the VA’s Recovery Engagement and Coordination for Health, Veterans Enhanced Treatment (REACH VET). The World Health Organization (WHO) defines Environmental Determinants of Health (EDH) as factors like clean air, stable climate, water and sanitation, chemical safety, radiation protection, safe workplaces, sustainable agriculture, healthy urban environments, and nature preservation, all of which are crucial for good health.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Report series: finding of effect and mitigation documentation for the mercury solar photovoltaic array and battery energy storage system, area 23, nevada national security site, nye county, nevada

The U.S. Department of Energy (DOE), National Nuclear Security Administration Nevada Field Office (NNSA/NFO) proposes to install solar photovoltaic (PV) power generation arrays and an associated battery energy storage system (BESS) for the town of Mercury at the Nevada National Security Site (NNSS) in Nye County, Nevada (Figure 1). The purposes of the development of this facility are to support long-term efforts to modernize Mercury and to provide energy-resilient infrastructure and address climate adaptation needs. Because it is within the boundary of the Mercury Historic District (MHD), it is subject to the terms of the Programmatic Agreement Between the National Nuclear Security Administration Nevada Field Office and the Nevada State Historic Preservation Officer Regarding Modernization and Operational Maintenance of the Nevada National Security Site, at Mercury in Nye County, Nevada (hereafter referred to as the Mercury PA). An identification and evaluation report prepared for this undertaking determined that a contributing element to the MHD, the Mercury airstrip (26NY15777), is within the APE (Haynes 2024). The Mercury airstrip was developed following the closure of Camp Desert Rock in 1957 and used until late 1963 or 1964 when the Camp Desert Rock Airport was renovated, and the Mercury Bypass road constructed. The town of Mercury and the immediate surrounding area have been determined eligible for listing in the National Register of Historic Places (NRHP) as the MHD (SHPO Resource No. D230) under Criteria A and C for its importance in supporting nuclear testing and scientific research from 1951 through 1992 (Reed 2019). Originally recorded in 2016, 26NY15777 was determined individually not eligible for listing in the NRHP because it lacked sufficient integrity to convey its significance (Palmer 2016). It was subsequently determined in 2018 to be a contributing element of the MHD in an architectural survey of the district (Palmer 2018) and identified in Appendix C of the Mercury PA as a Category I contributing element. However, as per Stipulation IV.B.2, because the airstrip had already been formally evaluated and determined not to be individually eligible for the NRHP in consultation with the SHPO, this categorization was an error. NNSA/NFO reported this to the SHPO in Haynes 2024 and the SHPO concurred on June 11, 2024 (Reed). Accordingly, the airstrip is a Category II Property for the purposes of complying with the Mercury PA. The Mercury airstrip is a historic property for the purposes of compliance with Section 106 of the National Historic Preservation Act (NHPA) and subject to the stipulations of the Mercury PA.

25 ENERGY STORAGE↗
DOE OSTI2025

LEBT Buncher / Feed Forward / Frequency Generation: System Design Document (SDD)

The LAMP Low Energy Beam Transport (LEBT) transfers a continuous beam at 100 keV from the ion source to RFQ in LEBT. A LEBT buncher imposes an energy tilt to initiate velocy bunching in the chopped beam pulse about 25 ns long to form a short MPEG bunch. One possible option for the LEBT buncher based on a two-gap LC-circuit driven structure was considered, which is similar to the existing LANSCE low-frequncy buncher (LFB). Another possible option is a non-resonant element driven by a pulse-forming-network that provides a single pulse at the repetition frequency of MPEG beam, with a period of 1.8 µs. The LEBT operation is synchronized with the accelerator timing system.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

LEBT Chopper System: System Design Document (SDD)

The Low Energy Beam Transport (LEBT) chopper creates a required beam time structure by chopping out (removing by deflecting from the beam path) unwanted parts of a continuous beam transported from the ion source to RFQ in LEBT. The unchopped parts of the beam propagate through the LEBT, while the deflected parts are deposited on an absorbing target. The chopper system in general consists of a deflecting structure, where the beam-deflecting fields are created, and a pulse generator that feeds this structure with voltage pulses having the required time pattern. The system should turn deflection on and off within a short time interval, because the beam parts within the pulse front and end will be only partially deflected and will form unwanted tails of the beam pulse downstream. This can be achieved with traveling slow-wave chopper structures where the field propagates with the same velocity as the beam, as illustrated in Fig. 1.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

MEBT Bunchers: System Design Document (SDD)

The LAMP Medium Energy Beam Transport (MEBT) transfers bunched beam at the energy 3 MeV from RFQ to the Drift-Tube Linac (DTL) entrance. The beam particles (protons or H- ) in the LAMP MEBT have velocity β = v/c = 0.08, where v is the beam velocity, c is the speed of light. The MEBT bunchers keep beam bunches from spreading longitudinally as they propagate through the MEBT, where some unwanted bunches are removed by a chopper to create a required beam pattern. The MEBT bunchers are RF cavities operating at the frequency 201.25 MHz; possible design options were considered in.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

MEBT Chopper System: System Design Document (SDD)

The Medium Energy Beam Transport (MEBT) chopper removes unwanted beam bunches by deflecting them to a target from a bunched beam transported through the MEBT from RFQ to the Drift-Tube Linac (DTL) entrance. The unchopped bunches propagate through the MEBT to DTL, while the deflected bunches are deposited on a target downstream of chopper. The chopper system consists of a deflecting structure, where the beam-deflecting fields are created, and a pulse generator (pulser) that feeds this structure with voltage pulses having the required time pattern. Ideally, the system should turn deflection on and off in the time interval between the bunches to prevent partially chopped / deflected bunches. This usually requires traveling slow-wave chopper structures where the field propagates with the same velocity as the beam, as illustrated in Fig. 1.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

ComStock Measure Documentation: Ideal Thermal Air Loads

This study provides hypothetical thermal heating and cooling loads for ComStock models representing the U.S. commercial building stock. This measure scenario removes all HVAC models from the baseline ComStock building model and instead uses "ideal air" to meet loads. "Ideal air" can be thought of as an HVAC unit that mixes air at the zone exhaust condition with the specified amount of outdoor air, and then adds or removes heat and moisture at 100% efficiency to produce a supply air stream at the specified conditions. The resulting ideal thermal loads are represented under the "district" fuel type for both heating and cooling and can be found in both annual and timeseries results in the ComStock public dataset. This measure scenario does not represent any real technology or improvement, but rather, serves as a resource for thermal heating and cooling loads for buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

VA Determinants of Health Data Curation Documentation FY25-Q2

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Determinants of Health (EDH) project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Addressing Investment Barriers by Improving Documentation of Sustainable Biomass Resources (Workshop Report)

On May 8, 2025, Oak Ridge National Laboratory (ORNL), in collaboration with IEA Bioenergy Task 43 and the Biofuture Platform, convened an international workshop in Vancouver, Canada to improve the Global Biomass Resource Assessment. This effort addresses investment barriers in the global bioeconomy by improving the transparency, consistency, and usability of biomass supply data. The workshop gathered 38 participants from 11 countries, representing government agencies, academia, and industry. Participants reviewed the status of the biomass dataset, tested the data-sharing platform, and provided direct input on priorities for improvement.

09 BIOMASS FUELS↗
DOE OSTI2025

ResStock Technical Reference Documentation (V.3.3.0)

ResStock™ is the best-in-class building stock energy model for simulating and publishing energy use, utility bills, and greenhouse gas emissions from the residential sector of the U.S. ResStock answers two primary questions: (1) How is energy used in the U.S. residential building stock? and (2) What is the timeseries aggregate impact of energy technologies? Specifically, ResStock quantifies energy use across geographical locations, demographic groups, building types, fuels, end uses, and time of day. Additionally, it details the impact of efficiency or electrification measures: total changes in the amount of energy used by measure; where or in what use cases efficiency or electrification upgrade measures save energy; when or at what times of day savings occur; and which building stock or demographic segments have the biggest savings potential. This model, and the datasets it produces, are foundational to identifying pathways to affordable and equitable decarbonization of the U.S. residential buildings sector.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

ComStock Measure Scenario Documentation: Chiller Replacement

Building on a 3-year effort to calibrate and validate the U.S. Department of Energy's ResStock (TM) and ComStock (TM) models, this work produces national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual energy consumption (at a subhourly resolution) of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. The goal of this work is to develop energy efficiency and demand flexibility end-use load shapes that cover high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to various "what-if" scenarios that can be applied to buildings. An end-use savings shape is the difference in energy consumption between a baseline building (or collection of buildings) and a building with an energy efficiency or demand flexibility measure applied. It results in a time-series profile broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step, as well as annual aggregations. This report describes the modeling methodology for a single end-use savings shape measure - chiller replacement - and briefly introduces key results. The full public dataset can be accessed on the ComStock (TM) data lake or via the Data Viewer at comstock.nrel.gov. The public data set enables users to create custom aggregations of results for their use case (e.g., filter to a specific county).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

VA Determinants of Health Data Curation Documentation FY25-Q3

The U.S. Department of Veterans Affairs (VA) places the health and well-being of our nation’s veterans as its top priority. VA is dedicated to offering timely access to high-quality, evidence-based mental health care that meets the needs of veterans and supports their reintegration into society. One of our core missions is to prevent suicide among veterans through innovative approaches and resources. With funding from the VA Office of Mental Health and Suicide Prevention (OMHSP), the Determinants of Health (EDH) project has developed innovative datasets associated with specific health outcomes, a methodology for transforming spatiotemporal data from one spatial reference (e.g., a 1km grid) to another (e.g., US Census Tracts), and capabilities for modeling health outcomes. These datasets represent an enhancement of the Agency for Healthcare Research and Quality (AHRQ), addressing key gaps by introducing finer spatial resolution (Census Tract) and additional geographical covariates into existing data. The curation and standardization of these datasets is a complex task since they often originate from various sources and are measured at different spatial and temporal resolutions. For example, US Census data products typically use census blocks, block groups, or counties, while data like weather data are available on 1km grids. Some economic data may only be available at the zip code level. In this context, ‘standardized’ means that all datasets share the same spatial extent (e.g., US Census Tract and/or County), and ‘curated’ implies a repeatable process with data provenance and the use of appropriate methodologies for covariate conversion. The Determinants of Health datasets draw from multiple sources, resulting in variables with varying degrees of availability, patterns of missing data, and methodological considerations across different sources, geographies, and years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

ComStock Measure Scenario Documentation: Standard Performance Heat Pump Rooftop Unit With New Windows

Building on a 3-year effort to calibrate and validate the U.S. Department of Energy's ResStock (TM) and ComStock (TM) models, this work produces national datasets that empower analysts working for federal, state, utility, city, and manufacturer stakeholders to answer a broad range of questions regarding their commercial building stock. ComStock is a highly granular, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual energy consumption (at subhourly resolution) of the commercial building stock across the United States. The baseline model intends to represent the U.S. commercial building stock as it existed in 2018. The methodology and results of the baseline model are discussed in the final technical report of the End-Use Load Profiles project. The goal of this work is to develop energy efficiency and demand flexibility end-use load shapes that cover high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to various "what-if" scenarios that can be applied to buildings. An end-use savings shape is the difference in energy consumption between a baseline building (or collection of buildings) and a building with an energy efficiency or demand flexibility measure applied. It results in a time-series profile broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step, as well as annual aggregations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

ComStock Measure Documentation: Photovoltaics With 40% Rooftop Coverage

This study investigates the impact of adding 40% rooftop coverage of photovoltaics to the U.S. commercial building stock. Panels are modeled as higher performance with 21% rated efficiency, 96% inverter efficiency, 1.10 DC/AC ratio,14% system losses, and azimuth/tilt angles that vary by location. Total panel area is modeled at 40% of the roof area for each model. Total rated PV power for a building is based on the calculated total panel area and the assumed efficiency of 21%. This measure is applied to all buildings modeled in ComStock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗