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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Where to cool off: a geospatial framework for placement of cooling centers

Indoor cooling is essential to reduce heat stress and increase passive survivability during heatwaves. Although air conditioning (AC) is recommended for maintaining indoor thermal comfort, low- and medium-income households in the U.S. often do not own an AC and/or limit AC usage to reduce energy consumption and associated costs, thereby risking their health and safety. With the frequency and intensity of heatwaves increasing, cooling centers are considered an appropriate alternative to indoor cooling and a possible mitigation strategy to prevent adverse health impacts of heat exposure. However, these centers are limited in numbers and not always accessible. This requires (i) developing a geospatial framework using physical and social factors for optimal siting of cooling centers to meet future needs and (ii) ranking of existing and potential cooling centers (schools, libraries, religious institutions) based on their accessibility among vulnerable populations and proximity to healthcare facilities. We developed and deployed a geospatial framework based on the Multi-criteria Decision Analysis approach in five U.S. cities (Los Angeles (LA), Phoenix, Austin, Atlanta, Miami) to evaluate the effectiveness of the framework in ranking cooling centers based on accessibility and population coverage. The results revealed that (i) access to cooling centers varies across cities and 32.2–50.7% of centers are within walking distance of the most vulnerable populations, (ii) vulnerable populations exposed to Urban Heat Island (UHI) effects are more likely to experience energy burden, and (iii) about 21.2–49.4% of population with high energy burden have access to these centers. Considering that more cooling centers are needed to assist energy burdened households alleviate heat exposure impacts, the framework developed herein could be adapted to incorporate other factors (e.g. health impacts, policies) to assess site suitability of existing shelters, identify potential sites for new cooling centers, and geo-target communities where energy efficient emerging technologies could be deployed to reduce heat stress.

58 GEOSCIENCES↗
DOE OSTI2020

Applying the index of watershed integrity to the Matanuska–Susitna basin

The Matanuska-Susitna Borough is the fastest growing region in the State of Alaska and is impacted by a number of human activities. We conducted a multiscale assessment of the stressors facing the borough by developing and mapping the Index of Watershed Integrity (IWI) and Index of Catchment Integrity (the latter considers stressors in areas surrounding individual stream segments exclusive of upstream areas). The assessment coincided with the borough’s stormwater management planning. We adapted the list of anthropogenic stressors used in the original conterminous United States IWI application to reflect the borough’s geography, human activity, and data availability. This analysis also represents an early application of the NHDPlus High Resolution geospatial framework and the first use of the framework in an IWI study. We also explored how remediation of one important stressor, culverts, could impact watershed integrity at the catchment and watershed scales. Overall, we found that the integrity scores for the Matanuska– Susitna basin were high compared to the conterminous United States. Low integrity scores did occur in the rapidly developing Wasilla–Palmer core area. We also found that culvert remediation had a larger proportional impact in catchments with fewer stressors.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2023

A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR

The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise, and this contribution is projected to accelerate over the next decades. A crucial tool for studying the evolution of surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs), which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. Using the results obtained in the case of the SMB, surface and air temperature are assessed through the comparison of the modeled outputs with in situ and satellite measurement. The downscaled products show a considerable improvement in the case of the downscaled product with respect to the original coarse output, with the coefficient of determination (R 2 ) increasing from 0.868 for the original MAR output to 0.935 for the SMB downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the slope and from the value −235 mm w.e. yr -1 (original) to −57 mm w.e. yr -1 (downscaled) in the case of the intercept, considerably improving upon results previously published in the literature.

Greenland Ice Sheet↗
DOE OSTI2022

Using remote sensing to quantify the additional climate benefits of California forest carbon offset projects

Abstract Nature‐based climate solutions are a vital component of many climate mitigation strategies, including California's, which aims to achieve carbon neutrality by 2045. Most carbon offsets in California's cap‐and‐trade program come from improved forest management (IFM) projects. Since 2012, various landowners have set up IFM projects following the California Air Resources Board's IFM protocol. As many of these projects approach their 10th year, we now have the opportunity to assess their effectiveness, identify best practices, and suggest improvements toward future protocol revisions. In this study, we used remote sensing‐based datasets to evaluate the carbon trends and harvest histories of 37 IFM projects in California. Despite some current limitations and biases, these datasets can be used to quantify carbon accumulation and harvest rates in offset project lands relative to nearby similar “control” lands before and after the projects began. Five lines of evidence suggest that the carbon accumulated in offset projects to date has generally not been additional to what might have otherwise occurred: (1) most forests in northwestern California have been accumulating carbon since at least the mid‐1980s and continue to accumulate carbon, whether enrolled in offset projects or not; (2) harvest rates were high in large timber company project lands before IFM initiation, suggesting they are earning carbon credits for forests in recovery; (3) projects are often located on lands with higher densities of low‐timber‐value species; (4) carbon accumulation rates have not yet increased on lands that enroll as offset projects, relative to their pre‐enrollment levels; and (5) harvest rates have not decreased on most project lands since offset project initiation. These patterns suggest that the current protocol should be improved to robustly measure and reward additionality. In general, our framework of geospatial analyses offers an important and independent means to evaluate the effectiveness of the carbon offsets program, especially as these data products continue improving and as offsets receive attention as a climate mitigation strategy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Basin-Scale Structural Features Database

The Basin-Scale Structural Features database provides spatial datasets of faults, fractures, folds, and earthquakes compiled from public, authoritative sources (e.g., U.S. Geological Survey and State Geological Surveys) and aggregated into derivative forms to support subsurface assessments. Recognizing that characterizing basin-scale structural features requires interpreting data that are often ambiguous or lack key information, the source data were evaluated using a knowledge-data framework and geospatial fuzzy logic method (Justman et al., 2020) to represent both measured (observed) and predicted (inferred or potential) structural features as derivative datasets. This workflow employs conceptual models for known structural features and predicted structural features, incorporating geospatial data to estimate potential, even with limited data. The aim is to aid and support an understanding of basin-scale features and identify potential gaps in data and knowledge. As of 4/30/2025, the database includes resources for nine sedimentary basins: Appalachian, Denver, U.S. Gulf Coast, Illinois, Michigan, Permian, Sacramento, San Joquin and Williston. The database is organized by basin and then data category: 1) Faults, fractures, folds, 2) Earthquakes, 3) Topographic, 4) Structural contours and isopachs, 5) Geophysical, and 6) Structural feature density assessment maps.

basin scale↗
DOE OSTI2024

SIGHT: Stacked Integration of Geospatial Hierarchical Typologies for Inferring Building Characteristics

Building characteristics are often absent in building stock datasets, particularly in regions most vulnerable to climate change and requiring effective disaster management strategies. Traditional machine learning approaches, while widely used to predict building attributes, typically neglect the spatial context of the data, leading to less accurate and reliable outcomes. To address these challenges, this paper introduces a novel algorithm, the Stacked Integration of Geospatial Hierarchical Typologies. This algorithm adapts a meta-learning framework to incorporate geospatial context into the predictive modeling process. We demonstrate the utility of the algorithm through two primary use cases: building use type classification and building height prediction. The algorithm consistently achieved or exceeded a 0.94 macro average F1 score across five geographically distinct countries for building use type classification. For building height prediction, it accurately predicted heights with a root mean square error of 3.01 in a comprehensive study using roughly 3.6 million buildings in Japan. These results underscore the benefits of integrating spatial hierarchies into machine learning models, enhancing both predictive accuracy and reliability in geospatial modeling. This work introduces a new algorithm to address the pervasive data sparsity issue in existing building stock datasets.

Adams, Daniel [ORNL] (ORCID:0000000196950577)↗
DOE OSTI2021

A geospatial environmental and techno-economic framework for sustainable phosphorus management at livestock facilities

Nutrient pollution of waterbodies is a major worldwide water quality problem. Excessive use and discharge of nutrients can lead to eutrophication and algal blooms in fresh and marine waters, resulting in environmental problems associated with hypoxia, public health issues related to the release of toxins and freshwater scarcity. A promising option to address this problem is the recovery of nutrient releases prior to being discharged into the environment. Driven by the sustainable materials management concept, the COW2NUTRIENT (Cattle Organic Waste to NUTRIent and ENergy Technologies) framework is developed for the techno-economic evaluation and selection of nutrient recovery systems at livestock facilities. Furthermore, environmental vulnerability to nutrient pollution determined through a geographic information system (GIS)-based model and techno-economic information of different state-of-the-art nutrient management technologies are combined in a multi-criteria decision analysis (MCDA) model, resulting in the selection and economic analysis of the most suitable process for each studied livestock facility. This framework has been employed for studying the implementation of sustainable phosphorus management systems at 2,217 livestock facilities in the Great Lakes area, resulting in capital expenses of 2.5 billion USD if only phosphorus recovery technologies are installed, and up to 5.2 billion USD if nutrient management is combined with biogas and power production. However, considering potential economic incentives for the recovery of phosphorus, net revenues up to 230 million USD per year can be achieved. Therefore, the framework presented reveals the potential of implementing nutrient management systems at regional scale for the abatement of phosphorus releases from livestock facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning

This project report presents the results from the AU31 project that is entitled “Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning” performed between FY2021 and FY2022. The technical objective of this project is to enhance risk analysis of accidental release of radioactive materials and provide novel risk forecasting and capabilities for assessing unplanned hazardous chemical releases for the U.S. Department of Energy (DOE). The approach includes a cross-platform finite element analysis of airborne chemical release, machine learning (ML) for simulation and forecasting, and a big data approach to data management, transformation and analysis capable of handling petabyte-scale unstructured and structured data. The outcome of these findings were assembled and accessible as a final output via an intuitive web hosted geospatial-based user interface for demonstration and general dissemination. These new results also provide a mechanism of combining this knowledge in a risk analysis framework tool. The geospatial analysis and output can utilize 5 years of qualified meteorological data to illustrate projected plume footprints that are generated from computational aspects of this study. Specifically, we show the development of a geospatial risk analysis capabilities to respond to and plan for accidental release of radioactive materials and to provide a risk forecasting tool for assessing hazardous chemical releases for the DOE. The focus of this effort is germane to common practices in risk analysis; and it is highly relevant to the timely and transparent release of information related to materials at risk including hazardous materials and chemical vapors and rapid assessment of the potential impacts on the workforce at active sites across the DOE complex.

42 ENGINEERING↗
NASA NTRS2019

SERVIR: Leveraging the Expertise of a Space Agency and a Development Agency to Increase Impact of Earth Observation in the Developing World

SERVIR is a joint initiative of the National Aeronautics and Space Administration (NASA) and the U.S. Agency for International Development (USAID), in collaboration with leading technical organizations around the world-- called SERVIR hubs--that serve and empower developing countries to use satellite data addressing critical challenges in food security and agriculture; water and water-related disasters; land cover, land use and ecosystems; and weather and climate. Over the past fourteen years, the program has worked with stakeholders in 50 countries across the world, partnered with 390 institutions, and generated and shared over 70 products from 27 satellites and sensors. In that process, around 7,400 specialists have been trained in the application of Earth observation data and technology. In its lifetime, SERVIR has been agile and innovative in shifting from what was essentially an incubator for testing and deploying Earth observation science and technology to making co-development the hallmark of its work, exemplified by both South-South and North-South scientific collaborations. SERVIR’s approach has embodied the concept that to solve really big problems, big, creative solutions are needed. SERVIR represents the world working together to address environmental challenges using spaced-based and geospatial technologies. Aligning with the very meaning of SERVIR, i.e. “to serve,” the program continues to be demand-driven in developing and deploying services (versus one-off products) which address development challenges using geospatial tools and Earth observation science. In 2016, as part of SERVIR’s evolution, USAID and NASA released the ‘SERVIR Service Planning Toolkit,’ a guidance document which provides a framework for how geospatial services can be used to tackle development challenges in a sustained manner. Since then, the Service Planning Toolkit’s systematic approach has begun to catch on in other Earth observation efforts. To improve access and use, SERVIR launched a Service Catalogue in February 2019, a searchable collection of demand-driven geospatial services that use Earth observations to support decision making. SERVIR implementing hub partners –include SERVIR-West Africa at the Agrometeorology, Hydrology and Meteorology (AGRHYMET) Regional Center, in Niamey, Niger; SERVIR-Eastern & Southern Africa at the Regional Centre for Mapping of Resources for Development in Nairobi, Kenya; SERVIR-Hindu Kush Himalaya at the International Centre for Integrated Mountain Development in Kathmandu, Nepal; SERVIR-Mekong at the Asian Disaster Preparedness Center in Bangkok, Thailand; and SERVIR’ -Amazonia, at the International Center for Tropical Agriculture (CIAT) in Cali, Colombia.

Searby, Nancy D.↗
DOE OSTI2024

T3CO (Transportation Technology Total Cost of Ownership) Open Source [SWR-21-54]

T3CO (Transportation Technology Total Cost of Ownership), is open source software for modeling total cost of ownership for commercial vehicles with advanced powertrains. T3CO is a modeling framework for determining geospatially and temporally optimized total cost of ownership (TCO) for vehicle powertrain technologies. T3CO runs NREL's FASTSim™ software for a representative set of operating conditions to minimize TCO based on vehicle parameters that affect purchase and operating costs (e.g., fuel/electricity consumption, asset depreciation, opportunity costs associated with charging time) while simultaneously ensuring that firm performance constraints (e.g. zero-to-sixty time, gradeability) are satisfied. T3CO will enable the user to control which powertrain parameters are used in optimizing TCO, and these parameters will be modified by a multi-objective optimization (MOO) algorithm to identify a Pareto-optimal solution set. The optimization algorithm will be modular so that users can choose from many different MOO options or insert their own user-defined optimization tool. NREL T3CO Homepage: https://www.nrel.gov/transportation/t3co.html PyPI package: https://pypi.org/project/t3co/

Lustbader, Jason↗
NASA NTRSDate not supplied

Natural Language Processing for Extracting Rich Disease Data Aligned To Satellite Meteorological Data

Global climate change is redefining our understanding of how diseases spread. In Sri Lanka, vector-borne diseases such as dengue fever, encephalitis, and leptospirosis historically surged during the monsoon seasons when temperatures were high enough for mosquito eggs to hatch. Unfortunately, due to rising temperatures and more erratic rainfall patterns, mosquito eggs can now hatch year-round and are increasingly unpredictable, leading to an alarmingly increasing number of hospitalizations and deaths. More data is needed to adapt our response to these diseases in an increasingly warmer world. In the contemporary landscape, a wealth of disease information is available, yet accessibility remains limited due to unstructured data formats such as PDFs. Therefore, converting unstructured disease reports into structured formats is necessary for effectively leveraging data. This paper introduces a comprehensive framework for collecting unstructured disease reports and transforming them into analyzable formats. By creating separate models tailored to each data format, we can ensure accuracy compared to general models. These straightforward models enhance accessibility and empower other researchers to use our tools. The returned structured data can then be harnessed for analysis, statistical purposes, and informing evidence-based public health interventions, thus facilitating more informed decision-making in healthcare. We deploy this framework to produce geospatial data for Sri Lanka and Brazil for many different conditions and align these data with satellite environmental data, providing for the first time a structured, aligned powerful dataset for disease modeling.

Open Source Open Science↗
DOE OSTI2025

VA EDH Advanced Software Pipeline Framework Report: Enhancing Automation and Scalability

The VA Environmental Determinants of Health (EDH) Advanced Software Pipeline Framework is designed to enhance the efficiency, scalability, and security of geospatial data processing workflows. This framework integrates modern data orchestration and containerization technologies, including Prefect for workflow automation, Docker for containerization, and PostgreSQL/PostGIS for geospatial data storage and analysis. It ensures standardized, reproducible, and automated data processing, supporting VA objectives related to substance use risk assessment and recovery research. The pipeline addresses key scalability and performance challenges through horizontal and vertical scaling, high-performance computing (HPC) integration, parallel processing, task caching, and dynamic resource allocation. These optimizations improve throughput and reduce latency, allowing the system to efficiently manage large and complex datasets. Additionally, security and compliance measures—such as data encryption (SSL), Role-Based Access Control (RBAC), and adherence to GDPR and HIPAA standards—safeguard sensitive information throughout data transmission and storage. A key implementation of this framework includes the automation of shelter list geolocation workflows, ensuring that up-to-date data is readily available for VA decision-making. Lessons learned from this project include the transition from in-memory processing to incremental storage writes, improving resource management and reliability. Future enhancements aim to expand automation, integrate AI-driven anomaly detection, and incorporate high-performance computing resources. This framework provides a scalable, secure, and adaptable solution for managing geospatial datasets, reinforcing the VA’s ability to support clinical and strategic initiatives through data-driven decision-making.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

GAPs (Geospatial Analysis Pipelines)

Geospatial Analysis Pipelines (GAPs) is a framework designed to scale single-location geospatial models to a High-Performance Computing (HPC) environment.

Pinchuk, Pavlo (Paul)↗
NASA NTRS2021

A Geodetic and Positioning Thematic Layer - Identifying tools to connect the GGRF and IGIF

Effective and sustainable modernization of a nation’s geodetic framework relies on the ability of the relevant organizations and other stakeholders to communicate, integrate, and align both their strategic objectives and operational planning with the United Nations Committee of Experts on Global Geospatial Information Management (UN GGIM) Sub-Committee on Geodesy’s (SCoG) roadmap for a Global Geodetic Reference Frame (GGRF) for Sustainable Development. In addition, to implement geodetic modernization initiatives through multiple government agencies, a holistic country action plan (CAP) for geospatial information management must incorporate pertinent geodetic outcomes and outputs. Presently, the CAP framework and principles used by nations is the UN GGIM Integrated Geospatial Information Framework (IGIF). From engagement with geospatial and survey communities across emerging nations in the Asia Pacific region, it is evident more assistance and coordination is necessary to articulate, integrate and connect geodetic organizational strategies with the requirements of the GGRF roadmap; the strategic pathways of the IGIF; and resourcing for a meaningful and relevant multi-faceted CAP. One of the supporting mechanisms for such planning or preparation, which representatives of the International Federation of Surveyors (FIG) Asia Pacific Capacity Development Network (AP CDN) and the UN SCoG Education, Training and Capacity Building (ETCB) working group are considering, is a policy framework and guide for a “Geodetic and Positioning Thematic Layer” (GPTL). Essentially, this “thematic layer”, in terms of the GGRF and IGIF, aims to provide a comprehensive understanding of, and a toolbox for, the “why, what, how, and who” of geodesy and positioning. This discussion paper will provide background and insights for a GPTL dedicated to recognizing, and aligning the geodetic capacity development needs with broader geospatial information management issues and applications. The paper will also outline a rigorous, participatory, and inclusive consultation process for the design and development of a thematic layer. Furthermore, as the intention of the authors is to prepare a forthcoming “white paper” that will concisely describe the issues of a GPTL and initiatives for an appropriate guide and/or policy framework, this paper will seek feedback on: the potential scientific, social, environmental and political benefits of modernizing geodetic infrastructure and systems; the challenges associated with the GGRF roadmap; leveraging the geodesy-relevant elements of each IGIF strategic pathway; the importance of collaborative efforts; and the capacity development needs and resources in relation to governance, technology and people.

SARIB, Rob↗