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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

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale that has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify highvalue data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies.

58 GEOSCIENCES↗
DOE OSTI2024

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗
NASA NTRS2009

Towards Designing an Integrated Architecture for NEO Characterization, Mitigation, Scientific Evaluation, and Resource Utilization

This poster reviews the planning and design for an integrated architecture for characterization, mitigation, scientific evaluation and resource utilization of near earth objects. This includes tracks to observe and characterize the nature of the threat posed by a NEO, and deflect if a significant threat is posed. The observation stack can also be used for a more complete scientific analysis of the NEO.

Adams, Robert B.↗
DOE OSTI2021

Characterization of Offshore Storage Resource Potential in the Central Planning Area of the Gulf of Mexico

This report contains a brief overview of the several studies that provide the basis for this work and details the reservoir characterization efforts that have been completed to date. Briefly, geological characterization data suggests that the deep-water reservoirs are the result of the interplay between turbidite depositional systems and salt tectonics. Project Partners have estimated the CO 2 storage capacity associated with saline reservoirs and that associated with CO 2 -Enhanced Oil Recovery (EOR) in the state waters of Louisiana. In addition, Project Partners have conducted a detailed analysis of CO 2 storage potential associated with CO 2 -EOR in the central planning area of the GOM. This work estimates that 3,140 million metric tonnes of CO 2 can be stored in optimal reservoirs of the shallow- and deep-water central GOM. A reduced order modeling and machine learning approach is introduced that will be used to evaluate CO 2 storage capacity and plume dynamics for down-selected, reservoir-specific, saline characterization. Last, a brief discussion establishes how the data presented in this report will support future work.

02 PETROLEUM↗
NASA NTRS1990

Biospheres and solar system exploration

The implications of biosphere technology is briefly examined. The exploration status and prospects of each world in the solar system is briefly reviewed, including the asteroid belt, the moon, and comets. Five program elements are listed as particularly critical for future interplanetary operations during the coming extraterrestrial century. They include the following: (1) a highway to Space (earth orbits); (2) Orbital Spaceports to support spacecraft assembly, storage, repair, maintenance, refueling, launch, and recovery; (3) a Bridge Between Worlds to transport cargo and crews to the moon and beyond to Mars; (4) Prospecting and Resource Utilization Systems to map and characterize the resources of planets, moons, and asteroids; and (5) Closed Ecology Biospheres. The progress in these five field is reviewed.

Paine, Thomas O.↗
NASA NTRS2019

Redeployable Sensor Probe for In-Situ Lunar Resource Mapping from Small Landers

The use of in-situ resources in lunar regolith for production of propellant, life support, and construction (e.g. polar water ice, hydrogen, helium-3, and regolith minerals) will enable sustainable robotic and human space exploration and pave the way for commercialization of lunar exploration. Currently, the search for and characterization of resources on the Moon uses orbital datasets and local geological and geophysical surveys to map and characterize potential deposits. To develop efficient ISRU systems, it is essential to find, characterize, and map lunar resources in-situ, at local scales, using deployable, analytical payloads. We have developed a 3 kg, TRL4 scientific payload, MoonSHOT (Moon Subsurface Hydrogen Optical Tool), to characterize and map lunar resources from a small lander or rover.

Sobron, P.↗
DOE OSTI2023

Wave climate and energy resources in American Samoa from a 42-year high-resolution hindcast

Here, this paper presents an analysis of the wave climate and a characterization of the wave energy resources in American Samoa, a U.S. territory covering seven south central Pacific islands and atolls. A numerical wave model based on WAVEWATCH III® and unstructured SWAN was developed, validated, and executed for 1979–2020 to generate a hindcast dataset suitable for resource characterization. Model-data comparisons were performed with measurements collected in situ and from satellite-based altimeters. The model was found to perform well with a bias in significant wave height of -0.14 m and -0.06 m against buoy and altimeters, respectively. The multimodal sea state of American Samoa is investigated by identifying the sources of energy reaching the islands and partitioning the wave spectrum accordingly. The wave resources characterization follows the International Electrotechnical Commission Technical Specifications to be compatible with studies performed for other U.S. regions. The average omnidirectional wave power at 2 km from shore around Tutuila, the main island of American Samoa, is 14 kW/m. Locations east and west of the islands have a more consistent resource throughout the year because the northwest and southwest swells that are dominant during different seasons complement each other.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Characterizing the Geothermal Lithium Resource at the Salton Sea

The energy transition towards a more sustainable and renewable future is a pivotal global endeavor. Central to this shift for the United States is the critical role of domestically sourced lithium, a key mineral in the production of high-performance batteries essential for electric vehicles and renewable energy storage systems. This has driven the United States to invest heavily in a domestic supply chain for batterygrade lithium to enhance energy security, reduce supply chain vulnerabilities, and foster economic growth by tapping into local resources. A notable example is the Biden Administration’s “American Battery Materials Initiative,” which was included in the $2.8-billion Bipartisan Infrastructure Law (The White House, 2022).

25 ENERGY STORAGE↗
DOE OSTI2025

Regime Characterization of Offshore Wind Resource Using Unsupervised Learning

Predictability of wind resource conditions is critical for offshore wind design and operations. While many studies of extreme wind conditions focus on specific events such as low-level jets or ramps, these rely on threshold definitions that limit generality. Here we present a data-driven framework that combines principal component analysis (PCA), self-organizing maps (SOM), and k-means clustering to classify wind resource conditions as typical and anomalous from climatological data. Anomalies are defined not by fixed thresholds but by flagging samples located far from SOM node centers inside the baseline SOM structure. This reframes extremes as rare ebents and hence, likely difficult to anticipate by numerical weather prediction models. We applied this approach to 23 years (2000–2022) of hourly profiles from the NOW-23 hindcast model at the Humboldt Wind Energy Area. Classification is conducted on a feature space consisting of 10 m wind speed and direction, bulk shear and veer across 30–270 m, and a low-level jet index. Dimensionality reduction is achieved through PC. A 2 × 3 OM lattice trained on the PCA vectors identified six baseline regimes spanning weak to strong flow states. High quantization-error profiles are identified and re-clustered into four anomalous regimes. The baseline regimes exhibited clear seasonal and diurnal cycles. Meanwhile, the anomalous regimes represented <10 % of all hours but showed distinct combinations of speed, shear, and veer, when compared to the baseline regimes. Anomalous regimes are typically short-lived (~few hours), yet their transitions can lead to hub-height wind changes of −18 to +9 m s -1 . For a representative 15 MW turbine, these shifts imply rapid swings in capacity factor from near-full output to negligible generation. Validation with lidar buoy data showed 51% agreement in SOM labels across ~6,000 overlapping hours, with most mismatches confined to adjacent speed classes. HRRR comparisons further revealed that anomalous regimes were disproportionately associated with forecast biases exceeding 5 m s -1 . Together, these results reframe extremes in offshore wind from absolute maxima or minima to weather states that are difficult to anticipate from models.

17 WIND ENERGY↗
DOE OSTI2021

Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble

To accurately plan and manage wind power plants, not only does the time-varying wind resource at the site of interest need to be assessed but also the uncertainty connected to this estimate. Numerical weather prediction (NWP) models at the mesoscale represent a valuable way to characterize the wind resource offshore, given the challenges connected with measuring hub-height wind speed. The boundary condition and parametric uncertainty associated with modeled wind speed is often estimated by running a model ensemble. However, creating an NWP ensemble of long-term wind resource data over a large region represents a computational challenge. Here, we propose two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup in which a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired long-term period (20 year). We quantify hub-height wind speed boundary condition and parametric uncertainty from the short-term model ensemble as its normalized across-ensemble standard deviation. Then, we develop and apply a gradient-boosting model and an analog ensemble approach to temporally extrapolate such uncertainty to the full 20-year period, for which only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both of the proposed approaches provide accurate estimates of the long-term wind speed boundary condition and parametric uncertainty across the region (R 2 >0.75), with the gradient-boosting model slightly outperforming the analog ensemble in terms of bias and centered root-mean-square error. At the three offshore wind energy lease areas in the region, we find a long-term median hourly uncertainty between 10 % and 14 % of the mean hub-height wind speed values. Finally, we assess the physical variability in the uncertainty estimates. In general, we find that the wind speed uncertainty increases closer to land. Also, neutral conditions have smaller uncertainty than the stable and unstable cases, and the modeled wind speed in winter has less boundary condition and parametric sensitivity than summer.

17 WIND ENERGY↗
NASA NTRS2008

RESOLVE Projects: Lunar Water Resource Demonstration and Regolith Volatile Characterization

To sustain affordable human and robotic space exploration, the ability to live off the land at the exploration site will be essential. NASA calls this ability in situ resource utilization (ISRU) and is focusing on finding ways to sustain missions first on the Moon and then on Mars. The ISRU project aims to develop capabilities to technology readiness level 6 for the Robotic Lunar Exploration Program and early human missions returning to the Moon. NASA is concentrating on three primary areas of ISRU: (1) excavating, handling, and moving lunar regolith, (2) extracting oxygen from lunar regolith, and (3) finding, characterizing, extracting, separating, and storing volatile lunar resources, especially in the permanently shadowed polar craters. To meet the challenges related to technology development for these three primary focus areas, the Regolith and Environment Science and Oxygen and Lunar Volatile Extraction (RESOLVE) project was initiated in February 2005, through funding by the Exploration Systems Mission Directorate. RESOLVE's objectives are to develop requirements and conceptual designs and to perform breadboard concept verification testing of each experiment module. The final goal is to deliver a flight prototype unit that has been tested in a relevant lunar polar environment. Here we report progress toward the third primary area creating ways to find, characterize, extract, separate, and store volatile lunar resources. The tasks include studying thermal, chemical, and electrical ways to collect such volatile resources as hydrogen, water, nitrogen, methane, and ammonia. We approached this effort through two subtasks: lunar water resource demonstration (LWRD) and regolith volatile characterization (RVC).

Source record↗
DOE OSTI2022

Chapter 5: Advanced Characterization and Testing of Grid-Forming Resources

In order to compare and define grid-forming (GFM) resources' performance requirements, uniform test procedures are necessary to characterize these resources' capabilities to serve various system needs. Most tests performed on grid-following (GFL) inverters are also required for GFM inverters, with additional tests also needed specifically for GFM inverters. This section describes advanced characterization and testing methods for GFM inverters, with the goal of serving as a guide- line for developing test procedures.

grid-forming resources↗
DOE OSTI2022

Offshore reanalysis wind speed assessment across the wind turbine rotor layer off the United States Pacific coast

Abstract. The California Pacific coast is characterized by considerable wind resource and areas of dense population, propelling interest in offshore wind energy as the United States moves toward a sustainable and decarbonized energy future. Reanalysis models continue to serve the wind energy community in a multitude of ways, and the need for validation in locations where observations have been historically limited, such as offshore environments, is strong. The U.S. Department of Energy (DOE) owns two lidar buoys that collect wind speed observations across the wind turbine rotor layer along with meteorological and oceanographic data near the surface to characterize the wind resource. Lidar buoy data collected from recent deployments off the northern California coast near Humboldt County and the central California coast near Morro Bay allow for validation of commonly used reanalysis products. In this article, wind speeds from the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), the Climate Forecast System version 2 (CFSv2), the North American Regional Reanalysis (NARR), the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5), and the analysis system of the Rapid Refresh (RAP) are validated at heights within the wind turbine rotor layer ranging from 50 to 100 m. The validation results offer guidance on the performance and uncertainty associated with utilizing reanalyses for offshore wind resource characterization, providing the offshore wind energy community with information on the conditions that lead to reanalysis error. At both California coast locations, the reanalyses tend to underestimate the observed rotor-level wind resource. Occasions of large reanalysis error occur in conjunction with stable atmospheric conditions, wind speeds associated with peak turbine power production (> 10 m s−1), and mischaracterization of the diurnal wind speed cycle in summer months.

17 WIND ENERGY↗
NASA NTRS2009

Characterizing Space Environments with Long-Term Space Plasma Archive Resources

A significant scientific benefit of establishing and maintaining long-term space plasma data archives is the ready access the archives afford to resources required for characterizing spacecraft design environments. Space systems must be capable of operating in the mean environments driven by climatology as well as the extremes that occur during individual space weather events. Long- term time series are necessary to obtain quantitative information on environment variability and extremes that characterize the mean and worst case environments that may be encountered during a mission. In addition, analysis of large data sets are important to scientific studies of flux limiting processes that provide a basis for establishing upper limits to environment specifications used in radiation or charging analyses. We present applications using data from existing archives and highlight their contributions to space environment models developed at Marshall Space Flight Center including the Chandra Radiation Model, ionospheric plasma variability models, and plasma models of the L2 space environment.

Minow, Joseph I.↗
DOE OSTI2025

Manufacturing Valuable Coal-Derived Products in Southern Appalachia

This project’s objective is to develop and deploy new technologies for manufacturing rare earth elements (REE), critical minerals (CM), and valuable non-fuel, carbon-based products (CBP) from coal and/or coal waste in the SoApp Basin, thus revitalizing distressed SoApp coal communities and reducing reliance on foreign imports of REE and CM. Resource availability and technological advances alone will not ensure this objective is met. The right mix of business climate, market forces, and policy will also be needed. This report identifies the critical technologies, infrastructure, supply chains, human capital, and policy gaps that must be addressed to manufacture REE, CM, and CBP from coal resources and wastes in SoApp The primary goal of the project is to characterize the resource base in the SoApp Basin.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

​Understanding a Stratigraphic Hydrothermal Resource – Geophysical Imaging at Steptoe Valley, Nevada

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The stratigraphic reservoir in northern Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, included data which further highlighted potential resource targets in the basin. Geophysical surveys, complemented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a >100MWe power-capable reservoir is likely economically accessible using conventional well placement and stimulation techniques in the Paleozoic carbonates of the deep/central basin of northern Steptoe Valley. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the northern Steptoe resource. The geophysical tools, interpretations, lessons learned, and public data generated by this study establish an exploration methodology to inform decisions for characterization and development of northern Steptoe Valley and other stratigraphic geothermal reservoirs in the western U.S.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Steptoe Valley NV Data Compilation: Understanding a Stratigraphic Hydrothermal Resource through Geophysical Imaging

Sandia National Laboratories partnered with a multi-disciplinary group of subject matter experts to evaluate a stratigraphic geothermal resource in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. Provided here are a compilation of newly acquired data over the area and select modeling efforts. This encompasses a 3D geological model (inclusive of full Leapfrog files, Leapfrog viewer files, and XYZ data for faults and stratigraphy) with embedded geophysical modeling, controlled-source electromagnetic (CSEM) and magnetotelluric (MT) data packages, aqueous spring geochemistry data, seismic reflection interpretations, and a gravity data package. The stratigraphic reservoir in Steptoe Valley was previously discovered during oil and gas exploration. Subsequent studies, such as the Nevada Play Fairway Analysis, added data which further highlighted potential resource targets in the basin. Geophysical surveys, complimented with refined geologic mapping and geochemical sampling, were deployed to further characterize the resource. The resulting 3D geologic interpretation, conceptual model refinements, and reservoir simulations suggest that a power-capable reservoir is economically accessible in the Paleozoic carbonates of the deep/central basin. Additional geophysical characterization and exploration drilling efforts are recommended to calibrate interpretation and determine where/how to potentially develop the Steptoe resource. The geophysical tools, interpretations, lessons learned, and publicly available data generated by this study establish an exploration methodology to inform decisions for successful development of stratigraphic reservoirs.

15 GEOTHERMAL ENERGY↗