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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Virginia's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, Virginia had 5,629,718 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗
DOE OSTI2022

West Virginia's Clean Energy Jobs Potential Through 2030

According to the U.S. Census Bureau, West Virginia had 1,129,785 people in its working population (15 to 64 years of age) in 2019. The graphs below show solar photovoltaic (PV), land-based wind, battery energy storage (BES), and energy efficiency job estimates in 2020, 2025, and 2030. These job estimates do not represent net job creation. Rather, they represent the size of the workforce required to achieve projected national deployment levels of each technology for 2025 and 2030 if the state captures the same proportion of jobs in the sector as it did in 2020.

clean energy↗
DOE OSTI2022

Cooperative Research and Development Agreement between National Energy Technology Laboratory and West Virginia University Research Corporation (Abstract)

The National Energy Technology Laboratory (NETL) and West Virginia University Research Corporation (Participant) will collaborate on the testing of a 3D printed sparger in a temperature and pressure chamber, for the purpose of supporting the “American-Made Geothermal Prize” competition, designed to catalyze manufacturing innovation in the American geothermal industry by harnessing the rapid advances additive manufacturing can provide in improving design, fabrication, and functionality, which is accomplished through a series of prize competitions and the development of a diverse and powerful support network that leverages national laboratories, energy incubators, and other resources from across the United States. The intent is to connect competitor teams with national laboratories that can help accelerate the development of innovative solutions and products. Teams who have won the Set!, Go!, Make! Contests are eligible to utilize vouchers at national laboratories to advance their ideas.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Uncertainty Quantified Machine Learning for Street Level Flooding Predictions in Norfolk, Virginia

Everyday citizens, emergency responders, and critical infrastructure can be dramatically affected by street flooding caused by the increasing tidal levels and major storm events magnified by climate change. Low-level flooding, such as nuisance flooding, continues to increase in frequency, especially in cities like Norfolk, Virginia, which can expect nearly 200 flooding events by 2050 [1]. Recently, machine learning (ML) models have been leveraged to produce real-time predictions based on local weather and geographic conditions. However, ML models are known to produce unusual results when presented with data that varies from their training set. For decision-makers to determine the trustworthiness of the model's predictions, ML models need to quantify their prediction uncertainty. This study applies Deep Quantile Regression (DQR) to a previously published, LSTM-based model for hourly water depth predictions [2], and analyzes its out-of-distribution (OOD) performance.

Goldenberg, Steven↗
DOE OSTI2023

Search for a Singly Strange Hexaquark Using Polarization Data From CLAS12 at Jefferson Lab Virginia

Recently hadron spectroscopy has seen a lot of success. There has been a large number of baryon and meson resonances discovered over the last two decades. There have also been several tetra and pentaquark candidates with experimental evidence but not confirmed. The first serious candidate was the X(3872), a potential tetraquark, leading to this rise in success for exotic hadron physics. This has brought about improvements in Quantum Chromo Dynamics (QCD), our current best description of interactions between quarks and gluons. QCD predicts the existence of hexaquark states, and then the d*(2380) was discovered. This thesis explores the rest of the d* Jp =3+ anti-decuplet by searching for experimental evidence of the ds hexaquark. This is the first analysis of the e'd ¿e'K+d0 s ¿e'K+¿n channel using the Py' measurements of the ¿ as a lens to perform this search. This observable was extracted from ¿ electroproduction events off a liquid deuterium target. From this an upper limit of the Breit-Wigner peak strength was extracted. This was done with the use of the CLAS12 detector system housed at Thomas Jefferson Laboratory in Virginia, USA. These polarization measurements are the first to be made on this data, and this is the first time the ds has been searched for in such a way.

Clash, Geraint↗
DOE OSTI2025

Characterization of Coal Refuse Sites in West Virginia: Stream Loss, Volume Estimation, and Land Cover Analysis for Restoration Feasibility

Coal refuse disposal across Appalachia has resulted in widespread environmental degradation, including stream burial, landscape destabilization, and long-term hydrologic impacts. This study provides a GIS-based spatial characterization of 623 coal refuse sites in West Virginia, evaluating their potential for ecological and economic restoration. Stream loss was quantified through digitization of historic topographic maps, revealing over 194,500 meters of historically present streams lost, with 105,000 meters directly buried beneath refuse piles. Volume estimation using empirical Bayesian kriging and modern LiDAR surfaces indicated approximately 1.96 billion cubic meters of deposited refuse. Land cover analysis based on NAIP-derived classification showed that 48% of the total site area was forested, while 15% remained barren. These results highlight not only the environmental footprint of legacy refuse deposits but also their potential for restoration through stream daylighting and beneficial reuse. Emerging opportunities, including rare earth element recovery and mitigation banking, may offer economic incentives to facilitate reclamation. This work establishes a spatial framework to prioritize refuse sites for restoration, balancing ecological uplift with feasible material management strategies.

58 GEOSCIENCES↗
DOE OSTI2014

Solar Radiation Monitoring of Bluefield State College: Bluefield, West Virginia (Data)

The Historically Black Colleges and Universities (HBCU) Solar Radiation Monitoring Network operated from July 1985 through December 1996. Funded by DOE, the six-station network provided 5-minute averaged measurements of direct normal, global, and diffuse horizontal solar irradiance. The data were processed at NREL to improve the assessment of the solar radiation resources in the southeastern United States. Historical HBCU data available online include quality assessed 5-min data, monthly reports, and plots. In January 1997 the HBCU sites became part of the CONFRRM solar monitoring network and data from the two remaining active stations, Bluefield State College and Elizabeth City State University, are collected by the NREL Measurement & Instrumentation Data Center (MIDC).

14 SOLAR ENERGY↗
DOE OSTI2016

Identifying Energy Savings in Water and Wastewater Plants - West Virginia

Since 1976, Industrial Assessment Centers (IACs) administered by the U.S. Department of Energy have supported small and medium-sized American manufacturers to reduce their energy use and improve their productivity and competitiveness. DOE is now offering up to 50 assessments per year at no cost to industrial or municipal water and wastewater plants.

IAC, West Virginia↗
DOE OSTI2024

Analyzing School Bus Electrification in Richmond, Virginia

School buses are an essential component of the transportation infrastructure, serving as a lifeline for students across the globe. However, the widespread use of diesel school buses has raised concerns about the health impact on millions of students exposed to harmful emissions daily. Recognizing this issue, school districts worldwide are urgently seeking cleaner energy alternatives. Electric school buses emerge as an environmentally friendly and sustainable option, fostering a healthier environment for both students and communities. However, school bus electrification faces the challenges of high upfront cost, cumbersome charging management, and constraints from power grids. To help school bus operators address those challenges, this study presents a data-driven analysis for school bus electrification. This study considered a real-world school bus system in Richmond, VA, and developed a mathematical programming model to analyze the system design, charging strategies, and charging load profiles for the electrification scenario. The study evaluated different charging strategies based on model outcomes, aiming to optimize efficiency and effectiveness. Ultimately, this research generated electric school bus charging demand profiles under various scenarios, shedding light on the feasibility and implications of transitioning to electric-powered school buses.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Application of a new hybrid particle swarm optimization-mixed kernels function-based support vector machine model for reservoir porosity prediction: A case study in Jacksonburg-Stringtown oil field, West Virginia, USA

Porosity is a fundamental property that characterizes the storage capability of fluid and gas-bearing formations in a reservoir. An accurate porosity value can be measured from core samples in the laboratory; however, core analysis is expensive and time consuming. Well-log data can be used to calculate porosity, but the availability of log suites is often limited in mature fields. Therefore, robust porosity prediction requires integration of core-measured porosity with available well-log suites to control for changes in lithology and fluid content. A support vector machine (SVM) model with mixed kernel function (MKF) is used to construct the relationship between limited conventional well-log suites and sparse core data. Porosity is the desired output, and two conventional well-log responses (gamma ray [GR] and bulk density) and three well-log-derived parameters (the slope of GR, the slope of density, and [Formula: see text]) are input parameters. A global stochastic searching algorithm, particle swarm optimization (PSO), is applied to improve the efficiency of locating the appropriate values of five control parameters in MKF-SVM model. The results of SVM with different traditional kernel functions were compared, and the MKF-SVM model provided an improvement over the traditional SVM model. To confirm the advantage of the hybrid PSO-MKF-SVM model, the results from three models: (1) radial basis function (RBF)-based least-squares SVM, (2) multilayer perceptron artificial neural network (ANN), and (3) RBF ANN, are compared with the result of the hybrid PSO-MKF-SVM model. The results indicate that the hybrid PSO-MKF-SVM model improves porosity prediction with the highest correlation coefficient ([Formula: see text] of 0.9560), the highest coefficient of determination ([Formula: see text] of 0.9140), the lowest root-mean-square error (1.6505), average absolute error value (1.4050), and maximum absolute error (2.717).

Geochemistry & Geophysics↗
DOE OSTI2019

Refined Lithostratigraphy of Upper and Middle Devonian Shales in West Virginia

The evolution of the formal lithostratigraphy for the Middle and Upper Devonian strata in the central Appalachian Basin has been complex. The original terminology (Chemung, Portage, Marcellus, Catskill, and others) was assigned in the latter 19th century. However, by the 1930s, the realization that these units changed in nature dramatically when traced laterally resulted in a crisis in stratigraphy that forced the differentiation of chronostratigraphy from lithostratigraphy. Further, the sheer size of the basin creates challenges, as stratigraphic terminology defined by examination along the outcrop belts at the basin margins is difficult to extend into the subsurface due to the distances involved, the different nature of outcrop and subsurface data, the different perspective of workers attempting to extend units either from the north and west or from the east, and the complex facies changes within the strata. As a result, the facies architecture and associated lithostratigraphic nomenclature for many Middle and Upper Devonian units remained unsettled, particularly in the basin center.

Boswell, Ray↗
DOE OSTI2021

Data from Raczka et al., Interactions between microbial diversity and substrate chemistry determine the fate of carbon in soil, from Elizabeth Woods in Morgantown, West Virginia, USA

File types include: Soil respiration as 13C cumulative respiration from soil incubation experiment, qSIP (quantitative stable isotope probing) data that is "18S apes wide" which is the fungal atom percent excess or how much the fungi took up of the labeled litter and "16S apes wide" which is the bacterial atom percent excess or how much the bacteria took up of the labeled litter, metabolomic data from the incubated soil samples called "Metab Soil Unique Filtered", which is the metabolomic data that had an initial filter from the raw data, and lipidomic data from the incubated soil samples called "All lipids soil". These files were used according to methods in Raczka et al. 2021 Scientific Reports. Headers show soil type (Oak, Poplar) and litter type (also called substrate here, which is labeled as no litter (control), poplar, and oak). This research was done to understand interactions between ecosystem-level processes and microbial diversity that impacts the composition of decomposition products that can form stable soil organic matter.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Interpretation of Hydraulic Fractures Based on Microseismic Response in the Marcellus Shale, Monongalia County, West Virginia, USA: Implications for Shale Gas Production

Summary Hydraulic fracturing is critical for extracting shale gas in the subsurface. The treatment technique of multistage hydraulic fracturing is widely used to maximize production. In multistage hydraulic fracturing, not all pumping stages make the same contribution for production, although the designed stimulation process is almost same in every pumping stage. In this study, we characterize the microseismic responses of hydraulic fracturing. Significant variations of microseismic characteristics are observed among the different pumping stages. Pre-existing natural fractures are examined along the horizontal well in selected stages. Combined with the Mohr’s circle analysis, results of the fracture study show that induced hydraulic fractures can be captured by pre-existing natural fractures. Induced hydraulic fractures are simulated by the unconventional fracture model (UFM), and the result reveals that the stress-shadow effect diverts the direction of hydraulic fractures. The diverted hydraulic fracture affects the development of the hydraulic fracture network, which has influence on production. The treatment of two-step pumping is investigated by application of hydraulic diffusivity. Hydraulic fracturing performance and production could be optimized by the treatment of two-step pumping in a single stage. The second pumping creates new fractures and fills the fractures with additional proppants to maintain production for a long duration.

Energy & Fuels↗