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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Feasibility of Development of Geothermal Deep Direct-Use District Heating and Cooling system at West Virginia University Campus-Morgantown, WV

The Morgantown campus of West Virginia University (WVU) is uniquely positioned to host the first geothermal deep direct-use district heating and cooling (GDHC) system in the eastern United States. While much of the eastern United States does not have elevated heat flow, the Morgantown region of West Virginia is unique in exhibiting sufficient temperatures at the depth of a formation, the Tuscarora, which is expected to support a desirable flow rate of geofluid. Temperature and flow rate were identified in the 2006 MIT Future of Geothermal Energy Report to be the two most critical factors in minimizing the cost of geothermal energy. The WVU campus site offers surface demand coupled with the potential subsurface viability. Specifically, the existing district heating and cooling system that is in use year round will be leveraged. Absorption chilling systems are used to cool the campus in the summer and provide hot water circulating heat in the winter. Our overall project objectives are to 1) collect local information on these critical factors to understand the uncertainty and reduce the risk associated with developing the geothermal resource for use in a WVU campus GDHC system, and 2) complete an optimized design for the GDHC system by minimizing the delivered Levelized Cost of Heat (LCOH). A Tuscarora geological model was built based on core analysis and permeability measurements using data from nearby wells. This geological model is then translated into a reservoir model. iTOUGH2/EOS1 is used to simulate the subsurface portion of a GDHC system using two well configurations: 1) a pair of vertical wells, and 2) a pair of horizontal wells. The performance of both configurations is evaluated based on achievable flow rates and production fluid temperatures; the recommended well configuration is selected based on the expected GDHC system performance. The thermal breakthrough and reservoir productivity increased for horizontal well configuration however, the well-head LCOH for vertical well horizontal well configuration is higher than vertical wells due to the additional cost of horizontal drilling. To minimize the delivered LCOH, we perform an integrated surface-to-subsurface optimization of the full GDHC system. The economic and performance analysis of the GDHC system is evaluated for two cases: 1) using existing district heating and cooling facilities, and 2) by converting the current WVU campus steam infrastructure to a hot water system. The optimized GDHC system will be selected by minimizing the LCOH over these two possible configurations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Large-Scale Solar Development: A Playbook for Southwest Virginia

his playbook is an introductory guide for local governments to facilitate large-scale solar projects in Southwest Virginia. In a region that has a long history of energy production, solar technologies offer enormous potential for economic development and job growth. Large-scale solar can take many forms, including rooftop or ground-mounted installations at local corporate offices, nonprofit organizations, or schools. It can also encompass utility-scale projects over many acres on former agricultural or timberlands, mined lands, or industrial sites. Regardless of the type of project, solar is a widely popular, cost-competitive energy choice that helps create sustainable and prosperous communities. This playbook is directed to municipal and county governments that have an essential role to play in encouraging large-scale solar projects. The first section provides an overview of state and national trends, including recent state legislation that will impact local oversight of solar development. This is followed by an overview of the solar project approval process from a developer’s perspective. The next section is an overview of the state and local permitting process for solar projects, followed by other development considerations such as local tax revenue options, financing incentives, and considerations for solar on brownfields and previously mined lands. The playbook concludes with a step-by-step guide for local governments to facilitate large-scale solar development.This playbook is part of the Solar Workgroup of Southwest Virginia’s effort to bring solar energy and associated jobs to the region. Over the past few years, the workgroup has met with stakeholder groups and crafted a strategy for local solar energy development. The workgroup has collaborated with cities and counties to bring SolSmart designation to eight counties and cities, implemented group purchase campaigns for commercial solar, and led research efforts.

14 SOLAR ENERGY↗
DOE OSTI2019

Refined Lithostratigraphy of Middle and Upper 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 late in the 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. This study uses log data from nearly 400 wells from West Virginia to produce detailed maps of the economically important, organic-rich facies and identifies various associated vertical and lateral lithostratigraphic unit boundaries. The study focuses on the Middle Devonian Hamilton Group and its constituent Marcellus and Mahantango formations. Within the Marcellus, a lower Union Springs Member, a middle Cherry Valley Member, and an upper Oatka Creek Member are defined within northeastern West Virginia only. Throughout the rest of the subsurface of the State, the Marcellus has no distinguishable members. In the Upper Devonian, the occurrence and limits of the Harrell Shale (and its basal Burket Shale Member), and its westward lateral transition into the largely-correlative Genesee Formation (with basal Geneseo Shale and upper West River Shale members) are mapped. Maps also detail the position at which the Sonyea Formation (with basal Middlesex Shale and upper Cashaqua Shale members), West Falls Formation (with basal Rhinestreet Shale and upper Angola Shale members), Java Formation (undifferentiated), and lower part of the Huron Member of the Ohio Shale transition eastward into age-equivalent strata of the Brallier Formation.

Boswell, Ray↗
DOE OSTI2019

Deep Direct-Use Feasibility Study Numerical Modeling and Uncertainty Analysis using iTOUGH2 for West Virginia University

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. As a result, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided to use single permeability model for further analysis; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the perdition, therefore, is considered in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis are used for economic analysis. The dataset submitted here support the described study. Manuscript is submitted to Geothermics, will be linked once paper is accepted.

15 GEOTHERMAL ENERGY↗
NASA NTRS2003

Multi-scale variation in spatial heterogeneity for microbial community structure in an eastern Virginia agricultural field

To better understand the distribution of soil microbial communities at multiple spatial scales, a survey was conducted to examine the spatial organization of community structure in a wheat field in eastern Virginia (USA). Nearly 200 soil samples were collected at a variety of separation distances ranging from 2.5 cm to 11 m. Whole-community DNA was extracted from each sample, and community structure was compared using amplified fragment length polymorphism (AFLP) DNA fingerprinting. Relative similarity was calculated between each pair of samples and compared using geostatistical variogram analysis to study autocorrelation as a function of separation distance. Spatial autocorrelation was found at scales ranging from 30 cm to more than 6 m, depending on the sampling extent considered. In some locations, up to four different correlation length scales were detected. The presence of nested scales of variability suggests that the environmental factors regulating the development of the communities in this soil may operate at different scales. Kriging was used to generate maps of the spatial organization of communities across the plot, and the results demonstrated that bacterial distributions can be highly structured, even within a habitat that appears relatively homogeneous at the plot and field scale. Different subsets of the microbial community were distributed differently across the plot, and this is thought to be due to the variable response of individual populations to spatial heterogeneity associated with soil properties. c2003 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

NASA Discipline Life Support Systems↗
DOE OSTI2023

Home range and resource selection of Virginia opossums in the rural southeastern United States

The Virginia opossum (Didelphis virginiana) has a rapidly expanding distribution in North America, but many aspects of its ecology remain relatively understudied, particularly in rural areas of its core range. We collected GPS telemetry data from 93 opossums in a rural, non-agricultural landscape in South Carolina, USA (2018–2019) to examine factors influencing space use and resource selection. Estimated male home ranges (99% utilization distributions) were on average 50% larger than those of females (mean home range 115.9 ± 103.7 ha vs 76.7 ± 75.0 ha). The home range size decreased on average by 20% with each 20% increase in deciduous land cover but was not affected by season or other landscape factors. Core area sizes (65% utilization distributions) were not influenced by sex (mean core area size 29.1 ± 23.7 ha and 22.4 ha ± 13.8 for males and females, respectively) or season, but the core area size decreased by 14% with each 400 m increase in distance from a permanent water source. Resource selection by opossums primarily occurred at the landscape level. Both males and females generally selected for wetlands while avoiding pine forests and developed/open areas, likely the result of differences in resource availability and predation risk between habitats. Opossums also tended to select for linear features such as unpaved roads and edge habitat, which may facilitate movement across the landscape. Finally, the home ranges we documented are among the largest recorded for opossums in the USA, likely the result of the relatively low resource abundance throughout our study area due to comparatively minimal anthropogenic influence.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Modeling study of deep direct use geothermal on the West Virginia university campus-morgantown, WV

To reduce the geothermal exploration risk, a feasibility study is performed for a deep direct-use (DDU) system proposed at the West Virginia University (WVU) Morgantown campus. This study applies numerical simulations to investigate reservoir impedance and thermal production. Because of the great depth of the geothermal reservoir, few data are available to characterize reservoir features and properties. Consequently, the study focuses on the following three aspects: 1. model choice for predicting reservoir impedance and thermal breakthrough: after investigating three potential models (one single permeability model and two dual permeability models) for flow through fractured rock, it is decided only the single permeability model is needed; 2. well placement (horizontal vs. vertical) options: horizontal well placement seems to be more robust to heterogeneity and the impedance is more acceptable; 3. Prediction uncertainty: the most influential parameters are identified using a First-Order-Second-Moment uncertainty propagation analysis, and the uncertain range of the model predictions is estimated by performing a Monte Carlo simulation. Heterogeneity has a large impact on the prediction, therefore, heterogeneity is included in the predictive model and uncertainty analysis. The numerical model results and uncertainty analysis will be used for further economic studies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia

Low-lying coastal cities, exemplified by Norfolk, Virginia, face the challenge of street flooding caused by rainfall and tides, which strain transportation and sewer systems and can lead to personal and property damage. While high-fidelity, physics-based simulations provide accurate predictions of urban pluvial flooding, their computational complexity renders them unsuitable for real-time applications. Using data from Norfolk rainfall events between 2016 and 2018, this study compares the performance of a previous surrogate model based on a random forest algorithm with two deep learning models: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The comparison of deep learning to the random forest algorithm is motivated by the desire to utilize a machine learning architecture that allows for the future inclusion of common uncertainty quantification techniques and the effective integration of relevant, multi-modal features.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data-driven method for electric vehicle charging demand analysis: Case study in Virginia

Electric vehicle (EV) adoption in the U.S. will be accelerated by the historic $7.5 billion public investments in EV charging infrastructure. Careful analysis of EV charging demands plays a vital role in understanding the energy requirements, power grid impact, and smart charging management opportunities of EVs. To this end, this paper develops a data-driven trip-chaining-based modeling framework including five steps: Trip data acquisition and preprocessing, EV adoption modeling, travel itinerary synthesis, EV charging demand simulation and EV load profile generation. The developed analysis framework was demonstrated using real-world data for one region in Virginia, U.S. The results show that the proposed modeling framework can work effectively. For the study region in 2040, the predicted number of plug-in EVs is 470,114, resulting in a weekly charging demand of 38,078,127 kWh (55% home, 9% work, and 36% public) in September and 45,920,358 kWh (61% home, 9% work, and 30% public) in February.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Prevalence of erythromycin-resistant emm92 -type invasive group A streptococcal infections among injection drug users in West Virginia, United States, 2021–23

Abstract Background Increasing incidence of invasive group A Streptococcus (iGAS) disease has been reported in Europe and the USA over the past several years. Coupled with this are observations of higher rates of resistance to erythromycin and clindamycin. Objectives To characterize iGAS and pharyngitis isolates from West Virginia (WV), a US state outside of the national Active Bacteria Core surveillance purview, where risk factors associated with iGAS infections are prevalent. Methods Seventy-seven invasive group A Streptococcus isolates were collected from 67 unique patients at the J.W. Ruby Memorial Hospital Clinical Microbiology Laboratory in WV from 2021 to 2023. Invasive isolates and 20 unique pharyngitis isolates were tested for clindamycin and erythromycin susceptibility in the clinical laboratory. Patient demographic and clinical information was retrieved from patient electronic health records. Isolates were further characterized based on emm subtype and detection of MLSB resistance determinants. Results Twenty-six (39%) isolates were of a single emm92 type. All emm92 isolates were uniformly erythromycin/clindamycin resistant with inducible or constitutive MLSB resistance imparted by the plasmid-borne erm(T) gene. The majority of emm92 infections were associated with adult patients who reported IV drug use, whereas no pharyngitis infections were caused by an emm92 strain. Overall, 51 (76%) of the 67 iGAS isolates were determined to carry MLSB resistance. Conclusions Isolates of emm92 type (clonal subtype emm92.0) were associated with iGAS infections in adult IV drug users, but not with paediatric pharyngitis, and were uniformly resistant to erythromycin and clindamycin.

Powell, Lillie M.↗
DOE OSTI2016

Determinants of enrollment in public incentive programs for forest management and their effect on future programs for woody bioenergy: evidence from Virginia and Texas

Several federal- and state-sponsored programs, including cost-sharing arrangements, tax incentives, and technical assistance programs, are available to forestland owners, aiming to encourage desired forest management practices and outcomes. However, enrollment rates in such programs are low, and trends of forestland parcelization hint at an even smaller enrollment rate in the future. Therefore, it is important to understand how socioeconomic attributes of forestland owners and past experience with such programs affect the likelihood of enrollment in public incentive programs. Among others, this will help us provide tailored information to forestland owners who are less likely to use these opportunities through extension and outreach programs. Towards this end, we conducted a survey of 1800 forestland owners in Virginia and Texas. Our recursive partitioning, logistic regression, and Cochran–Armitage trend test results suggest that forestland owners who are less likely to enroll in such programs have relatively smaller forestland acreage, a lower level of education, and shorter land ownership tenure. We also find that forestland owners with experience in public incentive programs tend to attach higher importance to potential programs, including those that do not directly benefit them. Furthermore, we also identify threshold values for explanatory variables such as acreage and tenure length.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Application of a convolutional neural network in permeability prediction: A case study in the Jacksonburg-Stringtown oil field, West Virginia, USA

Permeability is a critical parameter for understanding subsurface fluid flow behavior, managing reservoirs, enhancing hydrocarbon recovery, and sequestering carbon dioxide. In general, permeability is measured in the laboratory based on subsurface core samples, calculated from well logs or estimated from well tests. However, laboratory measurements and well tests are expensive, time-consuming, and usually limited to a few core samples or wells in a hydrocarbon field or carbon storage site. Machine-learning techniques are good options for generating a rapid, robust, and cost-effective permeability prediction model because of their strengths to recognize the potential interrelationships between input and output variables. Convolutional neural networks (CNN), as a good pattern recognition algorithm, are widely used in image processing, natural language processing, and speech recognition, but are rarely used with regression problems and even less often in reservoir characterization. We have developed a CNN regression model to estimate the permeability in the Jacksonburg-Stringtown oil field, West Virginia, which is a potential carbon storage site and enhanced oil recovery operations field. We also evaluate the concept of the geologic feature image, which is converted from geophysical well logs. Five variables, including two commonly available conventional well logs (the gamma rays [GRs] and bulk density) and three well-log-derived variables (the slopes of the GR and bulk density curves, and shale content), are used to generate a geologic feature image. The CNN treats the geologic feature image as the input and the permeability as the desired output. In addition, the permeability predicted using traditional backpropagation artificial neural networks, which are optimized by genetic algorithms and particle swarm optimization, is compared with the permeability estimated using our CNN. Our results indicate that the CNN regression model provides more accurate permeability predictions than the traditional neural network.

Geochemistry & Geophysics↗
DOE OSTI2018

Aftershock sequence of the 2011 Virginia earthquake derived from the dense AIDA array and backprojection

After the 23 August 2011 Mineral, Virginia, earthquake, a temporary dense array (Aftershock Imaging with Dense Arrays (AIDA)) consisting of ~200 stations was deployed at 200-400 m spacing near the epicenter for 12 days. Backprojection of the data was used to automatically detect and locate aftershocks. The co-deployment of a traditional aftershock network of 36 stations at ~2-10 km spacing enables a quantitative comparison. The AIDA backprojection aftershock catalog is complete to magnitude -1.0 and includes events as small as M-1.8. For comparison, the traditional network was complete to M-0.3 for the same time period. The AIDA backprojection catalog observes the same major patterns of seismicity in the epicentral region, but additional details are illuminated. The primary zone of seismicity is not a single fault but is a tabular zone of multiple small faults, this zone has a subtle concave shape along strike and with depth, and a broader zone of new events is observed at shallow depth. In addition, a new separate, shallow cluster was detected and located to the east of the main aftershock zone. The addition of smaller events to the catalog did not change the b-value or the temporal decay constant, but illuminated spatial and temporal patterns. Both the b-value and temporal decay constant are different for 12 days than for 4 months and are different at < 3km depth than at greater depth. Very low b-value, especially at greater depth, is consistent with observed very high stress drops. Conclusively, the results indicate the benefits of dense arrays and auto-detection by backprojection for aftershock studies. And finally, the reduced detection threshold and higher spatial resolution enabled the study of earthquake mechanisms and strain transfer at an unprecedented small scale.

Beskardes, Gungor Didem↗
DOE OSTI2016

Energy Efficiency, Water Efficiency, and Renewable Energy Site Assessment: Seneca Rocks Discovery Center, Seneca Rocks, West Virginia

As part of ongoing efforts by the U.S. Forest Service to reduce energy use and incorporate renewable energy technologies into its facilities, the Department of Energy's National Renewable Energy Laboratory performed an energy efficiency and renewable energy site assessment of the Seneca Rocks Discovery Center in Seneca Rocks, West Virginia. This report documents the findings of this assessment, and provides site-specific information for the implementation of energy and water conservation measures, and renewable energy measures.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Cost Effectiveness of ASHRAE Standard 90.1-2013 for the State of Virginia

Moving to the ASHRAE Standard 90.1-2013 (ASHRAE 2013) edition from Standard 90.1-2010 (ASHRAE 2010) is cost-effective for the State of Virginia. The table below shows the state-wide economic impact of upgrading to Standard 90.1-2013 in terms of the annual energy cost savings in dollars per square foot, additional construction cost per square foot required by the upgrade, and life-cycle cost (LCC) per square foot. These results are weighted averages for all building types in all climate zones in the state, based on weightings shown in Table 4. The methodology used for this analysis is consistent with the methodology used in the national cost-effectiveness analysis. Additional results and details on the methodology are presented in the following sections. The report provides analysis of two LCC scenarios: Scenario 1, representing publicly-owned buildings, considers initial costs, energy costs, maintenance costs, and replacement costs—without borrowing or taxes. Scenario 2, representing privately-owned buildings, adds borrowing costs and tax impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗