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DOE OSTI2025

DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)

Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Testing and Expertise for Marine Energy (TEAMER) Program Support (CRADA Final Report)

Virginia Tech (VT) had previously developed a 50 kW AC to DC power converter that is specifically designed to improve the performance and efficiency of wave energy converters (WECs). Through this CRADA, NLR will test the VT power converter via a coupled dynamometer and power electronics test platform. The NLR test platform will be comprised of (1) a rotary dynamometer that will drive a VT provided gearbox and generator, (2) one or more DC regenerative power supplies that provide input power to and take power from the VT supplied power electronics, and (3) a data acquisition system for measurement. NLR will work with VT to develop a test plan, set up the test platform and integrate the test article, perform the testing, and assist in the data analysis.

16 TIDAL AND WAVE POWER↗

DOE OSTI2026

A Data-driven Phytotechnology Framework for Identification and Remediation of Leached-Metals-Contaminated Soil Near Coal Ash Impoundments

This project developed and evaluated advanced remote sensing and machine learning approaches to identify, monitor, and address environmental contamination associated with coal combustion residual (CCR) impoundments and landfills at coal-fired power plants. In Phase I, historical and multi-temporal Sentinel-2 satellite imagery, groundwater monitoring data, and environmental variables were integrated to detect vegetation stress potentially caused by toxic metal leaching from coal ash disposal sites. Multiple vegetation and biophysical indices were analyzed to determine their effectiveness in identifying abnormal vegetation growth patterns linked to contamination. Results from case studies conducted at coal ash–impacted power plant sites in North Carolina and Virginia demonstrated that satellite-based vegetation monitoring can serve as an effective early indicator of environmental stress associated with metals such as arsenic, cadmium, cobalt, lead, lithium, radium, and thallium.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2021

Acoustic Exposure to Turbine Operation Quantifies Risk to Bats at Commercial Wind Energy Facilities

ABSTRACT Turbine‐related bat mortality at commercial wind energy facilities may threaten populations of migratory tree‐roosting bat species in North America. Industry stakeholders and regulatory agencies alike are investigating strategies to reduce risk of population‐level consequences as the wind energy industry grows. Bats collide with turbines only when turbine rotors are spinning and curtailing turbine operation at low wind speeds can effectively reduce bat fatality rates. Nonetheless, few quantitative data exist to determine appropriate threshold wind speeds below which turbine operations should be curtailed. Carcass monitoring is labor‐intensive and does not provide information on factors linked to bat fatality rates on any scale finer than nightly. We tested whether acoustic bat data recorded at turbine nacelles could provide a more precise and sensitive measure of fatality risk to bats by analyzing acoustics, weather, turbine operation, and carcass data collected at 2 commercial wind energy facilities in West Virginia over 7 years. Each wind facility implemented several distinct curtailment treatments during our study, allowing us to compare fatality rates and acoustic bat activity across multiple operational strategies. We found that bat passes exposed to turbine operation explained close to 80% of the variation in carcass‐based estimates of bat fatality rates and accounted for significant variation in raw carcass counts per turbine and probability of finding bat carcasses during individual turbine searches. Conversely, bat activity occurring when turbines were not operating had little or no relationship to fatality rates. We also found that patterns in bat activity exposure could be predicted accurately among turbines and years. Our results demonstrate that measuring exposure of acoustic bat activity provides a quantitative basis for designing, evaluating, and adaptively managing curtailment strategies. This is an important advance towards using curtailment to reduce bat fatality rates strategically while allowing for increased generation of renewable energy. © 2021 The Authors. Wildlife Society Bulletin published by Wiley Periodicals LLC on behalf of The Wildlife Society.

17 WIND ENERGY↗

DOE OSTI2024

Manganese exposure from spring and well waters in the Shenandoah Valley: interplay of aquifer lithology, soil composition, and redox conditions

Manganese (Mn) is of particular concern in groundwater, as low-level chronic exposure to aqueous Mn concentrations in drinking water can result in a variety of health and neurodevelopmental effects. Much of the global population relies on drinking water sourced from karst aquifers. Thus, we seek to assess the relative risk of Mn contamination in karst by investigating the Shenandoah Valley, VA region, as it is underlain by both karst and non-karst aquifers and much of the population relies on water wells and spring water. Water and soil samples were collected throughout the Shenandoah Valley, to supplement pre-existing well water and spring data from the National Water Information System and the Virginia Household Water Quality Program, totaling 1815 wells and 119 springs. Soils were analyzed using X-ray fluorescence and Mn K-Edge X-ray absorption near-edge structure spectroscopy. Factors such as soil type, soil geochemistry, and aquifer lithology were linked with each location to determine if correlations exist with aqueous Mn concentrations. Analyzing the distribution of Mn in drinking water sources suggests that water wells and springs within karst aquifers are preferable with respect to chronic Mn exposure, with < 4.9% of wells and springs in dolostone and limestone aquifers exceeding 100 ppb Mn, while sandstone and shale aquifers have a heightened risk, with > 20% of wells exceeding 100 ppb Mn. The geochemistry of associated soils and spatial relationships to various hydrologic and geologic features indicates that water interactions with aquifer lithology and soils contribute to aqueous Mn concentrations. Relationships between aqueous Mn in spring waters and Mn in soils indicate that increasing aqueous Mn is correlated with decreasing soil Mn(IV). These results point to redox conditions exerting a dominant control on Mn in this region.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Drug discovery efforts at George Mason University

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

A Regional Approach to Offshore Wind Energy Manufacturing in the Central Atlantic: Workforce

Meeting the additional job requirements spurred by potential growth in the offshore wind industry is likely to involve intentional and effective recruitment practices and training program development across all occupations (e.g., skilled trades, engineers, professionals) at state, regional, and local levels. The workforce development ecosystem is complex, especially for an industry like offshore wind, so assessing the readiness of an area to support the workforce needs of new manufacturing facilities involves evaluating numerous economic and training factors at an occupational level. Funded by the National Offshore Wind Research and Development Consortium and led by the National Renewable Energy Laboratory, this report represents one part of a two-part study that explores the challenges and opportunities for Delaware, Maryland, Virginia, and North Carolina in fostering regional collaboration to build a domestic supply chain for the U.S. offshore wind energy sector.

17 WIND ENERGY↗

NASA NTRS2002

A geostatistical analysis of small-scale spatial variability in bacterial abundance and community structure in salt marsh creek bank sediments

Small-scale variations in bacterial abundance and community structure were examined in salt marsh sediments from Virginia's eastern shore. Samples were collected at 5 cm intervals (horizontally) along a 50 cm elevation gradient, over a 215 cm horizontal transect. For each sample, bacterial abundance was determined using acridine orange direct counts and community structure was analyzed using randomly amplified polymorphic DNA fingerprinting of whole-community DNA extracts. A geostatistical analysis was used to determine the degree of spatial autocorrelation among the samples, for each variable and each direction (horizontal and vertical). The proportion of variance in bacterial abundance that could be accounted for by the spatial model was quite high (vertical: 60%, horizontal: 73%); significant autocorrelation was found among samples separated by 25 cm in the vertical direction and up to 115 cm horizontally. In contrast, most of the variability in community structure was not accounted for by simply considering the spatial separation of samples (vertical: 11%, horizontal: 22%), and must reflect variability from other parameters (e.g., variation at other spatial scales, experimental error, or environmental heterogeneity). Microbial community patch size based upon overall similarity in community structure varied between 17 cm (vertical) and 35 cm (horizontal). Overall, variability due to horizontal position (distance from the creek bank) was much smaller than that due to vertical position (elevation) for both community properties assayed. This suggests that processes more correlated with elevation (e.g., drainage and redox potential) vary at a smaller scale (therefore producing smaller patch sizes) than processes controlled by distance from the creek bank. c2002 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

Non-NASA Center↗

NASA NTRS1992

The SERENDIP piggyback SETI project

The SERENDIP project is an ongoing program of monitoring and processing broadband radio signals acquired by existing radio astronomy observatories. SERENDIP operates in a piggyback mode: it makes use of whatever observing plan (sequence of frequencies, sky coordinates, and polarizations) is under way at its host observatory. Moreover, the SERENDIP data acquisition system, once installed, operates autonomously. This approach makes it possible to obtain large amounts of high quality observing time in a manner that is economical and that does not adversely affect ongoing radio astronomy survey work. The SERENDIP II system has been installed at the NRAO 300-foot telescope at Green Bank, West Virginia, and has operated there for several thousand hours. In this report, we summarize our findings from these observations and describe the present status of the project. Two key elements of SERENDIP are the automated data acquisition system that uses adaptive thresholds and logs only statistically significant peaks in the real-time power spectra, and the subsequent off-line analysis programs that identify and reject a variety of interference signals. Several specific correlations have been identified that offer promise. At present, the development and testing of these interference rejection algorithms is the main thrust of our work.

NASA Discipline Number 52-60↗

NASA NTRS1994

(abstract) Airborne Emission Spectrometer (AES)

AES is a low-cost analog of the TES downlooking modes. Because AES operates at ambient temperature, limb-viewing is not possible. The first flight of AES took place in April 1994 on the NASA P3B aircraft out of Wallops Island, VA. While planned as an engineering test flight, spectra were successfully acquired both over the Atlantic Ocean and the area of the Great Dismal Swamp on the Virginia-North Carolina border. At this writing (July 1994), a second series of flights on the NASA DC8 aircraft out of Ames RC,CA is in progress. By the time of the workshop, a third series using the NASA C130 should have been accomplished.

Airborne Emission Spectrometer spectrometry AES Gr↗

NASA NTRS2014

Recent Pharmacology Studies on the International Space Station

The environment on the International Space Station (ISS) includes a variety of potential stressors including the absence of Earth's gravity, elevated exposure to radiation, confined living and working quarters, a heavy workload, and high public visibility. The effects of this extreme environment on pharmacokinetics, pharmacodynamics, and even on stored medication doses, are not yet understood. Dr. Wotring will discuss recent analyses of medication doses that experienced long duration storage on the ISS and a recent retrospective examination of medication use during long‐duration spaceflights. She will also describe new pharmacology experiments that are scheduled for upcoming ISS missions. Dr. Virginia E. Wotring is a Senior Scientist in the Division of Space Life Sciences in the Universities Space Research Association, and Pharmacology Discipline Lead at NASA's Johnson Space Center, Human Heath and Countermeasures Division. She received her doctorate in Pharmacological and Physiological Science from Saint Louis University after earning a B.S. in Chemistry at Florida State University. She has published multiple studies on ligand gated ion channels in the brain and spinal cord. Her research experience includes drug mechanisms of action, drug receptor structure/function relationships and gene & protein expression. She joined USRA (and spaceflight research) in 2009. In 2012, her book reviewing pharmacology in spaceflight was published by Springer: Space Pharmacology, Space Development Series.

Wotring, Virginia↗

DOE OSTI2024

Recovery of Natural Gas Equipment Emissions into Gas Compression Engines for the Reduction of Potential Greenhouse Gas Emissions

Since the turn of the millennium, the United States (U.S.) oil and natural gas (ONG) industry has nearly doubled its natural gas production rate. As a result, the ONG industry has recently come under increasing scrutiny for its contributions to greenhouse gas (GHG) emissions. Consequently, various solutions to this problem have been proposed and formulated to reduce the impacts of GHG emissions on the environment. West Virginia University (WVU) have found it important to research the impacts of recovering vented gas streams into prime-mover engines. The U.S. Department of Energy (DOE) and National Energy Technology Laboratory (NETL) have granted WVU funding to research and develop a “Methane Mitigator” (M2) - a “Scalable Vent Mitigation Strategy to Simultaneously Reduce Methane Emissions and Fuel Consumption from the Compression Industry.” One of the main areas of interest for this research was the collection of emissions from natural gas equipment into a Caterpillar G3508J natural gas compression engine. The parameters being analyzed from the engine were brake-specific emissions and power output. The emissions sources considered for this research were pneumatic controllers (PCs), reciprocating compressor vents, and the engine’s open crankcase breather. The compressor vent and PC emissions were simulated using a mass flow controller (MFC) and flowed into the engine using two separate methods: (1) directly into the air intake, and (2) through a retrofitted closed crankcase ventilation system (CCV), serving as a buffer volume. The crankcase emissions were quantified without the CCV, and the impact on exhaust emissions from circulating the crankcase gases into the intake was measured. The simulated compressor vent and PC flows from the MFC had limited effect on the steady state operation of the engine and resulting performance. When the simulated flows were fed directly into the engine’s air intake, the changes within the engine’s continuous performance and emission parameters were larger but lasted for shorter durations. Conversely, when the simulated flows were fed into the CCV before entering the air intake, the changes in the engine’s performance and emission parameters were less pronounced for continuous analysis but lasted for longer durations. In either case, the continuous emission changes in both emissions and performance varied in size depending on the test scenario being run, but the cycle average changes in emissions and performance showed little impact overall compared to the engine’s baseline operation. As a result, the inclusion of a CCV shows a decrease in baseline carbon dioxide equivalent (CO2-eq.) engine emissions (from combined exhaust and open crankcase) of almost 4%. Likewise, the CCV inclusion reduced baseline total methane (CH4) from combined exhaust and open crankcase by upwards of 16%. These atmospheric emissions only decreased further with the inclusions of collected PC and compressor vent flows. The resulting changes in time-averaged rated exhaust behavior (or lack thereof) prove that the proposed M2 system could likely be deployed at sites with modern lean-burn natural gas engines as a viable option for reducing and eliminating potential GHG sources that would have otherwise been unutilized as energy sources.

03 NATURAL GAS↗

DOE OSTI2021

State changes: insights from the U.S. Long Term Ecological Research Network

Understanding the complex and unpredictable ways ecosystems are changing and predicting the state of ecosystems and the services they will provide in the future requires coordinated, long-term research. This paper is a product of a U.S. National Science Foundation funded Long Term Ecological Research (LTER)network synthesis effort that addressed anticipated changes in future populations and communities. Each LTER site described what their site would look like in50 or 100 yr based on long-term patterns and responses to global change drivers in each ecosystem. Common themes emerged and predictions were grouped into state change, connectivity, resilience, time lags, and cascading effects. Here, we report on the “state change” theme, which includes examples from the Georgia Coastal (coastal marsh), Konza Prairie (mesic grassland), Luquillo(tropical forest), Sevilleta (arid grassland), and Virginia Coastal (coastal grassland) sites. Ecological thresholds(the point at which small changes in an environmental driver can produce an abrupt and persistent state change in an ecosystem quality, property, or phenomenon) were most commonly predicted. For example, in coastal ecosystems, sea-level rise and climate change could convert salt marsh to mangroves and coastal barrier dunes to shrub thicket. Reduced fire frequency has converted grassland to shrubland in mesic prairie, whereas overgrazing combined with drought drive shrub encroachment in arid grasslands. Lastly, tropical cloud forests are susceptible to climate-induced changes in cloud base altitude leading to shifts in species distributions. Overall, these examples reveal that state change is a likely outcome of global environmental change across adverse range of ecosystems and highlight the need for long-term studies to sort out the causes and consequences of state change. The diversity of sites within the LTER network facilitates the emergence of overarching concepts about state changes as an important driver of ecosystem structure, function, services, and futures.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Assessment of habitat‐specific competition for oral rabies vaccine baits between raccoons and opossums

Abstract Throughout the eastern United States, the National Rabies Management Program (NRMP) distributes oral rabies vaccine (ORV) baits to manage rabies virus circulation in raccoon ( Procyon lotor ) populations. The consumption of vaccine baits by non‐target species including Virginia opossums ( Didelphis virginiana ) may reduce the effectiveness of ORV programs, but competition for baits remains poorly quantified in many areas of the southeastern United States. We distributed placebo ORV baits injected with a biomarker across 4 land cover types (bottomland hardwood, upland pine, riparian, isolated wetland) on the Savannah River Site in South Carolina, USA, 2017–2019. We then trapped and collected whiskers from 247 raccoons and 78 opossums to assess biomarker presence using fluorescent microscopy. Our data revealed greater bait uptake probability by raccoons (estimated x̅ = 0.30, 95% CI = 0.19–0.44) compared to opossums (estimated x̅ = 0.11, 95% CI = 0.05–0.23) across all cover types surveyed. Probability of bait consumption was not affected by cover type or the abundance of raccoons or opossums. Among raccoons, males were more likely to consume baits than females (estimated x̅ = 0.28, 95% CI = 0.17–0.44 for males and 0.14, 95% CI = 0.05–0.31 for females) and probability of consumption increased by 0.08 with each additional day trapped during the 10‐day trapping session. Uptake rates for raccoons were relatively low compared to other studies and not influenced by competition with opossums. These low consumption rates indicate that additional research addressing the roles of baiting season, bait density, and resource selection will be important to maximize ORV bait uptake by target species in these southeastern landscapes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Soil carbon sequestration potential and the identification of hotspots in the Eastern Corn Belt of the United States

Soil carbon sequestration is a significant CO 2 mitigation strategy but precise assessments of sequestration require spatially explicit modeling of potential changes in soil organic carbon (SOC) in response to soil, climate, land condition and management interactions. We assessed the SOC sequestration potential of the Eastern Corn Belt (ECB) in the USA (Indiana, Kentucky, Maryland, Michigan, Ohio, Pennsylvania, and West Virginia) in response to the adoption of conservation farming practices and land use change (pasture and forestation) using the SOCRATES model. Input data was provided through an intersection of the STATSGO, National Land Cover Database and a PRISM climate surface. At the end of the 20th century, the 15.3 Mha of cropped soils in the ECB contained 632 Tg C, an estimated reduction of 52% since the introduction of agriculture in the mid-1800s. Complete adoption of no-tillage practices on Prime Cropland would potentially recover 147 Tg SOC over 20 years, whereas a continuation of conventional tillage would produce a loss of 35 Tg SOC. Sequestration hotspots (> 500 Gg increase in SOC) under no-tillage cover 2.3 Mha providing 28 Tg C over 20 years. The conversion of Marginal (non-Prime) agricultural lands to Forests would yield an additional 13 Tg C in SOC and 381 Tg C in aboveground biomass. The rehabilitation of Minelands to Forest would yield an additional 4 Tg C in SOC and 42 Tg C in biomass. Here, opportunities to sequester C in the ECB via tillage and reforestation are substantial and should be incorporated into regional and national climate change mitigation strategies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Petrology and geochemistry of the Middle Pennsylvanian (Langsettian) Clintwood coalbed, Pike County, Kentucky

Abstract The Middle Pennsylvanian, Langsettian sub-stage Clintwood coal, along with its correlatives, was one of the more important energy resources in eastern Kentucky. The coal thickens from the NE-SW-trending Belfry anticline in central Pike County to the southeast, towards the Virginia-Kentucky border. Much of the thickness increase is in the bright lithotypes below a dull lithology. The bright lithologies transition from a bright clarain + vitrain + fusain lithology to a clarain + fusain lithology to the southeast. Half of the lithotypes in the three analyzed sections exceed 1000-µg/g Rare earth elements + Y + Sc (REYSc) (ash basis). The La N /Sm N vs. Gd N /Yb N and La N /Sm N vs. Ce N /Ce N * plots suggest that the top lithotype may have had best indication of an oxidizing environment. Positive Gd N /Gd N * and Eu N /Eu N * in some of the lithologies suggests that there may have been a hydrothermal influence in the sediment sources and/or in the diagenesis of the coal. Consideration of La N /Sm N and Zr (µg/g; ash basis), Zr vs. Sr, and V/Cr vs. inertinite/(inertinite + vitrinite) indicates that the dull lithotypes existed in a distinct geochemical environment compared to the brighter lithotypes. The isolation of the dull lithotypes from the bright lithotypes is supported by principal components analysis on inertinite/(inertinite + vitrinite), La N /Sm N , V/Cr, Sr, and either Zr or Ln(100(TiO 2 )/Al 2 O 3 ).

Hower, James C. (ORCID:0000000346942776)↗

DOE OSTI2026

Experiments at Jefferson Lab

This chapter presents experiments conducted at Thomas Jefferson National Accelerator Facility (Jefferson Lab), a U.S. Department of Energy national laboratory in Newport News, Virginia. There, physicists exploring the nature of matter make use of the Continuous Electron Beam Accelerator Facility (CEBAF), a DOE Office of Science user facility that enables the research of more than 1,650 scientists worldwide. CEBAF’s precise electron beams can reach energies up to 12 billion electron-volts and exhibit high degrees of polarization. Jefferson Lab’s first experiment began taking data in 1995. Since then, the facility has become a world leader in the study of quantum chromodynamics. Today, experiments are carried out simultaneously in four experimental halls, each with specialized capabilities. The primary instruments in use are focusing or large-acceptance magnetic spectrometers, many of which feature superconducting elements. Jefferson Lab’s physics program provides unprecedented insight into the particles and forces that shape the visible universe.

Achenbach, Patrick [Thomas Jefferson National Acce↗

DOE OSTI2021

A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

14 SOLAR ENERGY↗