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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Spectroscopic Characterization of Highly Active Fe–N–C Oxygen Reduction Catalysts and Discovery of Strong Interaction with Nafion Ionomer

Scaling up clean-energy applications necessitates the development of platinum group metal (PGM)-free fuel cell electrocatalysts with high activity, stability, and low cost. Here, X-ray absorption (XAS) at the Fe K-edge and Fe K β X-ray emission (XES) spectroscopies were used to study the electronic structure of Fe centers in highly active Fe–N–C oxygen reduction catalysts with significant commercial potential. X-ray absorption near-edge structure (XANES) analysis has shown that the majority (>95%) of Fe centers are in the Fe 3+ oxidation state, while extended X-ray absorption fine structure (EXAFS) detected a mixture of single site Fe–N 4 centers (>95%) and centers with short (~2.5 Å) Fe–Fe interactions of Fe metal and/or Fe-carbide nanoparticles (<5%) featuring the Fe o oxidation state. Surprisingly, addition of Nafion, the most widely used ionomer, resulted in pronounced changes in the XAS spectra, consistent with a strong catalyst–ionomer interaction where long Fe–Fe interactions at ~3.1 Å were shown to be a feature of Fe 3+ ions bound with the Nafion. We conclude that exposure to Nafion during the device formulation has a different effect from the aggressive acid leaching typically used in the preparation of Fe–N–C catalysts. Furthermore, it was hypothesized that the polymer interacts with single sites’ Fe 3+ centers, as well as with graphene layers protecting the Fe o nanoparticles, and extracts some Fe ions into the Nafion matrix.

25 ENERGY STORAGE↗
DOE OSTI2020

Quantitative temporally and spatially resolved X-ray fluorescence microprobe characterization of the manganese dissolution-deposition mechanism in aqueous Zn/α-MnO 2 batteries

Rechargeable aqueous Zn/α-MnO 2 batteries are a possible alternative to lithium ion batteries for scalable stationary energy storage applications due to their low cost, safety and environmentally benign components. A critical need for advancement of this battery system is a full understanding of the electrochemical reaction mechanisms, which remain unclear. In this report, operando , spatiotemporal resolved synchrotron X-ray fluorescence mapping measurements on a custom aqueous Zn/α-MnO 2 cell provided direct evidence of a Mn dissolution-deposition faradaic mechanism that governs the electrochemistry. Simultaneous visualization and quantification of the Mn distribution in the electrolyte revealed the formation of aqueous Mn species during discharge and depletion on charge. The findings are supported by ex situ transmission electron microscopy (TEM), X-ray diffraction, Mn K-edge X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) measurements. The elucidated mechanism is fundamentally different from the previously proposed Zn 2+ insertion or conversion reactions. These findings provide a foundation for developing dissolution- deposition chemistries suitable for scalable stationary energy storage with aqueous electrolyte.

25 ENERGY STORAGE↗
DOE OSTI2021

Characterization and differentiation of aluminum powders used in improvised explosive devices. Part 2: Micromorphometric method refinement and preliminary statistical analysis

Abstract Aluminum (Al) powder is commonly encountered in improvised explosive devices (IEDs) as a metallic fuel due to its availability and low cost. Although available commercially in powder form, amateur bomb‐makers also produce their own Al powder via simple methods found online. In order to provide investigative leads and forensic intelligence, it is important to evaluate not only the composition of homemade devices, but also to distinguish between the various forms of Al powder they contain. To achieve this goal, a method using automated microscopy in combination with statistical techniques has been demonstrated to have the potential to provide source discrimination and investigative leads in source attribution of Al powders in IEDs. The present research refined this method and investigated 59 industrially and amateurly produced Al powder sources with seven subsamples per source using two traditional linear discriminant analyses (LDA), one with a standard data split for training and testing, and another using leave‐one‐out cross‐validation. Averaging the classification accuracies for the two LDA‐based analyses, LDA has the ability to correctly classify 59.26%, 83.35%, and 80.69% of the samples based on their powder source, type, and production method, respectively. This classification accuracy represents a 3407%, 317%, and 61.38% increase in accuracy from random class assignment, respectively. Further, in most instances of incorrect data attribution to a particular source, the subsample has been misidentified with another sample of the same powder type or production method.

Ommen, Danica M.↗
DOE OSTI2026

Hydrogen Recombiner Catalyst Evaluations for Waste Storage

Radiolysis of water in nuclear waste storage generates hydrogen gas that can accumulate within sludge style waste and be rapidly released during agitation events, creating a significant flammability hazard. Engineering controls are therefore required to limit hydrogen concentrations during both quiescent storage and transient disturbances. Catalytic recombination of hydrogen in waste storage offgas is a proven mitigation strategy, maintaining hydrogen levels below flammability limits and managing sudden concentration spikes. Conventional recombiners rely on platinum and/or palladium catalysts, with development efforts focused on extending service life, increasing active surface area, and ensuring safe deployment in radioactive environments. Savannah River National Laboratory (SRNL) is evaluating a newly developed hydrogen recombiner catalyst from Canadian Nuclear Laboratories as a cost-effective and durable alternative for nuclear waste applications. Testing was conducted in SRNL’s Shielded Cells facility, which enables reduced-scale experimental modeling under radiation fields and near-use-case conditions relevant to radioactive waste storage. Catalyst performance was evaluated using a custom offgas characterization system designed for near-zero flow conditions. The experimental apparatus consisted of a gas-tight 2.7 L PTFE vessel equipped with temperature monitoring, gas flow controls, and a variable-speed mixer to simulate sludge agitation. Offgas composition was monitored using a dedicated gas chromatograph with argon carrier gas and a krypton internal standard. Measurements were obtained for an empty vessel, the vessel containing a well characterized radioactive tank waste sample, and the same configuration with the candidate catalyst installed. Results demonstrate that the new catalyst effectively reduced hydrogen concentrations in the offgas within the constraints of the experimental design. In addition to confirming catalytic activity, the testing provided valuable insights into experimental optimization and considerations for future performance evaluations. These findings support the potential scalability of the technology and highlight its applicability to broader nuclear waste management operations, offering improved safety and reduced operational costs through enhanced catalyst durability and lower replacement frequency.

Tener, Zachary P. [Savannah River National Laborat↗
DOE OSTI2024

CIGS Technology Advancement via Fundamental Modeling of Defect/Impurity Interactions (Final Technical Report)

The primary goals of the proposed work were to provide modeling tools (and the associated insight which comes along with model development) for design and optimization of CuIn x Ga 1-x Se 2 (CIGS) and CdSeTe (CST) solar cell manufacturing processes and to establish the foundation for comprehensive end-to-end predictive modeling tools to enable optimization of thin film photovoltaic technology for performance, cost, yield, and reliability. The initial focus of efforts within this project was to develop coupled process/optical/device models for CIGS PV technology and to work with Siva Power to apply that TCAD (technology computer-aided design) system to improve the efficiency and reduce manufacturing costs for CIGS solar cells. Our approach to that end was to generate an extensive database of DFT calculations and to use those calculations via statistical thermodynamics methods and Monte Carlo simulation to develop and characterize models for the behavior of native defects as well as intentional and unintentional impurities, including the redistribution of the primary components of CIGS films. Increased effort went toward coupling those models for defect behavior and composition evolution to the performance of multicrystalline CIGS solar cells via prediction of doping level and recombination lifetime as function of manufacturing process. In the second budget period, the project pivoted to developing a similar system for the CdSeTe system, focused especially on understanding the role of Se/Te alloy concentration. Execution of the project resulted in the successful development of TCAD systems for both CIGS and CdSeTe thin film PV within the Synopsys Sentaurus framework by utilizing the Alagator interface. In the first budget period of the project, we developed quantitative models for the major components of CIGS PV and implemented them within a framework that couples process, optical, and device simulation. From the insights we have gained, we identified novel opportunities for enhancing CIGS solar cell performance and have laid the groundwork to further optimize the layer structure, composition profile, and thermal cycles for substantially improved efficiency and lower manufacturing costs. For the CIGS system, process changes to achieve greater than 1% absolute enhancement in efficiency were identified, but testing of those approaches was stymied by lack of a domestic CIGS manufacturing partner after the closure of Siva Power as well as Miasole. For CdSeTe, a fully capable TCAD system only became ready to apply near the end of the project period, so substantial opportunities remain to apply those models to enhance the leading thin film PV technology.

14 SOLAR ENERGY↗
DOE OSTI2025

Achieving Higher Activity of Acidic Oxygen Evolution Reaction Using an Atomically Thin Layer of IrO x over Co 3 O 4

The development of electrocatalysts with reduced iridium (Ir) loading for the oxygen evolution reaction (OER) is essential to produce low-cost green hydrogen from water electrolysis under acidic conditions. Herein, an atomically thin layer of iridium oxide (IrO x ) has been uniformly dispersed onto cobalt oxide (Co 3 O 4 ) nanocrystals to improve the efficient use of Ir for acidic OER. In situ characterization and theoretical calculations reveal that compared to the conventional IrOx cluster, the atomically thin layer of IrO x shows stronger interaction with the Co 3 O 4 and consequently higher OER activity due to the Ir-O-Co bond formation at the interface. Equally important, the facile synthetic method and the promising activity in the proton exchange membrane water electrolyzer, reaching 1 A cm -2 at 1.7 V with remarkable durability, enable potential scale-up applications. In conclusion, these findings provide a mechanistic understanding for designing active, stable and lower-cost electrocatalysts with well-defined structures for acidic OER.

58 GEOSCIENCES↗
DOE OSTI2026

Offshore Wind Farm Turbine and Energy Storage Optimization

Abstract This paper evaluates the technical and economic feasibility of repurposing decommissioned offshore oil and gas platforms as electrical substations for offshore wind projects in the U.S. Gulf of America, a region characterized by relatively low and highly variable wind speeds, extensive legacy offshore infrastructure, and exposure to merchant electricity markets. A unified techno-economic framework is developed using the Repurposing Offshore Infrastructure for Continued Energy (ROICE) Economic Model (REM) to integrate Gulfspecific wind resource assessment, commercial wind turbine performance, offshore infrastructure cost modeling, and wholesale electricity market exposure. Gulf wind speed data are vertically extrapolated to turbine hub height and combined with manufacturer power curves to compute annual energy production and capacity factors across a broad portfolio of commercial turbines, enabling identification of turbine designs best suited for low-wind offshore environments. Hourly electricity price data from the Midcontinent Independent System Operator (MISO) day-ahead market are incorporated to characterize revenue potential, price volatility, and the temporal alignment between wind generation and market conditions. In addition, a conceptual framework for offshore battery energy storage system (BESS) integration is developed to support future investigation of market-responsive energy shifting at repurposed platforms. Results from the turbine evaluation demonstrate that machines with lower cut-in wind speeds and earlier ‘rated-power’ characteristics significantly outperform larger, industry-standard offshore turbines for the same net power under Gulf wind conditions, underscoring the need for region-specific technology selection. Market analysis further reveals substantial price variability and limited intrinsic alignment between wind production and high-price periods, motivating consideration of operational flexibility mechanisms. While storage optimization is not implemented in this study, the REM framework establishes a transparent and replicable foundation for co-evaluating turbine selection, infrastructure constraints, and market exposure, providing a practical pathway for assessing the potential role of repurposed offshore platforms in enabling economically viable offshore wind development in the Gulf of America.

02 PETROLEUM↗
DOE OSTI2024

Entangling quantum logic gates in neutral atoms via the microwave-driven spin-flip blockade

The Rydberg dipole blockade has emerged as the standard mechanism to induce entanglement between neutral-atom qubits. In these protocols, laser fields that couple qubit states to Rydberg states are modulated to implement entangling gates. Here, in this study, we present an alternative protocol to implement entangling gates via Rydberg dressing and a microwave-field-driven spin-flip blockade. We consider the specific example of qubits encoded in the clock states of cesium. An auxiliary hyperfine state is optically dressed so that it acquires partial Rydberg character. It thus acts as a proxy Rydberg state, with a nonlinear light shift that plays the role of blockade strength. A microwave-frequency field coupling a qubit state to this dressed auxiliary state can be modulated to implement entangling gates. Logic gate protocols designed for the optical regime can be imported to this microwave regime, for which experimental control methods are more robust. We show that unlike the strong dipole-blockade regime usually employed in Rydberg experiments, going to a moderate-spin-flip-blockade regime results in faster gates and smaller Rydberg decay. We study various regimes of operations that can yield high-fidelity two-qubit entangling gates and characterize their analytical behavior. In addition to the inherent robustness of microwave control, we can design these gates to be more robust to laser amplitude and frequency noises at the cost of a small increase in Rydberg decay.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Control of airborne infectious disease in buildings: Evidence and research priorities

The evolution of SARS-CoV-2 virus has resulted in variants likely to be more readily transmitted through respiratory aerosols, underscoring the increased potential for indoor environmental controls to mitigate risk. Use of tight-fitting face masks to trap infectious aerosol in exhaled breath and reduce inhalation exposure to contaminated air is of critical importance for disease control. Administrative controls including the regulation of occupancy and interpersonal spacing are also important, while presenting social and economic challenges. Indoor engineering controls including ventilation, exhaust, air flow control, filtration, and disinfection by germicidal ultraviolet irradiation can reduce reliance on stringent occupancy restrictions. However, the effects of controls-individually and in combination-on reducing infectious aerosol transfer indoors remain to be clearly characterized to the extent needed to support widespread implementation by building operators. We review aerobiologic and epidemiologic evidence of indoor environmental controls against transmission and present a quantitative aerosol transfer scenario illustrating relative differences in exposure at close-interactive, room, and building scales. We identify an overarching need for investment to implement building controls and evaluate their effectiveness on infection in well-characterized and real-world settings, supported by specific, methodological advances. Finally, improved understanding of engineering control effectiveness guides implementation at scale while considering occupant comfort, operational challenges, and energy costs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Editorial for Special Issue: Grid-Interactive Efficient Buildings - Part 1

We are pleased to present starting with this volume of ASME Journal of Engineering for Sustainable Buildings and Cities (JESBC), a series of peer-reviewed papers related to some of ongoing research projects and applications of Grid-interactive Efficient Buildings (GEBs). As part of a new program initiated by the Building Technology Office (BTO) with the US Department of Energy (DOE), GEBs are characterized as highly energy-efficient buildings that are equipped with smart technologies and distributed energy resources that can be dynamically controlled to meet grid needs and minimize electricity system costs, while meeting occupants’ comfort and productivity requirements. In simple terms,DOE defines GEBs as efficient, connected, smart, and flexible buildings. The GEB technologies touch on the main topical areas of JESBC aimed at fostering sustainable buildings and cities by providing results of evaluation studies and demonstration projects. Indeed, one of the main goals for JESBC is to disseminate solutions to enhance the built environment energy efficiencies, low carbon services, inter-connectivity services, resilient multi-services, and healthy and comfortable living spaces.

buildings↗
DOE OSTI2022

Recent Advances and Prospects in Design of Hydrogen Permeation Barrier Materials for Energy Applications—A Review

The hydrogen infrastructure involves hydrogen production, storage and delivery for utilization with clean energy applications. Hydrogen ingress into structural materials can be detrimental due to corrosion and embrittlement. To enable safe operation in applications that need protection from hydrogen isotopes, this review article summarizes most recent advances in materials design and performance characterization of barrier coatings to prevent hydrogen isotopes’ absorption ingress and permeation. Barriers are crucial to prevent hydride formation and unwanted hydrogen effects to increase safety, materials’ lifetime and reduce cost for applications within nuclear and renewable energy. The coating may be applied on a material that requires protection from hydrogen pick-up, transport and hydride formation in hydrogen storage containers, in pipelines, spent nuclear fuel storage or in nuclear reactors. While existing, commercial coatings that have been much in use may be satisfactory for various applications, it is desirable to evaluate whether alternative coating concepts can provide a greater resistance to hydrogen isotope permeation along with other improved properties, such as mechanical strength and thermal resistance. The information presented here is focusing on recent findings within the past 5–7 years of promising hydrogen barriers including oxides, nitrides, carbon, carbide, MAX-phases and metals and their mechanical strength, hydrogen pick-up, radiation resistance and coating manufacturing techniques. A brief introduction to hydrogen permeation is provided. Knowledge gaps were identified to provide guidance for material’s research prospects.

08 HYDROGEN↗
DOE OSTI2020

Ambient Mass Spectrometry Rapid Analysis Methods for Trace Inorganics

The ability to rapidly detect and characterize the chemistry of trace quantities of inorganic species has multiple potential applications, including nuclear safeguards, nuclear forensics, and environmental monitoring. Some mass spectrometric methods currently exist, but they are costly and time-intensive, often destroying chemical information in the process of ionization. Ambient mass spectrometry is a relatively new analytical method in which samples are introduced to the mass spectrometer from atmospheric pressure, rather than from high vacuum. Ambient MS can operate with gentler ionization methods such as electrospray ionization (ESI) and paper-spray ionization (PSI), reducing fragmentation and preserving the chemical information of the analyte. This may be useful for determining the intended purpose of material in a sample. Strontium, part of nuclear fallout, is analyzed here to test these new analytical methods for viability. Electrospray ionization mass spectrometry functions by spraying the sample as a solution through a needle charged to a few kV. A nebulizing gas flows out along with the sample to create the spray before droplets enter a drying chamber with heated drying gas to remove the solvent molecules. From there bare analyte ions enter the mass spectrometer. This method has the advantage of relatively fast sample preparation and stable ionization. More stable ionization produces higher-quality data. Paper-spray ionization mass spectrometry operates by applying sample to a paper triangle. The paper triangle is then electrified in a metal clamp to a few kV before spray solvent is applied. The spray solvent transports analyte in the sample to the tip, where it is ionized and enters the mass spectrometer. One advantage of this method is reduced sample preparation - an analyst can even swipe a surface and cut a triangle directly from the swipe. The data analysis serves to rapidly process tens of samples all at once. The mass spectrometer outputs a file containing a few hundred spectra for each sample. The pipeline processes each one to identify peaks of interest. A full palette of data analysis metrics are generated, including plots of the raw data and plots of calculated values such as isotopic ratios. The pipeline filters the raw data to remove noise and background peaks, as well as peaks representing detector saturation. A heat map is generated to show the overall spectrum in mass-time- intensity space. Post-processing, we obtained the desired calibration curves, demonstrating that both ESI-MS and PSI-MS are viable methods for quantifying trace amounts of inorganics while retaining their chemical speciation information. ESI-MS demonstrated a linear response region of 100 ppb to 20 ppm, with a limit-of-detection of around 100 ppb. At top left is an example spectrum with the {sup 88}SrNO{sub 3} peak identified to show the retention of chemical speciation. PSI-MS showed a linear response region of around 1 ppb to 1 ppm, with a limit-of-detection of near 1 ppb. PSI-MS produced more erratic results than ESI-MS, likely due to the variability in ionization resulting from manual positioning of the paper triangle near the MS inlet. Future efforts will focus on improving the repeatability of PSI-MS as well as analyzing other species of interest such as cerium and uranium. Improvements will also be made to create more reliable data extraction and intelligent data filtering.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Role of Nuclear Science User Facilities (NSUF) in Nuclear Energy Materials Research

The Nuclear Science User Facilities (NSUF) is one of a diverse number of U.S. Department of Energy (DOE) user facilities established to provide researchers with the most advanced tools of modern science. The NSUF is unique and represents a consortium of capabilities distributed across the U.S. at twenty-one institutions. The NSUF is centered at and managed from the Idaho National Laboratory (INL), where it was originally founded, but it coordinates activities at twenty “partner” institutions that include universities, the Center for Advanced Energy Studies (CAES), national laboratories, and a nuclear industry vendor. These institutions have capabilities that include neutron, ion, and gamma irradiation, hot cells, advanced materials characterization equipment, and high-performance computing resources. Many of these capabilities were beyond reach for most researchers before NSUF. The NSUF provides researchers to access these capabilities at no cost to nuclear energy researchers to produce the highest quality research results to increase understanding of advanced nuclear energy technologies important to DOE-NE and support national priorities by adapting to the needs of DOE-NE programs, industry, and new innovative concepts for sustainable nuclear future.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Global biomass supply modeling for long-run management of the climate system

Bioenergy is projected to have a prominent, valuable, and maybe essential, role in climate management. However, there is significant variation in projected bioenergy deployment results, as well as concerns about the potential environmental and social implications of supplying biomass. Bioenergy deployment projections are market equilibrium solutions from integrated modeling, yet little is known about the underlying modeling of the supply of biomass as a feedstock for energy use in these modeling frameworks. We undertake a novel diagnostic analysis with ten global models to elucidate, compare, and assess how biomass is supplied within the models used to inform long-run climate management. With experiments that isolate and reveal biomass supply modeling behavior and characteristics (costs, emissions, land use, market effects), we learn about biomass supply tendencies and differences. The insights provide a new level of modeling transparency and understanding of estimated global biomass supplies that informs evaluation of the potential for bioenergy in managing the climate and interpretation of integrated modeling. For each model, we characterize the potential distributions of global biomass supply across regions and feedstock types for increasing levels of quantity supplied, as well as some of the potential societal externalities of supplying biomass. We also evaluate the biomass supply implications of managing these externalities. Finally, we interpret biomass market results from integrated modeling in terms of our new understanding of biomass supply. Overall, we find little consensus between models on where biomass could be cost-effectively produced and the implications. We also reveal model specific biomass supply narratives, with results providing new insights into integrated modeling bioenergy outcomes and differences. The analysis finds that many integrated models are considering and managing emissions and land use externalities of supplying biomass and estimating that environmental and societal trade-offs in the form of land emissions, land conversion, and higher agricultural prices are cost-effective, and to some degree a reality of using biomass, to address climate change.

09 BIOMASS FUELS↗
DOE OSTI2022

Characterization and Speciation of Marine Materials Using Synchrotron Probes: Guidelines for New Users

Synchrotron instruments are useful for marine studies because they make nondestructive measurements of chemical composition and speciation on small sample volumes and at low concentrations. Synchrotron beamtime is available without cost using a peer-reviewed proposal system. New users do not have to be synchrotron radiation experts to design a good experiment, but some guidance is needed to design and propose appropriate experiments. Here we present some of that guidance to encourage and increase access to synchrotron facilities for marine science. We provide advice and examples from experts on how to access these instruments, choose the optimal sample preparation, and avoid common pitfalls. We then present some examples of successful marine studies that use these techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Performance characterization of 5×5×12 mm 3 virtual Frisch-grid TlBr detectors

Thallium bromide (TlBr) is a promising material for semiconductor gamma-ray detectors due to its high atomic number (Tl = 81, Br = 35), high density (7.56 g/cm 3 ), and relatively low melting point (460 °C). These properties give TlBr several competitive features, including high detection efficiency, good energy resolution, and lower fabrication costs, making it suitable for applications requiring compact, highly sensitive gamma-ray detection systems. Recent developments of TlBr detectors have progressed toward thicker crystals, up to 10–20 mm, configured as pixelated or virtual Frisch-grid (VFG) devices with volumes >1 cm 3 . Despite these advances, challenges remain in practical implementation, including material and fabrication issues that affect energy resolution, device stability, performance, and longevity. These challenges are similar to those faced by HgI 2 and lead-halide perovskite detectors. Here, this study presents results from characterizing over 100 position-sensitive 5×5×12 mm 3 virtual Frisch-grid detectors fabricated by Radiation Monitoring Devices Inc. For a prototype Radioisotope Identification Device (RIID). The work identified factors limiting the performance and longevity of TlBr detectors, including stochastic response variations that limit the energy resolution to 2–3 % (at 662 keV) for most devices, performance degradation associated with defect decoration, and contact delamination. Addressing these issues advances the use of TlBr and similar materials for reliable, high-resolution gamma-ray spectroscopy.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Field Laboratory for Emerging Stacked Unconventional Plays in Central Appalachia

The goal of the Field Laboratory for Emerging Stacked Unconventional Plays (ESUP) in Central Appalachia project was to investigate and characterize the resource potential for multi-play production of emerging unconventional reservoirs in Central Appalachia. Project activities included drilling, logging and coring of a vertical characterization well drilled to basement to approximately 15,000 feet in depth. The data from drilling and well logs as well recovered core samples were used by the research team to support characterization of the geology and potential pay zones within the Nora Gas Field of southwestern Virginia and the greater Central Appalachian Basin. This project was led by Dr. Nino Ripepi of the Virginia Center for Coal and Energy Research (VCCER) in close collaboration with EnerVest Operating LLC (EnerVest). VCCER is housed in the Department of Mining and Minerals Engineering at Virginia Tech and was created by an Act of the Virginia General Assembly on March 30, 1977, as an interdisciplinary study, research, information, and resource facility for the Commonwealth of Virginia. EnerVest is a top tier, low-cost oil and natural gas company with a long history of traditional operating relationships with institutional investors as well as public and private companies. This report summarizes two major research activities: 1) Log and Core Analysis and 2) Numerical Modeling. Core analysis comprises the characterization efforts of the ESUP Field Laboratory and includes a discussion of the well logs run in the deep characterization wells, an initial analysis of those, the number, size and location of cores recovered, core testing and analysis done to date as well as plans for future analysis. Numerical modeling is comprised of seven major modeling efforts, including (1) Multiphysics shale transport modeling work, (2) the developed in-house compositional simulator studies, (3) reservoir modeling studies using GEM software, (4) fracture modeling work using EFRAC3D software and (5) numerical modeling efforts with ABAQUS software, (6) Numerical modeling efforts using FLAC3D and PFC3D, and (7) Automatic Machine Learning (AutoML) Studies.

02 PETROLEUM↗
DOE OSTI2022

Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps

Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.

54 ENVIRONMENTAL SCIENCES↗