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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

JHTDB-wind: a web-accessible large-eddy simulation database of a wind farm with virtual sensor querying

This paper introduces JHTDB-wind (https://turbulence.idies.jhu.edu/datasets/windfarms, last access: 11 November 2025), a publicly accessible database containing large-eddy simulation (LES) data from wind farms. Building on the framework of the Johns Hopkins Turbulence Database (JHTDB), which hosts direct numerical simulation (DNS) and some LES datasets of canonical turbulent flows, JHTDB-wind stores the 4D space–time history of the flow and provides users the ability to access and query the data via a web-based virtual sensor interface. The initial dataset comprises LES results from a large wind farm with 10×6 turbines, modeled using a filtered actuator line method, under conventionally neutral atmospheric conditions. These data comprise 1 h (hour) of flow field data (velocity, pressure, potential temperature deviation, subgrid-scale (SGS) eddy viscosity, and turbine forces, approximately 15 TB (terabytes) and wind turbine data – including both turbine-level operational quantities and blade-level aerodynamic quantities (approximately 1.3 TB) – stored in Zarr and Parquet formats, respectively. Data retrieval is facilitated by the giverny Python package, allowing remote users to query the database in Python or MATLAB (C and Fortran support are available for flow field data). This paper details the simulation setup and demonstrates data access through examples that analyze wind farm flow structures and turbine performance. The framework is extensible to future datasets, including the JHTDB-wind diurnal cycle simulation analyzed in Xiao et al. (2025).

17 WIND ENERGY↗
DOE OSTI2024

Atmospheric Radiation Measurement (ARM) airborne field campaign data products between 2013 and 2018

Airborne measurements are pivotal for providing detailed, spatiotemporally resolved information about atmospheric parameters and aerosol and cloud properties, thereby enhancing our understanding of dynamic atmospheric processes. For 30 years, the US Department of Energy (DOE) Office of Science supported an instrumented Gulfstream 1 (G-1) aircraft for atmospheric field campaigns. Data from the final decade of G-1 operations were archived by the Atmospheric Radiation Measurement (ARM) Data Center and made publicly available at no cost to all registered users. To ensure a consistent data format and to improve the accessibility of the ARM airborne data, an integrated dataset was recently developed covering the final 6 years of G-1 operations (2013 to 2018, https://doi.org/10.5439/1999133; Mei and Gaustad, 2024). The integrated dataset includes data collected from 236 flights (766.4 h), which covered the Arctic, the US Southern Great Plains (SGP), the US West Coast, the eastern North Atlantic (ENA), the Amazon Basin in Brazil, and the Sierras de Córdoba range in Argentina. These comprehensive data streams provide much-needed insight into spatiotemporal variability in the thermodynamic quantities and aerosol and cloud properties for addressing essential science questions in Earth system process studies. This paper describes the DOE ARM merged G-1 datasets, including information on the acquisition, data collection challenges and future potentials, and quality control processes. It further illustrates the usage of this merged dataset to evaluate the Energy Exascale Earth System Model (E3SM) with the Earth System Model Aerosol–Cloud Diagnostics (ESMAC Diags) package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Incorporating long-range dependence and fractal features in turbulence spectra

We introduce an advanced turbulence spectrum model developed from mathematical foundations from a covariance function class and empirically validated using extensive field data. This model captures the complex dynamics of long-range dependence, and fractal characteristics prevalent in riverine and atmospheric boundary layer (ABL) flows that are ignored by classical spectrum models, such as IEC (International Electrotechnical Commission) von Kármán and Kaimal model. The model delineates scaling behaviors across distinct frequency bands and offers substantial flexibility through five well-defined parameters each characterizing a distinct physical aspect of the velocity time series. A detailed procedure for obtaining each parameter from time series data is outlined. The comprehensive validations with field data from tidal currents and ABL flows substantiate the model’s fidelity in accurately replicating observed phenomena. This validation establishes the reliability of the proposed model and, when incorporated into stochastic full-field simulators such as TurbSim, demonstrates its potential to advance the predictive modeling and analysis of turbulent flows in environmental science and engineering contexts.

Cheng, Shyuan [Univ. of Illinois at Urbana-Champai↗
DOE OSTI2024

Thermally anisotropic building envelope for thermal management: finite element model calibration using field evaluation data

The thermally anisotropic building envelope (TABE) is an active building envelope that redistributes thermal loads in response to weather conditions and building energy demand. Conductive layers throughout the TABE distribute low-grade heat among hydronic loops, altering heat flow direction and intensity. Finite element models of TABE roof and wall panels were developed and calibrated using field evaluation data. The calibration results showed that heat flux differences between the experimental data and finite element models averaged –0.42% and 3.57%, with a maximum mean square error of 1.78 and 3.96 for roof and wall panels, respectively. A reduction in heat flux from the environment to the building living space over the entire testing period (weeks in July/August) was found to be 85% for roof panels and 335% (load reversed) for wall panels. Finally, these results indicate TABE can effectively harness low-grade thermal energy sources to achieve high energy efficiency and promote demand-side management.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

In-Field Performance of Biomass Balers

Herbaceous biomass will contribute significantly to meeting renewable energy goals. Harvesting equipment for hay is generally suitable for mowing, raking, and baling grasses such as switchgrass; however, there is a need for field data to better understand machine performance in energy crops. The purpose of this study was to collect field data to estimate baler field capacity, throughput, and speed. Data gathered with a Differential Global Positioning System (DGPS) unit during baling provided time-motion studies of baler productivity. Six fields were used to compare field capacity, speed, and throughput results from four round balers and one large-square baler. The results show that in-field performance of round balers is significantly affected by yield, but that the relationship can be represented with machinery management concepts, knowledge of maximum throughput, and wrap-eject time. Baler performance will be overestimated if the yield, maximum throughput, and wrap-eject time are not correctly accounted for.

Grisso, Robert “Bobby” (ORCID:0000000267893558)↗
DOE OSTI2020

Development of a grain growth model for U 3 Si 2 using experimental data, phase field simulation and molecular dynamics

The purpose of this work is to develop a model for normal grain growth in U 3 Si 2 . The average grain boundary energy was determined from previously published molecular dynamics simulations. The grain growth kinetics were quantified at various temperatures by annealing nanocrystalline samples. Here, the mobility was determined by comparing phase field grain growth simulations to the experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Prediction and Analysis of Utah FORGE Injection Activities using a Coupled Thermo-hydro-mechanical and Earthquake (THM+E) Modeling Workflow

A coupled thermo-hydro-mechanical (THM) numerical workflow that is capable of modeling seismic slip is critical for the successful development of enhanced geothermal systems (EGS). By integrating key physical processes, this workflow enables accurate simulation of temperature and pressure diffusions, stress changes, and induced seismicity. As a result, it serves as a vital tool for predicting induced seismicity and optimizing reservoir stimulation strategies. The Utah FORGE (Frontier Observatory for Research in Geothermal Energy) project, located near Milford, Utah, is a U.S. Department of Energy initiative aimed at advancing EGS technology. In April 2024, eight new stimulation stages (Stages 3R-10) were conducted in well 16A (injection well) subsequent to the first series of stimulation (Stages 1-3) performed in April, 2022. To monitor the induced seismicity, geophones were deployed in wells 58-32, 56-32, and 78B-32, while fiber optic cables were also installed in wells 16B, 78-32, and 78B-32 to collect microseismic data and detect frac hits Preliminary analyses of microseismic catalogs and fiber optic data suggest that the stimulated fractures in Stages 3R–6 closely align with that generated during Stage 3, indicating that the new stimulations were likely reactivating the previously stimulated fracture. To better understand the underlying process, a comprehensive modeling approach that can accurately capture thermal, hydrological, mechanical, and seismic responses is essential. In this work, we propose and utilize a coupled thermo-hydro-mechanical and earthquake (THM+E) simulation workflow to numerically investigate the stimulation activities on well 16A. The specific objective is to confirm whether the new stimulation stages (Stages 3R–6) reactivated fractures previously stimulated during Stage 3. For this purpose, we perform THM+E simulations individually for Stages 3, 3R, 4, and 5, incorporating the discrete fracture networks (DFNs) created by the plane-fitting technique based on the microseismic catalogs. The simulation workflow consists of two separate models: a THM model and an earthquake model, coupled in a one-way manner. Detailed descriptions of the workflow are provided in Section 3. Simulation results are presented in terms of injection pressure, permeability evolution, and predicted seismic catalogs, which are then compared with field data for further analyses. This report is structured as follows. In Section 2, we present detailed analyses of the field data and propose the hypothesis that the new stimulation stages (Stages 3R–6) were probably reactivating the previously stimulated fractures in Stage 3. In Section 3, we introduce the coupled THM+E workflow and the problem setup to validate our hypothesis, followed by the simulation results for each stage in Section 4. Meanwhile, discussions are included to analyze the model predictions and their comparison with field data. Lastly, we conclude the report and outline future plans in Section 5.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Impacts of Year-to-Year Weather Variability and Inter-Panel Spacing on Crop Yields in a Massachusetts Agrivoltaics System

This presentation summarizes recently published work on agrivoltaic field data and irradiance modeling. The body of agrivoltaic field data is still growing, and crop responses to different solar configurations under different local climates are highly varied. We investigate the impact of adding spacing between adjacent solar panels in a fixed-tilt system to improve light diffusion to crops. For four crops (broccoli, peppers, kale, Swiss chard) grown across 3 years in an agrivoltaic system in Massachusetts, we found that only kale had a linearly increasing trend as the inter-panel spacing increased from 0.6 m to 1.5 m (2 ft to 5 ft). However, there were significant year-to-year differences in the yield of agrivoltaic versus control fields. Agrivoltaic and full sun fields produced equivalent yields in a hot, dry year, whereas the full-sun control beds produced more salable yield for all four crops in a warm, wet year. This demonstrates variability of agricultural outcomes and the need for more multi-year studies to ensure agrivoltaic impacts are not under- or overestimated.

14 SOLAR ENERGY↗
DOE OSTI2020

Application of Gaussian Bayes classifier to differentiate chlorine-based chemical agents

The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical warfare agents (CWA) non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a mechanically-cooled high-purity germanium (HPGe) detector. One of the difficult technical challenges is to discriminate the chlorine-based chemical agents. Especially, CN, CNB, CNS and CG have similar chemical compositions to make it hard to discriminate them with a higher confidence. Current identification algorithms for PINS systems with 252-Cf sources have been improved and updated continuously as more field data became available, and new algorithms was studied to complement the current algorithms by adopting the Gaussian Bayes classifier. These new algorithms were intended to be applied to a subset of chlorine-based chemical agents, and their main goal is discriminate CN, CNB, CNS and CG with their ratios of the chlorine neutron inelastic 1763keV peak to the chlorine thermal neutron capture 1959keV peak, which is referred to as the “Cl i/c” or “clic” ratio in this study. The Cl i/c ratios were assumed to follow Gaussian distributions with the means and the standard deviations unique to their corresponding chemical agents. The prior probabilities of these four chemical agents were optimized with a collection of field data to achieve the best performance in terms of precision or positive predictive value (PPV). Finally, their posterior probabilities as functions of the Cl i/c ratio were implemented in the current version of PINS analysis software in order to be tested with more field data.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

A New Modeling Framework for Multi-Scale Simulation of Hydraulic Fracturing and Production from Unconventional Reservoirs

This paper describes a new modeling framework for microscopic to reservoir-scale simulations of hydraulic fracturing and production. The approach builds upon a fusion of two existing high-performance simulators for reservoir-scale behavior: the GEOS code for hydromechanical evolution during stimulation and the TOUGH+ code for multi-phase flow during production. The reservoir-scale simulations are informed by experimental and modeling studies at the laboratory scale to incorporate important micro-scale mechanical processes and chemical reactions occurring within the fractures, the shale matrix, and at the fracture-fluid interfaces. These processes include, among others, changes in stimulated fracture permeability as a result of proppant behavior rearrangement or embedment, or mineral scale precipitation within pores and microfractures, at µm to cm scales. In our new modeling framework, such micro-scale testing and modeling provides upscaled hydromechanical parameters for the reservoir scale models. We are currently testing the new modeling framework using field data and core samples from the Hydraulic Fracturing Field Test (HFTS), a recent field-based joint research experiment with intense monitoring of hydraulic fracturing and shale production in the Wolfcamp Formation in the Permian Basin (USA). Below, we present our approach coupling the reservoir simulators GEOS and TOUGH+ informed by upscaled parameters from micro-scale experiments and modeling. We provide a brief overview of the HFTS and the available field data, and then discuss the ongoing application of our new workflow to the HFTS data set.

58 GEOSCIENCES↗
DOE OSTI2023

Systems and methods for generating a graded lattice structure and their application to additive manufacturing

Systems and methods for generating graded lattice structures that can be used as infill for additively manufactured articles. Tailored sectioning and field-based smoothing are modified polygon, e.g., circle, packing algorithms that adjust the size of the circles based on physical field data to adapt the infill generation process to a field expected to be experienced by the article. Molecular dynamically generated lattice infill is based on force balancing a node distribution instead of a circle packing. Field data can be utilized to adjust the spacing of the node distribution according to a force balance equilibrium model that accounts for the field expected to be experienced by the article being additively manufactured. The resultant non-uniform honeycomb structures from tailored sectioning, field-based smoothing, and force-balancing robustly and efficiently address the connection issues with traditional non-uniform lattice structures.

Kim, Seokpum↗
DOE OSTI2026

Data for Multisite Field Evaluation of Oil Accumulation and Agronomic Performance in Grain and Sweet Sorghums Engineered for Lipid Hyperaccumulation

Oil sorghum (OS) has been developed by engineering grain (TX430) and sweet (Ramada) genetic backgrounds to accumulate triacylglycerols (TAG) in vegetative tissues as an energy-dense feedstock for sustainable aviation fuel (SAF) and other biofuels. This study evaluated two TX430 OS lines (TxHO-2, TxHO-3) and two Ramada OS lines (RmHO-1, RmHO-2) alongside wild-type (WT) lines in NE and IL over 2 years (2023–2024) to quantify genotype × environment effects on agronomic performance and TAG accumulation. Across four environments, TX430 OS lines showed average TAG concentrations of 15.0 g kg−1 in leaves and 12.8 g kg−1 in stems, approximately 19-fold higher than WT. Ramada OS lines accumulated 26.1 g kg−1 in leaves and 12.3 g kg−1 in stems, approximately 25-fold and 13-fold increases over WT, respectively. OS lines in TX430 exhibited an 18% reduction in biomass (8.4 vs. 9.9 Mg ha−1 for WT), while Ramada OS lines had similar WT biomass (18.3 vs. 19.9 Mg ha−1 for WT). Among TX430 OS lines, TxHO-2 achieved the highest TAG yield (190 kg ha−1), while RmHO-1 led the Ramada lines (335 kg ha−1) due to higher biomass and similar TAG concentration. Enhanced TAG accumulation increased N, P, and K removal in TX430 lines but not in Ramada lines. Structural carbohydrate and ash concentration were unaffected. Overall, results confirm vegetative lipid accumulation as a viable strategy for high-biomass sorghum, supporting its potential as a dual-purpose feedstock for SAF. Future work should focus on minimizing biomass yield penalties and improving nutrient use efficiency in oil sorghum systems.

Agronomy↗
DOE OSTI2023

Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest

Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in Amazon forests. Images from medium-resolution optical satellites combined with extensive field data have allowed researchers to assess patterns of windthrow tree-mortality and to monitor forest recovery over decades of succession in different regions. Although satellites with high spatial-resolution have become available in the last decade, they have not yet been employed for the quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November 2015. Remote sensing estimates of windthrow tree-mortality were produced from Spectral Mixture Analysis and evaluated with forest inventory data (i.e., ground true) by using Generalized Linear Models. Field measured windthrow tree-mortality (3 transects and 30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Although the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3%, p < 0.001), Landsat 8 had the most accurate results and efficiently captured field-observed variations in windthrow tree-mortality across the entire gradient of disturbance (Sentinel 2 and WorldView 2 produced the second and third best results, respectively). As expected, mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of model fits showed the opposite pattern. We suggest that this reflects the influence of a relatively minor disturbance, such as defoliation and crown damage, and the fast growth of natural regeneration, which were not measured in the field nor can be captured by coarser resolution imagery. Our results validate the reliability of Landsat imagery for assessing plot-to-landscape patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Satellites with high spatial resolution can improve estimates of windthrow severity by allowing the quantification of crown damage and mortality of lower canopy and understory trees. However, this requires the validation of remote sensing metrics using field data at compatible scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Developing and Testing a Novel Stochastic Ice Microphysics Parameterization for Cloud and Climate Models Using ARM Field Campaign Data (Final Progress Report)

The major goals of this project were: 1) to use field campaign measurements from DOE’s Atmospheric Radiation Measurement (ARM) program to characterize variability of important parameters describing properties of ice particles in the atmosphere; 2) based on this observational analysis, to develop a parameterization scheme for weather and climate models that stochastically varies these parameters, and implement the new scheme into a weather model called the Weather Research and Forecasting model (WRF); 3) to use WRF coupled with the new stochastic scheme to simulate ARM field campaign thunderstorm cases and analyze how accounting for this parameter variability affects the model simulations. This work was performed jointly between the National Center for Atmospheric Research, University of Oklahoma, and University of Utah. To accomplish these goals, we extended an approach previously developed to characterize the variability in the size distribution of ice particles to parameters that are explicitly represented in models (i.e., relationships between ice particle mass and size, and between particle fall velocity and size). Our project was, to our knowledge, the first to apply observationally-constrained estimates of this parameter variability describing mass-size and fall velocity-size in a modeling framework. Our results showed efficacy of the approach, evaluated using ARM observations. Similarly, to our knowledge, work in this project was the first to propose and evaluate in detail a stochastic approach for unresolved turbulent mixing in high-resolution model simulations against detailed, benchmark large eddy simulations and ARM observations. Results showed some promising behavior, particularly with increased mixing and dilution of air in thunderstorm cores with surrounding environmental air, bringing the stochastic simulations closer to the benchmark large eddy simulations; however, results were somewhat degraded using stochastic mixing compared to observations from the AMIE/DYNAMO field campaign. This project also further refined and applied a modeling methodology called “piggybacking” that can robustly separate dynamical and thermodynamic impacts of model changes, and comparison studies of different models based on cases developed from ARM observations. Finally, this project directly supported three graduate students who completed their PhDs as well as a postdoctoral research fellow.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DOE Cold Climate Heat Pump Challenge Field Validation: Data Collection and Analysis Plan

This document details the data collection, storage, and analysis plan and methodologies for conducting the field validation portion of DOE’s Cold Climate Heat Pump (CCHP) Challenge. The study is focused on validating the in-field heating performance of prototypical CCHPs that have successfully demonstrated that they meet or exceed the Challenge specification in a laboratory setting. The units that have passed laboratory testing will be installed in real homes along with sensors and loggers for monitoring performance over an entire heating season. Data collected through monitoring will be cleaned and stored in a secure database. The data will be analyzed for calculating the key metrics defined by the Challenge including heating capacities at low outdoor air temperatures (below 32 °F), efficiency in terms of the Coefficient of Performance (COP), switchover temperatures and auxiliary heat staging. Demand Response (DR) capabilities will also be tested using specially designed DR events. In most cases, shoulder season and cooling season performance will also be determined and reported.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Ambient-Frequency-Data Based System-Level Inertia Estimation Using Physical Equation and its Practice on Hawaii Islands

Here, in this paper, a practical ambient-frequency-data-based inertia estimation method using a physical equation is proposed and validated by real measurement data from Hawaii island grids. With high renewable penetration, accurate inertia estimation is important and urgent. Ambient frequency oscillation always exists in power grids, so the proposed method has advantages of real-time inertia estimation and no need for additional disturbances. This paper first developed the physical equation for inertia estimation to offer a clear mechanism for easy implementation in practice. To apply the proposed method in actual grids, a practical method to extract the ambient frequency oscillation from frequency measurement is further proposed. The inertia estimation using the physical equation is validated by KIUC simulation data and HECO field data, in which error rates are around 2% and 8%, respectively. Practical inertia estimation is challenging due to the large amounts of resources contributing to the power grid's effective inertia, but the method provided in this paper can offer a novel way for practical inertia estimation, which can help renewable penetration to boost carbon-free grid.

14 SOLAR ENERGY↗
DOE OSTI2025

Data for "Field-scale Evaluation of Ecosystem Service Benefits of Bioenergy Switchgrass"

Purpose-grown perennial herbaceous species are nonfood crops specifically cultivated for bioenergy production and have the potential to secure bioenergy feedstock resources while enhancing ecosystem services. This study assessed soil greenhouse gas emissions (CO2 and N2O), nitrate (NO3-N) leaching reduction potential, evapotranspiration (ET), and water-use efficiency (WUE) of bioenergy switchgrass ( Panicum virgatum L.) in comparison to corn ( Zea mays L.). The study was conducted on field-scale plots in Urbana, IL, during the 2020–2022 growing seasons. Switchgrass was established in 2020 and urea-fertilized at 56 kg N ha−1 year−1. Corn management followed best management practices for the US Midwest, including no-till and 202 kg N ha−1 year−1 fertilization, applied as urea–ammonium nitrate (32%). Our results showed lower direct N2O emissions in switchgrass compared to corn. Although soil CO2 emissions did not differ significantly during the establishment year, emissions in subsequent years were over 50% higher in switchgrass than in corn, likely due to increased belowground biomass, which was over five times higher in switchgrass. Nitrate-N leaching decreased as the switchgrass stand matured, reaching 80% lower than in corn by the third year. Differences in ET and WUE between corn and switchgrass were not significant; however, results indicate a trend toward reduced WUE in switchgrass under drought, driven by lower aboveground biomass production. Our study demonstrates that switchgrass can be implemented at a commercial scale without negatively impacting the hydrological cycle, while potentially reducing N losses through nitrate-N leaching and soil N2O emissions, and enhancing belowground C storage.

field data↗
DOE OSTI2024

Self‐Potential Tomography Preconditioned by Particle Swarm Optimization—Application to Monitoring Hyporheic Exchange in a Bedrock River

Abstract A self‐potential (SP) data‐inversion algorithm was developed and tested on an analytical model of electrical‐potential profile data attributed to single and multiple polarized electrical sources. The developed algorithm was then validated by an application to SP‐monitoring field data measured on the floodplain of East Fork Poplar Creek, Oak Ridge, Tennessee, to image electrical sources in areas conducive to preferential flow into the flood plain from the bedrock‐lined riverbed. The algorithm combined stochastic source‐localization by particle‐swarm‐optimization (PSO) of electrical sources characterized by simplified geometries with source tomography by regularized weighted least‐squares minimization of a quadratic objective function. Prior information was incorporated by preconditioning the tomography algorithm by PSO results. Variable percentages of random noise were added to analytical‐model data to evaluate the algorithm performance. Results indicated that true parameters of single‐source models were inverted and approximated with small residual error, whereas inversion of analytical‐model data representing multiple electrical sources accurately approximated the locations of the sources but miscalculated some parameters because of the non‐uniqueness of the inverse‐model solution. Source tomography applied to analytical model data during testing produced a spatially continuous parameter field that identified the locations of point‐scale synthetic dipole sources of electrical current flow with varying degrees of accuracy depending on the prior information incorporated into the tomography. When applied to SP‐monitoring field data, the algorithm imaged electrical sources within a known fault that intersects the bedrock riverbed and flood plain of East Fork Poplar Creek and depicted dynamic electrical conditions attributed to hyporheic exchange.

54 ENVIRONMENTAL SCIENCES↗