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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

UAVSAR Program: Initial Results from New Instrument Capabilities

UAVSAR is an imaging radar instrument suite that serves as NASA's airborne facility instrument to acquire scientific data for Principal Investigators as well as a radar test-bed for new radar observation techniques and radar technology demonstration. Since commencing operational science observations in January 2009, the compact, reconfigurable, pod-based radar has been acquiring L-band fully polarimetric SAR (POLSAR) data with repeat-pass interferometric (RPI) observations underneath NASA Dryden's Gulfstream-III jet to provide measurements for science investigations in solid earth and cryospheric studies, vegetation mapping and land use classification, archaeological research, soil moisture mapping, geology and cold land processes. In the past year, we have made significant upgrades to add new instrument capabilities and new platform options to accommodate the increasing demand for UAVSAR to support scientific campaigns to measure subsurface soil moisture, acquire data in the polar regions, and for algorithm development, verification, and cross-calibration with other airborne/spaceborne instruments.

P-band polarimetry↗
DOE OSTI2024

Autotrophic biofilms sustained by deeply sourced groundwater host diverse bacteria implicated in sulfur and hydrogen metabolism

Abstract Background Biofilms in sulfide-rich springs present intricate microbial communities that play pivotal roles in biogeochemical cycling. We studied chemoautotrophically based biofilms that host diverse CPR bacteria and grow in sulfide-rich springs to investigate microbial controls on biogeochemical cycling. Results Sulfide springs biofilms were investigated using bulk geochemical analysis, genome-resolved metagenomics, and scanning transmission X-ray microscopy (STXM) at room temperature and 87 K. Chemolithotrophic sulfur-oxidizing bacteria, including Thiothrix and Beggiatoa , dominate the biofilms, which also contain CPR Gracilibacteria, Absconditabacteria, Saccharibacteria, Peregrinibacteria, Berkelbacteria, Microgenomates, and Parcubacteria. STXM imaging revealed ultra-small cells near the surfaces of filamentous bacteria that may be CPR bacterial episymbionts. STXM and NEXAFS spectroscopy at carbon K and sulfur L 2,3 edges show that filamentous bacteria contain protein-encapsulated spherical elemental sulfur granules, indicating that they are sulfur oxidizers, likely Thiothrix . Berkelbacteria and Moranbacteria in the same biofilm sample are predicted to have a novel electron bifurcating group 3b [NiFe]-hydrogenase, putatively a sulfhydrogenase, potentially linked to sulfur metabolism via redox cofactors. This complex could potentially contribute to symbioses, for example, with sulfur-oxidizing bacteria such as Thiothrix that is based on cryptic sulfur cycling. One Doudnabacteria genome encodes adjacent sulfur dioxygenase and rhodanese genes that may convert thiosulfate to sulfite. We find similar conserved genomic architecture associated with CPR bacteria from other sulfur-rich subsurface ecosystems. Conclusions Our combined metagenomic, geochemical, spectromicroscopic, and structural bioinformatics analyses of biofilms growing in sulfide-rich springs revealed consortia that contain CPR bacteria and sulfur-oxidizing Proteobacteria, including Thiothrix , and bacteria from a new family within Beggiatoales. We infer roles for CPR bacteria in sulfur and hydrogen cycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

An automated system for continuous monitoring of CO 2 geosequestration using multi-well offset VSP with permanent seismic sources and receivers: Stage 3 of the CO2CRC Otway Project

Time-lapse seismic is an essential tool for monitoring CO 2 injection into the subsurface but suffers from high cost and long intervals between surveys. An alternative approach is continuous monitoring using permanent sources and receivers capable of mapping CO 2 plume evolution on an almost daily basis and drastically reduce human effort, cost and survey turnaround. As part of the CO2CRC Otway project, a continuous monitoring system is employed to monitor a small (15,000 tonnes) CO 2 injection within the CO2CRC Otway Project. The permanent monitoring system employs distributed acoustic sensors (DAS) installed in boreholes as seismic receivers and seismic orbital vibrators (SOV) as seismic sources. The monitoring approach is based on vertical seismic profiling using five wells equipped with DAS cemented behind the casing and nine SOVs and is acquiring the data continuously in an autonomous manner. The data is then automatically processed on-site producing a seismic image every 2.5 days, which is then transmitted to a remote office. The data has good repeatability with NRMS of about ~10-15%.

58 GEOSCIENCES↗
NASA NTRS1985

Thermal Infrared Observations of Lava Flows During the 1984 Mauna Loa Eruption

Thermal infrared videotape images of the flowing lava streams and the vent areas at 10.6 microns were made, as well as some broadband images in the 8 to 12 micron range (for gas plume detection). These data were calibrated with on-site hand-held radiometer measurements, in-flow thermocouple measurements, and with later laboratory kiln measurements. Infrared video data are useful in quantitatively assessing the pattern and mode of flow thermal losses, particularly with regard to radiative losses from established/incipient floating crust. The general cooling of the flows downstream was readily apparent. Upper reaches of the active flow exhibited nearly crust-free main channels, radiating at about 700 to 800 degrees C. Below about the 7500 foot level (about 8 km from the vent) the flows formed nearly continuous crust and tended to spread, become less well-defined and founder due to a reduction in slope. Nevertheless, in thermal IR observations, the surface trace of the active subsurface channel was visible, radiating at about 500 to 700 degrees C. At the active flow front, most solid crust radiated at temperatures less than 500 to 600 degrees C, however bright high temperature interiors (approximately 900 to 1000 degrees C) were clearly visible though evolving fissures.

Pieri, D. C.↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
NASA NTRSDate not supplied

The Project “Analogs for VENus’s GEologically Recent Surfaces” (AVENGERS): Unveiling Active Volcanism on Venus

The recently selected missions to Venus opened a new era for the exploration of the Earth’s hellish twin sister. One of the key questions that the selected missions to Venus will address is whether the planet is volcanically active. The identification of ongoing volcanic activity on Venus can indeed unveil important clues on the geologic evolution of the planet, also helping us to better understand how volcanic and atmospheric processes work on Earth, especially those related to the climate change produced by greenhouse gases. To fully contextualize the higher resolution datasets to be provided by the future missions to Venus, we need to be able to exploit the wealth of data to be produced in the best possible way. Different types of instruments will indeed perform unprecedent orbiting and in-situ investigations of the surface and subsurface of Venus. To this regard, the Project “Analogs for VENus’s GEologically Recent Surfaces” (AVENGERS) aims to build a comprehensive database of Terrestrial active volcanoes which may act as suitable analogs for identifying ongoing volcanic activity on Venus. To do this, the project AVENGERS will combine the implementation of terrestrial orbiter radar data analysis techniques (i.e., radar interferometry) with infrared and laboratory analyses of lava flow samples from Terrestrial active volcanoes. As a first suitable analog of the Project AVENGERS, we selected Mount Etna in Sicily for a direct comparison with potentially active volcanic structures of Venus, and with Idunn Mons, one of the most promising possibly active volcanoes of Venus. Given its composite nature, Mount Etna offers the opportunity to identify and study multiple types of eruptive products (both effusive and explosive) from the analysis of high-resolution radar images. The frequent eruptions characterizing this volcano will also allow to test techniques of radar interferometry. Moreover, its ease of access facilitates geologic field trips and in-situ investigations with related sample retrieving. Ongoing infrared laboratory analysis on several altered and unaltered samples retrieved by Mount Etna will help to distinguish spectral signatures of fresh vs weathered volcanic deposits.

P. D’Incecco↗
DOE OSTI2025

Fracture Network Quantification for Enhanced Reservoir Characterization in CO2 Storage Sites

Presentation slides on “Fracture Network Quantification for Enhanced Reservoir Characterization in CO2 Storage Sites” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. Accurate reservoir characterization is fundamental for the safe and efficient operation of carbon capture, utilization, and storage (CCUS) projects. This process is not only critical during initial site selection but also plays a key role in continuously updating reservoir geomodels throughout injection operations to effectively manage subsurface storage sites. Our primary objective is to develop a comprehensive understanding of fracture networks within CO2 injection reservoirs, which are critical for predicting the behavior of injected fluids, ensuring storage integrity post-injection, and mitigating risks such as leakage or induced seismicity.

artifical intelligence / machine learning (AI/ML)↗
DOE OSTI2025

Maps of growing season gross primary production and net ecosystem exchange for Council Road Mile Marker 71, Seward Peninsula, Alaska, [2017-2023]

This data archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025a). Murphy et al. (2025a) evaluated whether incorporating observed Arctic vegetation heterogeneity into ELM, the land model of the Department of Energy’s Energy Exascale Earth System Model (E3SM), improved simulations of tundra carbon cycling. The associated model archive can be found at Murphy et al. (2025b). The study focused on the spatial patterns and net landscape-level growing season productivity and carbon uptake. As part of this evaluation, observationally derived maps of average growing season (June–August) net ecosystem exchange (NEE) and gross primary production (GPP) were developed for the same domain. These maps, which form the dataset described here, integrate eddy covariance flux tower, remote sensing, and vegetation community data to provide spatially explicit benchmarks for model evaluation. The maps provide spatially explicit estimates of average growing season NEE and GPP across 13 tundra vegetation communities within the study domain. By combining flux tower observations with Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral imagery and drone-based normalized difference vegetation index (NDVI), these maps capture the heterogeneity of carbon fluxes associated with different Arctic vegetation types. While they represent average seasonal conditions rather than interannual variability, the maps provide a unique dataset for evaluating model performance, comparing vegetation community contributions to landscape-scale carbon cycling, and supporting regional analyses of Arctic carbon dynamics. This data archive contains 5 m resolution maps of vegetation communities, vegetation community average growing season GPP, and vegetation community average growing season NEE (three *.tif files), a User’s Guide (*pdf file), and Table 1 of the User’s Guide displaying vegetation community coverage and average growing season NEE and GPP values (*.csv file).

Murphy, Bailey [ORNL] (ORCID:0000000203995221)↗
NASA NTRS2008

Crack Offset Measurement With the Projected Laser Target Device

The device and associated analysis methodology summarized in this report were developed for the purpose of estimating the size of discontinuities in the surface of the foam that covers the Space Shuttle External Tank. These surface offsets are thought to be due to subsurface cracks in the foam insulation. The mathematical analysis and procedure described here provide a method to quantity the dimensions of the crack offset in a direction perpendicular to the surface, making use of the projected laser target device (PLTD) tool and a laser line projector. The keys to the construction and use of the PLTD are the following geometrical design requirements: Laser dots are on a square grid: length on a side. Laser beams are perpendicular to projected surface. Beams are parallel out to the distance being projected. The PLTD can be used to (1) calibrate fixed cameras of unknown magnification and orientation (far-field solution); (2) provide equivalent calibration to multiple cameras, previously achieved only by the use of known target points, for example, in 3.D foreign-object debris tracking on a fixed launch platform; (3) compute scaling for conventional 2.D images, and depth of field for 3.D images (near-field solution); and (4) in conjunction with a laser line projector, achieve accurate measurements of surface discontinuity (cracks) in a direction perpendicular to the surface.

Source record↗
DOE OSTI2022

Dynamic development of geochemical reaction fronts during hydraulic stimulation of shale

Injection of acidic hydraulic fracture fluid (HFF) into shale formations for unconventional oil/gas production results in chemical reactions in the shale matrix that can alter fluid transport. Here, we report the results of set of experiments designed to evaluate the impact of calcite dissolution as a function of carbonate mineral content on matrix chemical reactivity and pore-space modification concomitant with imbibition. In this study, we tracked acidic HFF transport in four samples of Wolfcamp shale with calcite contents varying from 4% to 59% by monitoring the rate and spatial extent of bromide tracer transport using synchrotron-based X-ray fluorescence microprobe (XFM) imaging. Concurrently, we also carried out XFM imaging of the spatial distribution of Ca in the Wolfcamp shale cores (as a proxy of calcite distribution). Our approach thus yields a direct record of time-resolved selective ion transport resulting from the penetration of acidic HFF and the associated mineral transformations in the shale cores. We show that the variability in calcite content of Wolfcamp shale samples can directly affect the rate and spatial extent of imbibition. Although reaction of the acidic HFF with carbonates in shales enhances calcite dissolution and increases porosity, the spatial extent of calcite dissolution in the shale matrix is limited due to a rapid neutralization of pH. The relative abundance and spatial distribution of calcite control the chemical saturation state of the HFF progressing into the matrix. As a result, calcite has a major impact on the spatial extent and rate of matrix alteration and thus on HFF transport during subsurface reservoir stimulation. Consequently, increased calcite content in the shale matrix inhibits the spatial extent of the pore-volume increase and, by extension, the spatial extent and rate of imbibition. Our results thus show that the overall rates of calcite dissolution approach the rates of acidic HFF transport (i.e., Damköhler number ~1), which could contribute to the efficiency of subsurface reservoir stimulation. A better understanding of HFF-calcite reaction rates is crucial for improving the prediction and optimization of fluid transport across HFF-shale interfaces during hydraulic fracturing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Phase II Field Demonstration at Lansing Smith Generating Plant, Southport, Florida (Final Report)

The Final Technical Report: Field Test Design and Pressure Management Strategies for Phase II Field Demonstration of Optimal Pressure Control, Plume Management, and Produced Water Strategies presents the culmination of multi-year efforts under the U.S. Department of Energy’s Brine Extraction and Storage Test (BEST) program, led by EPRI in partnership with Gulf Power at the Plant Smith site near Panama City, Florida. The project was designed to evaluate and demonstrate the technical feasibility of managing subsurface pressures and fluid movement associated with large-scale CO₂ injection, using low-salinity water as a proxy fluid. Through a combination of field injection testing, reservoir modeling, and optimization studies, the research team developed and refined active and passive brine extraction strategies aimed at controlling injection-induced pressure buildup, mitigating risks of fault activation, and managing plume migration. The field demonstration incorporated a new injection well (TIW-2), a new monitoring/extraction well (TEMW-A), and an existing well (TIW-1) repurposed for passive pressure relief. Complementary geophysical monitoring was designed to track plume development and evaluate the effectiveness of pressure management strategies. The report details the integrated workflow encompassing wellfield development, regulatory permitting, model calibration, and survey design. It includes a comprehensive electromagnetic (EM) modeling and inversion study used to develop a cost-effective, time-lapse geophysical monitoring plan capable of imaging the evolving low-salinity plume within the highly saline Lower Tuscaloosa formation. Reservoir simulation results guided the design of the 17-month injection program and the timing of active extraction to maintain formation pressures below a hypothetical fault reactivation threshold. Supporting analyses evaluated potential injectivity risks related to clay fines migration and geochemical incompatibility, identifying practical mitigation measures such as salinity and pH control. The final design recommends a series of crosswell EM surveys—conducted before, during, and after injection—as the most effective and economical approach for plume imaging, supplemented by continuous downhole pressure and flow monitoring. Collectively, the study provides a field-ready framework for cost-effective pressure management and monitoring in support of future CO₂ storage projects.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2009

Evaluation of Surface and Near-Surface Melt Characteristics on the Greenland Ice Sheet using MODIS and QuikSCAT Data

The Greenland Ice Sheet has been the focus of much attention recently because of increasing melt in response to regional climate warming. To improve our ability to measure surface melt, we use remote-sensing data products to study surface and near-surface melt characteristics of the Greenland Ice Sheet for the 2007 melt season when record melt extent and runoff occurred. Moderate Resolution Imaging Spectroradiometer (MODIS) daily land-surface temperature (LST), MODIS daily snow albedo, and a special diurnal melt product derived from QuikSCAT (QS) scatterometer data, are all effective in measuring the evolution of melt on the ice sheet. These daily products, produced from different parts of the electromagnetic spectrum, are sensitive to different geophysical features, though QS- and MODIS-derived melt generally show excellent correspondence when surface melt is present on the ice sheet. Values derived from the daily MODIS snow albedo product drop in response to melt, and change with apparent grain-size changes. For the 2007 melt season, the QS and MODIS LST products detect 862,769 square kilometers and 766,184 square kilometers of melt, respectively. The QS product detects about 11% greater melt extent than is detected by the MODIS LST product probably because QS is more sensitive to surface melt, and can detect subsurface melt. The consistency of the response of the different products demonstrates unequivocally that physically-meaningful melt/freeze boundaries can be detected. We have demonstrated that these products, used together, can improve the precision in mapping surface and near-surface melt extent on the Greenland Ice Sheet.

Hall, Dorothy K.↗
NASA NTRS2022

2019 ARIA Proposal Final Report Public Abstract: What Happens to Life in an Ocean World Plume?

The NASA Cassini mission to Saturn discovered persistent jets of water being ejected into space from a subsurface ocean on the small moon Enceladus and evidence that this ocean is habitable for life. The ice resulting from these plumes forms the E Ring of Saturn. Missions are under study to fly through these plumes and collect and analyze samples to look for evidence of life. To support design and instrument selection for these missions, there is a critical need to understand what happens to microbial life adapted to a liquid environment when suddenly ejected into the vacuum of space. We experimentally determined the structural changes in microbial cells in liquid medium when ejected into vacuum. We first developed a vacuum chamber fitted with a feedthrough and nozzle allowing fluid to be injected into vacuum. The nozzle dispersed the fluid into micron-sized droplets. Fluid containing a population of E. Coli microbes was injected into the chamber and droplets were collected on microscope slides and small cups for imaging with a Scanning Electron Microscope (SEM). Epifluorescence microscopy, using a lipid stain, and SEM imaging were used to evaluate the cellular integrity of E. coli after injection through the pressurized nozzle into vacuum. The experimental samples showed a 94% decrease in visible intact E. coli cells as compared with control samples but showed a fluorescent residue in the shape of the sublimated droplets that indicated the presence of lipids. E. coli was selected for testing although other cell types could vary physiologically which could affect their response to a vacuum environment. More testing is needed to determine whether other types of microbial cells can survive the plume environment. However, these results suggest that lipids may be directly detectable evidence of life in icy world plumes, but organisms are unlikely to survive due to disruption of their cell walls.

C. R. Stoker↗
DOE OSTI2024

Underground hydrogen storage leakage detection and characterization based on machine learning of sparse seismic data

Underground hydrogen storage (UHS) is considered as a scalable approach for massive storage and seasonal extraction of hydrogen (H 2 ). Although conventional leakage detection and characterization methods based on time-lapse seismic imaging and inversion generally apply to H 2 leakage detection problem, a high-fidelity yet cost effective geophysics approach is still missing to reliably inform leakage location and properties based on very sparse data. In response, we develop a novel supervised machine learning method to detect and characterize H 2 leakage from UHS. The input to our neural network are sparse time-lapse seismic waveforms, while the output from the neural network includes the spatial location and physical properties of a H 2 leakage. Here, we generate high-quality time-lapse waveforms using the elastic-wave equations to train the neural network. We train and validate our machine learning model and find that it attains high accuracy in using extremely sparse time-lapse seismic data to detect and characterize H 2 leakage. Our investigation is the first systematic study that focuses on applying machine learning to subsurface H 2 leakage detection and characterization and could potentially serve as a cost-effective geophysical tool for underground hydrogen leakage detection and characterization with high fidelity.

08 HYDROGEN↗
NASA NTRS2000

Radar Reflectivity of the Martian Polar Regions

Radar experiments provide a unique method of probing the surfaces and subsurfaces of planetary bodies. Information on surface and subsurface structures and properties can be extracted from radar data. There is a well developed history of radar investigations of the planet Mars, beginning with the first reports of variations of scattering properties as a function of martian longitude in the mid-1960's. Because of the rapid rotation of Mars, the standard technique of delay-doppler mapping cannot be used as effectively as it is on Mercury and Venus, making it a more difficult radar target. Techniques have been developed to overcome this difficulty, including an inversion technique to combine many doppler-only (CW) experiments from different viewing geometries into a map of surface reflectivity, and new random long-code techniques. These techniques still suffer from ambiguities, however. A third technique which does not suffer from these ambiguities is the combination of the powerful Goldstone transmitter with the VLA as the receiving instrument to create a combined radar imaging instrument. We have used this combined radar instrument to image the surface of Mars in 3.5-cm radar reflectivity during the 1988, 1992/93, and 1999 oppositions. During the 1988 experiments, the residual south polar ice cap (RSPIC) was the brightest radar reflector on the planet - intrinsically brighter than even the Tharsis lava flows. This was quite an unexpected result. In contrast, during the 1992/93 experiments, the residual north polar ice cap (RNPIC) was not nearly so bright, and in fact showed no enhancement at all, This was puzzling, given the 1988 results for the RSPIC. We attributed the lack of a radar reflection enhancement to a combination of three effects: the geometry was different; the season was different; and the intrinsic scattering from the residual ice caps was different. The 1999 experiments provided a chance to test the relative importance of these three effects, since the RNPIC would be visible in a geometry very similar to the RSPIC in 1988, and the season would also be very similar. Preliminary reduction of the data taken in 1999 shows that the RNPIC did in fact show a radar reflectivity enhancement, but that it was still not as bright as the RSPIC in 1988. Details regarding the radar reflectivity of the residual ice caps, as well as the surrounding polar layered terrains (in light of our radar reflectivity data) will be discussed. Additional information is contained in the original extended abstract.

Butler, B. J.↗
DOE OSTI2022

Advanced Terrestrial Simulator (ATS) evaluation dataset at 7 catchments across the continental United States

This dataset comprises of the input files and other files required for Advanced Terrestrial Simulator (ATS) simulations at 7 catchments across the continental United States. ATS is an integrated surface-subsurface hydrology model. We include Jupyter notebooks (within scripts folder) for individual catchments showing information (including data sources, river network, soil, geology, landuse types etc.) on preparing the machine readable input files. ATS observation output files are provided in the output folder. Figures and analyses (.xlsx sheets) are also provided. The catchments include, Taylor River Upstream (Colorado); (b) Cossatot River (Arkansas); (c) Panther Creek (Alabama); (d) Little Tennessee River (North Carolina and Georgia); (e) Mayo River (Virginia); (f) Flat Brook (New Jersey); (g) Neversink River headwaters (New York). Readme files are provided inside the directories providing more details. Files types include: .xml, .h5, .xlsx, .png, .ipynb, .py, .nc, .txt. All of the files types can be accessed by open source software, details on software requirements are following: .xml (any text editors including notepad and textedit), .h5 (in python using hdf libraries), .xlsx (WPS Office Spreadsheets, OpenOffice Calc, LibreOffice Calc, Microsoft Office etc.), .png (any image viewer), .ipynb (Jupyter notebook), .py (any text editors including notepad and textedit), .nc (using python or other open source software).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Model Data Archive Associated with Manuscript "Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon"

This data package supports the publication “Fire-altered Carbon Pools Create Disturbance Memory in Stream Dissolved Organic Carbon” by Li et al. (2026). The package contains processed model inputs, configuration files, restart files, simulation outputs, scripts, and visualization products used to evaluate post-fire dissolved organic carbon (DOC) dynamics in the Naches River Watershed, Washington, USA, following the 2021 Schneider Springs Fire. The modeling workflow couples ELM-BGC, the biogeochemistry-enabled Energy Exascale Earth System Model Land Model; ATS, the Advanced Terrestrial Simulator for integrated surface-subsurface hydrology; and PFLOTRAN, a reactive transport model for multicomponent aqueous geochemistry. Together, these models simulate how wildfire-induced changes in vegetation, litter, coarse woody debris, and soil organic matter influence DOC production, transport, and reaction from burned hillslopes to stream networks. The archive includes preprocessed meteorological, geospatial, hydrologic, and biogeochemical forcing data; ELM-BGC-derived DOC source terms; ATS mesh files; PFLOTRAN reactive-transport inputs; model configuration files; spin-up and transient restart files; watershed-scale diagnostic outputs; stream concentration time series; and figures or visualization files used to inspect and reproduce key results. File types include Hierarchical Data Format 5 (HDF5) files for gridded forcing and model-coupling data, model input and configuration files for ELM-BGC, ATS, and PFLOTRAN, restart and simulation-output files generated by the modeling workflow, tabular or time-series diagnostic outputs, scripts for post-processing and figure generation, and image or visualization products associated with the manuscript. Use of the package depends on the intended task. Re-running the simulations requires the relevant modeling software, including ELM-BGC, ATS, and PFLOTRAN as ATS's geochemical engine. Inspecting outputs and reproducing figures requires Python with scientific plotting libraries such as Matplotlib, and three-dimensional model outputs may be viewed with ParaView. Geographic information system files or maps may be inspected with ArcGIS Pro or comparable GIS software. The data package is intended to enable traceability, reuse, and partial reproduction of the coupled land-to-watershed hydro-biogeochemical modeling workflow used to test how wildfire disturbance affects terrestrial carbon pools and downstream DOC dynamics.

ATS↗
DOE OSTI2025

FracML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage

Poster on “FRACML: A Machine Learning Based Tool to Quantify Reservoir Scale Fracture Network for CO2 Storage” for the CCUS 2025 conference held in Houston, Texas March 3-5, 2025. The accurate characterization of subsurface fracture networks is essential for the secure operation of carbon capture, utilization, and storage (CCUS) projects. A thorough understanding of the spatial distribution of subsurface faults and fractures is crucial for predicting CO2 plume evolution and minimizing risks such as potential leakage into overlying formations or induced seismicity. In this context, robust fracture network quantification plays a pivotal role in reservoir management, providing the data necessary to fine-tune operational parameters, and ensure the environmental and economic viability of CCUS projects. As part of the U.S. Department of Energy’s SMART (Science-informed Machine Learning for Accelerating Real-time Decisions in Subsurface Applications) initiative, we focused on the development and application of a machine learning-based tool (FRACML) designed to quantify and map fracture networks using real-world (non-synthetic) data from an active CO2 injection site. Our objective is to demonstrate the utility of this tool in improving operational efficiency and safety across CCUS sites.

artifical intelligence / machine learning (AI/ML)↗