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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

STOCK: Structure mapper and online coarse-graining kit for molecular simulations

We present a web toolkit STructure mapper and Online Coarse-graining Kit for setting up coarse-grained molecular simulations. The kit consists of two tools: structure mapping and Boltzmann inversion tools. The aim of the first tool is to define a molecular mapping from high, e.g. all-atom, to low, i.e. coarse-grained, resolution. Using a graphical user interface it generates input files, which are compatible with standard coarse-graining packages, e.g. VOTCA and DL_CGMAP. Our second tool generates effective potentials for coarse-grained simulations preserving the structural properties, e.g. radial distribution functions, of the underlying higher resolution model. The required distribution functions can be provided by any simulation package. Simulations are performed on a local machine and only the distributions are uploaded to the server. The applicability of the toolkit is validated by mapping atomistic pentane and polyalanine molecules to a coarse-grained representation. Effective potentials are derived for systems of TIP3P (transferable intermolecular potential 3 point) water molecules and salt solution. The presented coarse-graining web toolkit is available at http://stock.cmm.ki.si.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Vegetation classification map and covariates associated with NEON AOP survey, East River, CO 2018

This package includes geospatial data layers developed to investigate how environmental gradients—specifically topography and near-surface soil properties—drive the spatial arrangement of dominant plant communities in mountainous watersheds. The geospatial products, which support the analysis of these ecological relationships, are derived from airborne hyperspectral and LiDAR datasets acquired by the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP), in conjunction with an extensive ground field campaign conducted in summer 2018. This work is part of the DOE Watershed Function Science Focus Area (SFA) and features geospatial datasets developed based on observations and ground data collected at East River, Colorado, in collaboration with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey in June 2018. Classification Map: - Classification Map (PNG, GeoTIFF): Derived from hyperspectral and LiDAR airborne data using a machine learning approach. - Class Code Mapper (CSV): Associates pixel values with corresponding vegetation/non-vegetation classes. - Classification Reference Data (CSV): Reference data used in the machine learning procedure. LiDAR-Derived Products: - Topographical Metrics (GeoTIFFs): Elevation, slope, curvature, TWI, TPI, solar insolation, and canopy height model (CHM), smoothed with a 5x5 pixel window. Vegetation Indices: - GeoTIFFs of NDVI, NDNI, NDWI: Vegetation indices derived from hyperspectral data. Urban Masks: - Urban Mask (GeoTIFF): Applied to the mapping to convert bare soil classes to urban classes. Software Compatibility: GeoTIFFs: Can be visualized with GIS software or libraries that support GeoTIFF images. CSV Files: Can be opened with any software that handles comma-separated values. The FLMD file provides details and links to the source datasets used to derive the products. The manuscript (in the Method session) provides details on how each product was derived. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Update on 2026-03-25: Since the original dataset publication date of 02/28/2020, this package has a new classification map derived by an improved methodology. This update also includes additional ground data that improved the representation of some of the communities. See the methods for further details on what has changed between versions.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2014

WE-D-18A-05: Construction of Realistic Liver Phantoms From Patient Images and a Commercial 3D Printer

Purpose: To assess image quality and radiation dose reduction in abdominal CT imaging, physical phantoms having realistic background textures and lesions are highly desirable. The purpose of this work was to construct a liver phantom with realistic background and lesions using patient CT images and a 3D printer. Methods: Patient CT images containing liver lesions were segmented into liver tissue, contrast-enhanced vessels, and liver lesions using commercial software (Mimics, Materialise, Belgium). Stereolithography (STL) files of each segmented object were created and imported to a 3D printer (Object350 Connex, Stratasys, MN). After test scans were performed to map the eight available printing materials into CT numbers, printing materials were assigned to each object and a physical liver phantom printed. The printed phantom was scanned on a clinical CT scanner and resulting images were compared with the original patient CT images. Results: The eight available materials used to print the liver phantom had CT number ranging from 62 to 117 HU. In scans of the liver phantom, the liver lesions and veins represented in the STL files were all visible. Although the absolute value of the CT number in the background liver material (approx. 85 HU) was higher than in patients (approx. 40 HU), the difference in CT numbers between lesions and background were representative of the low contrast values needed for optimization tasks. Future work will investigate materials with contrast sufficient to emulate contrast-enhanced arteries. Conclusion: Realistic liver phantoms can be constructed from patient CT images using a commercial 3D printer. This technique may provide phantoms able to determine the effect of radiation dose reduction and noise reduction techniques on the ability to detect subtle liver lesions in the context of realistic background textures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Geospatial Information, Metadata, and Maps for Global River Corridor Science Focus Area Sites (v5)

This dataset provides geospatial information, metadata, and maps for the Pacific Northwest National Laboratory (PNNL) River Corridor Science Focus Area (RC-SFA; https://www.pnnl.gov/projects/river-corridor) sites. The RC-SFA works to transform understanding of spatial and temporal dynamics in river corridor hydrobiogeochemical functions from molecular reaction to watershed and basin scales. The knowledge we gain is used to formulate and test hypotheses and to improve mechanistic representation of river corridor processes and their response to disturbances in multiscale models of integrated hydrobiogeochemical function. The data provided includes Site ID, latitude, longitude, stream name, and common ID (COMID) for sites used across the RC-SFA. The COMID can be used to find and download data from NHDPlus (https://www.epa.gov/waterdata/nhdplus-national-hydrography-dataset-plus) and other platforms. The sites included are non-exhaustive. Sites (including past sites) will be added to this data package in the future. Data generated from the RC SFA can be accessed at https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA. This data package was originally published in April 2023. It was updated in June 2023 (v2; modified files), December 2023 (v3; modified files), January 2025 (v4; modified files), and December 2025 (v5; modified files). See the change history section in the readme for more details. This dataset is comprised of one main data folder. The data folder consists of (1) file-level metadata; (2) data dictionary; (3) readme; (4) methods codes; (5) geospatial information for all RC SFA sites including International Generic Sample Number (IGSN); (6) maps of all sites and sites in Washington State, USA; and (7) a subfolder with the shapefile of all sites. All files are .csv, .pdf, .shp, .cpg, .dbf, .prj, .qmd, or .shx. We thank the Confederated Tribes and Bands of the Yakama Nation for access to field locations where some data were collected in Washington state. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data

Geophysical surveys were conducted at the NGEE Arctic Teller mile marker 27 site, Teller mile marker 47 site, and Kougarok mile marker 64 site during the summers of 2018, 2019, and 2021. Additional data was collected at Teller mile marker 47 during September 2021 and August 2022. These surveys were used to identify locations of near-surface permafrost during the period of maximum seasonal thaw depth for ground truth data used in machine learning predictions of near-surface permafrost extent at each site. This dataset contains CSV files of ground truth observations of near-permafrost presence or absence for each site, where PF = 1 indicates permafrost presence and PF = 0 indicates permafrost absence. The dataset also includes 2 sets of binary rasters (WGS84 UTM zone 3) of permafrost extent for each site using 1) all of the training data and 2) the transferred model. For both sets of rasters, 0 = non-permafrost and 1 = permafrost. Included are 6 *.tif files and 5 *.csv files that include a data dictionary (dd.csv) and file-level metadata (flmd.csv). This dataset is in support of the paper "Machine learning-derived high-resolution maps of near-surface permafrost for three watersheds on the Seward Peninsula, Alaska" that is in review (May 2023). The Next-Generation Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

FLARE: field line analysis and reconstruction for 3D boundary plasma modeling

The FLARE code is a magnetic mesh generator that is integrated within a suite of tools for the analysis of the magnetic geometry in toroidal fusion devices. A magnetic mesh is constructed from field line segments and permits fast reconstruction of field lines in 3D boundary plasma codes such as EMC3-EIRENE. Both intrinsically non-axisymmetric configurations (stellarators) and those with symmetry breaking perturbations of an axisymmetric equilibrium (tokamaks) are supported. The code itself is written in Modern Fortran with MPI support for parallel computing, and it incorporates object-oriented programming for the definition of the magnetic field and the material surface geometry. Extended derived types for a number of different magnetohydrodynamic equilibrium and plasma response models are implemented. The core element of FLARE is a field line tracer with adaptive step-size control, and this is integrated into tools for the construction of Poincaré maps and invariant manifolds of X-points. A collection of high-level procedures that generate output files for visualization is build on top of that. The analysis modules are build with Python frontends that facilitate customization of tasks and/or scripting of parameter scans.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Mosaicked Historic Airborne Imagery from Seward Peninsula, Alaska, Starting in the 1950's

Historical airborne imagery for each Seward Peninsula NGEE Arctic site - Teller, Kougarok, Council - were obtained with multiple years for each site in order to identify landscape changes over time when compared to more recent imagery. This dataset includes mosaicked, geolocated and, where possible, orthorectified, historic airborne and recent satellite imagery. The older photos were sourced from the USGS Earth Explorer site and the newer, satellite imagery is from the Statewide Digital Mapping Initiative (SDMI) project managed by the Geographic Information Network of Alaska on behalf of the state of Alaska. Check with Jessica Cherry (jessica.cherry@alaska.edu), University of Alaska, and Lisa Wirth (lisa@gina.alaska.edu), Geographic Information Network of Alaska (GINA) and the Statewide Digital Mapping Initiative (SDMI), for citation information and redistribution restrictions of the imagery. Dataset includes the following types of files: *.tif, *.txt, *.pdf, *.jpg, *.xml. Please refer to metadata files included inside the data folders for detailed descriptions of the data products. The use of historical aerial photographs in combination with more recent satellite imagery has proven effective at detecting and quantifying landscape changes. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

BOREAS RSS-15 SIR-C and Landsat TM Biomass and Landcover Maps of the NSA

As part of BOREAS, the RSS-15 team conducted an investigation using SIR-C, X-SAR, and Landsat TM data for estimating total above-ground dry biomass for the SSA and NSA modeling grids and component biomass for the SSA. Relationships of backscatter to total biomass and total biomass to foliage, branch, and bole biomass were used to estimate biomass density across the landscape. The procedure involved image classification with SAR and Landsat TM data and development of simple mapping techniques using combinations of SAR channels. For the SSA, the SIR-C data used were acquired on 06-Oct-1994, and the Landsat TM data used were acquired on 02-Sep-1995. The maps of the NSA were developed from SIR-C data acquired on 13-Apr-1994. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Hall, Forrest G.↗
DOE OSTI2015

SU-C-201-07: Validation of a GATE Gamma Camera Model for the Siemens Symbia

Purpose: To develop a simulation model of a clinical gamma camera/SPECT system and to validate the model using experimental and published measurements from the clinical system. Methods: Geant4 Application for Tomographic Emission (GATE) was used to create a model of the Siemens Symbia gamma camera. A modular model was implemented that allows specifying combinations of crystal thickness (3/8”, 5/8”) and collimator (LEHR, MELP, HE). Shielding, energy resolution, intrinsic resolution, crystal thickness, and collimator properties were set based on manufacturer specifications. Validation of the model was performed by simulating NEMA 2007 gamma camera tests including spatial resolution and sensitivity for Tc99; these were compared with experimental and published data for the scanner. The simulated energy spectra of a Tc99 line source in acrylic blocks was visually compared with the corresponding experimental acquisition. For a 4 cm diameter sphere filled with Tc99, the attenuation maps were generated from simulation data, and the photopeak and scatter window were extracted from GATE output using ROOT to create DICOM files to use in the clinical reconstruction. Results: Simulated spatial resolutions for LEHR 3/8” crystal at 0, 10 cm, 10 cm (with scatter), and 30 cm were 4, 6.7, 7.9, and 14.5 mm FWHM; these were 9% less than published data. For 5/8” crystal the spatial resolutions were 4.5, 7.0, 8.5, and 14.7 mm FWHM; these were 4% to 10% less than published data. Simulated sensitivity was within 3.5% of published data for both LEHR 3/8” and 5/8”. The simulated energy spectra matched the photopeak and scatter window well, but did overestimate the counts below 90 keV. The simulated attenuation map and projection data were successfully reconstructed with the clinical software, and the passed visual inspection. Conclusions: Validation of a specific clinical scanner allows future studies of quantification accuracy for both planar and SPECT imaging. Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under Award Number R01CA138986. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Interoperability format translation and transformation between IFC architectural design file and simulation file formats

Automatically translating a building architecture file format (Industry Foundation Class) to a simulation file, in one aspect, may extract data and metadata used by a target simulation tool from a building architecture file. Interoperability data objects may be created and the extracted data is stored in the interoperability data objects. A model translation procedure may be prepared to identify a mapping from a Model View Definition to a translation and transformation function. The extracted data may be transformed using the data stored in the interoperability data objects, an input Model View Definition template, and the translation and transformation function to convert the extracted data to correct geometric values needed for a target simulation file format used by the target simulation tool. The simulation file in the target simulation file format may be generated.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2015

Interoperability format translation and transformation between IFC architectural design file and simulation file formats

Automatically translating a building architecture file format (Industry Foundation Class) to a simulation file, in one aspect, may extract data and metadata used by a target simulation tool from a building architecture file. Interoperability data objects may be created and the extracted data is stored in the interoperability data objects. A model translation procedure may be prepared to identify a mapping from a Model View Definition to a translation and transformation function. The extracted data may be transformed using the data stored in the interoperability data objects, an input Model View Definition template, and the translation and transformation function to convert the extracted data to correct geometric values needed for a target simulation file format used by the target simulation tool. The simulation file in the target simulation file format may be generated.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2005

Thermal Analysis Methods for Aerobraking Heating

As NASA begins exploration of other planets, a method of non-propulsively slowing vehicles at the planet, aerobraking, may become a valuable technique for managing vehicle design mass and propellant. An example of this is Mars Reconnaissance Orbiter (MRO), which will launch in late 2005 and reach Mars in March of 2006. In order to save propellant, MRO will use aerobraking to modify the initial orbit at Mars. The spacecraft will dip into the atmosphere briefly on each orbit, and during the drag pass, the atmospheric drag on the spacecraft will slow it, thus lowering the orbit apoapsis. The largest area on the spacecraft, and that most affected by the heat generated during the aerobraking process, is the solar arrays. A thermal analysis of the solar arrays was conducted at NASA Langley, to simulate their performance throughout the entire roughly 6-month period of aerobraking. Several interesting methods were used to make this analysis more rapid and robust. Two separate models were built for this analysis, one in Thermal Desktop for radiation and orbital heating analysis, and one in MSC.Patran for thermal analysis. The results from the radiation model were mapped in an automated fashion to the Patran thermal model that was used to analyze the thermal behavior during the drag pass. A high degree of automation in file manipulation as well as other methods for reducing run time were employed, since toward the end of the aerobraking period the orbit period is short, and in order to support flight operations the runs must be computed rapidly. All heating within the Patran Thermal model was combined in one section of logic, such that data mapped from the radiation model and aeroheating model, as well as skin temperature effects on the aeroheating and surface radiation, could be incorporated easily. This approach calculates the aeroheating at any given node, based on its position and temperature as well as the density and velocity at that trajectory point. Run times on several different processors, computer hard drives, and operating systems (Windows versus Linux) were evaluated.

Amundsen, Ruth M.↗
DOE OSTI2024

XRF-XFS-XAS-Auto v1.0 - Beta release

This software allows to analyze XRF maps, XFS spectra and XAS spectra collected at the Advanced Light Source's Beamline 10.3.2. Features include: 1) XRF maps: - process XRF maps, all elemental maps are saved as bmp automatically and labeled with the incident energy used, the scale bar is also labeled and can be controlled. - XRF elemental correlation plots, save the correlation plots automatically - Extract single or multiple transects in XRF maps on one or several regions of interest, each transect profile is numbered and saved in a corresponding folder, along with the corresponding maps showing transect location. 2) XFS spectra - save in log10 scale the XFS spectra, either a single or multiple files all at once. The files are saved as .bmp. - XFS spectra are labeled according to tabulated fluorescence emission lines. 3) XAS spectra - allows to plot individual scalers in the raw data. - allows calibration of the spectra using an Io internal glitch present in all spectra and performing 1st derivative. - Least-square linear combination fitting of XANES or extended XANES spectra using a database of standards using 1, 2 or 3 components maximum. It also provides the 5 top combinations and provide the user for the possibility of saving the 2nd, 3rd, 4th and 5th best combinations in addition to the best one. The processed spectra (pre-edge background substracted, post-edge normalized), the fits and residuals are automatically saved. A table of the component, with fit% and SSN is provided and saved automatically as well.

Fakra, Sirine↗
DOE OSTI2021

Model-Data for Joint Estimation of Biogeochemical Model Parameters from Multiple Experiments: A Bayesian Approach Applied to Mercury Methylation

This modeling archive supports the manuscript submitted for publication in the Environmental Modeling and Software. This study is supported by ORNL-SFA and IDEAS-Watershed. This study aims to improve calibration of complex biogeochemical models using datasets from multiple experiments targeting specific subprocesses. The proposed Bayesian joint-fitting scheme calibrates the entire biogeochemical model in one go using all the available datasets and estimate parameter uncertainties using Markov Chain Monte Carlo (MCMC). This allows for complete propagation of uncertainties and utilization of the information shared between different datasets. Mapping joint distribution of parameters guides model improvement by identifying null spaces in the parameter space. This archive contains files used to perform MCMC, post-process outputs and visualize results.

East Fork Poplar Creek↗
DOE OSTI2019

Appalachian Basin Temperature-Depth Maps and Structured Data in support of Feasibility Study of Direct District Heating for the Cornell Campus Utilizing Deep Geothermal Energy

This dataset contains shapefiles and rasters that summarize the results of a stochastic analysis of temperatures at depth in the Appalachian Basin states of New York, Pennsylvania, and West Virginia. This analysis provides an update to the temperature-at-depth maps provided in the Geothermal Play Fairway Analysis of the Appalachian Basin (GPFA-AB) Thermal Quality Analysis (GDR repository 879: https://gdr.openei.org/submissions/879). This dataset improves upon the GPFA-AB dataset by considering several additional uncertainties in the temperature-at-depth calculations, including geologic properties and thermal properties. A Monte Carlo analysis of these uncertain properties and the GPFA-AB estimated surface heat flow was used to predict temperatures at depth using a 1-D heat conduction model. In this data submission, temperatures are provided for depths from 1-5 km in 0.5 km increments. The mean, standard deviation, and selected quantiles of temperatures at these depths are provided as shapefiles with attribute tables that contain the data. Rasters are provided for the mean and standard deviation data. Figures and maps that summarize the data are also provided. For the pixel corresponding to Cornell University, Ithaca, NY, a .csv file containing the 10,000 temperature-depth profiles estimated from the Monte Carlo analysis is provided. These data are summarized in a figure containing violin plots that illustrate the probability of obtaining certain temperatures at depths below Cornell.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

HERO WEC V1.0 2024 - WEC-Sim Detailed Simulation Runs and Summary Data

This dataset includes results from simulations of NREL's hydraulic and electric reverse osmosis wave energy converter (HEREO WEC). Simulation runs include 135 wave cases that were based on the updated WEC-Sim model, which is linked below. The data represented in this repository is based on an updated WEC-Sim model using laboratory data to tune and refine the original WEC-Sim model for the V1.0 HERO WEC. The 135 wave cases represent waves with the following wave height and wave period ranges: - Significant Wave Height: 0.25 - 3.75m in 0.25m increments - Wave Period: 5 - 13 sec in 1 sec increments Each run was simulated using a Pierson-Moskowitz irregular wave spectrum with a 100 second ramp time, a total simulation time of 3,100 seconds, and a simulation time-step of 0.005s. A reference table has been included to map each multi condition run (MCR) case with each wave condition. Summary data set includes a spreadsheet and image files with matrices that are associated with data from simulation runs. All matrices cover the same significant wave height and wave periods from the simulation runs, in the same increments. The following matrices are included: - Power Abs: The average absorbed power from the WEC (calculated from anchor reaction force and heave velocity) - Power Hyd: The average hydraulic power output at pump (calculated from pump output flow and pressure) - Power - Hyd ROi: The average hydraulic power measured at the RO system inlet (calculated from RO system pressure and flow (pre-accumulator)) - Flow - Pump out: The average flowrate measured at the pump outlet - Flow - Perm: The average permeate (clean water) production - Flow - RO (pre): The average flowrate measured at the inlet of the RO system before the accumulators - Flow - RO (post): The average flowrate measured after the accumulator bank in the RO system - Pressure - RO: The average pressure measured at the inlet of the RO system This data set has been developed by the National Renewable Energy Laboratory, operated by the Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗