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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 OSTI2023

Model Data for the Mesh Convergence Study Demonstrating Benefits of Mixed-polyhedral Mesh in Integrated Hydrology Simulations

This archived model data is related to a study introducing a unique method that employs a stream-aligned mixed-polyhedral mesh to effectively and accurately represent river valleys, stream corridors, and narrow engineered channels in integrated hydrology simulations. The study finds that utilizing stream-aligned mixed-polyhedral meshes in integrated hydrology simulations achieves accuracy on par with a finely refined TIN-based mesh while markedly diminishing computational costs. This archive contains scripts and data files needed to generate the ATS model input, including mesh and ATS input files, for all mesh scenarios using the Watershed Workflow package. Additionally, this archive also provides key outputs from the model simulations that are used in the analysis and post-processing scripts to reproduce figures in the manuscript. The Watershed Workflow package is implemented in Python3. The Jupyter notebooks can be executed through multiple open-source tools, for example, Anaconda Jupyter Lab, VS Studio Code, etc. Other data files include CSV and HDF5 files, which can be read through Python scripts. The input files for the ATS model, open-source integrated hydrology, and transport model are in XML format and can be edited in any commonly used text editors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Data used in Wainwright, H.M. et al. 2021, “Watershed zonation through hillslope clustering for tractably quantifying above- and belowground watershed heterogeneity and functions”

This data package contains spatial data layers and processing scripts used in Wainwright, H.M. et al. 2021, “Watershed zonation approach for tractably quantifying above-and- belowground watershed heterogeneity and functions”. The purpose of the data and paper is to develop a watershed zonation approach for characterizing watershed organization and function in a tractable manner by applying clustering methods to multiple spatial data layers. The data package contains the geotiff files of spatial data layers, and the processed data values corresponding to the figures in the paper. The Data_description file describe each file in details. The spatial data sets (geotiff) are included in the zip files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data and Code for: Observation-constrained agroecosystem model inversion reveals continental-scale variation of winter wheat traits

This repository contains the simulation outputs and processing scripts associated with the study of winter wheat traits across the United States, utilizing the Ecosys agroecosystem model. The dataset includes model results for both rainfed and irrigated winter wheat systems, supporting the findings presented in the manuscript titled "Observation-constrained agroecosystem model inversion reveals continental-scale variation of winter wheat traits." Data includes the original Ecosys simulation outputs (archived in .db format within the compressed .zip files) and extracted analysis data (stored in .pkl files for efficient processing). Python code for data processing and figure generation is provided in a Jupyter notebook. External Observational Datasets should refer to the following official repositories for the input and validation data used in this study. The eddy covariance data from the AmeriFlux network (https://ameriflux.lbl.gov/). Climate-forcing data of NLDAS-2 from NASA LDAS (https://ldas.gsfc.nasa.gov/nldas/nldas-2-forcing-data). Soil data from the Gridded Soil Survey Geographic Database (gSSURGO), available at (https://www.nrcs.usda.gov/resources/data-and-reports/gridded-soil-survey-geographic-gssurgo-database). Crop yields, planting and harvest dates from the USDA public databases (https://quickstats.nass.usda.gov/; https://webapp.rma.usda.gov/apps/actuarialinformationbrowser/CropCriteria.aspx). Satellite-derived SLOPE GPP data from ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1786). Land use and crop progress information from the USDA Crop Data Layer and Crop Progress and Condition Gridded Layers (https://www.nass.usda.gov/Research_and_Science/). The Ecosys model code is available online at https://github.com/jinyun1tang/ECOSYS.

Wheat↗
DOE OSTI2020

Imagery Applications for Advanced Event Analytics (WBS 24.3.1.3.3-IDC FY202 Final Project Report)

Accurate event locations and replicability of location analyses are essential for assessing the nature of an event, its context, ambient site conditions, and proximity to relevant facilities and infrastructure. Additionally, accurate event locations provide valuable information that reduce uncertainties, improve confidence in event analyses, and inform in-field verification activities. However, event location/relocation and replicability are difficult due to a number of factors, including spatially-sparse network coverage in some areas of the globe and variability in seismic data processing. This team proposed that the incorporation of high-fidelity imagery as a data backbone to the analytical assessment of a suspected underground explosion and/or an advanced seismic event bulletin produced by the International Data Centre (IDC) of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO PrepCom) could reduce uncertainties and improve confidence in analyses. Specifically, temporally-separated images can reduce uncertainty by identifying areas where change has occurred (e.g., building construction or demolition, road or facilities improvements). The primary goal of this project was to develop an automated geospatial processing script for imagery change detection to better reflect needs of the technical community (including the IDC) and to make the use of such a tool accessible in a variety of settings across platforms. Technical experts at Los Alamos National Laboratory successfully built GAIA: the Geospatial Automated Imagery Analysis tool, to fill this need. GAIA combines five tool components to produce orthorectified time separated imagery and imagery change detection maps. Our toolkit (1) reduces error by providing a standardized workflow for image analyses and (2) significantly reduces processing time from between 7 and 24+ hours to approximately 5 minutes. Technical experts at Sandia National Laboratories supported GAIA via beta-testing and by introducing a web-based system approach for increased applicability. To test the function, performance, broad application, and ease-of-use of GAIA, we applied it to four separate test cases. The results of this preliminary investigation show promise in reducing uncertainty in seismic event locations: if satellite imagery can show regions where operations that produce seismic activity likely occurred, then pursuing imagery to locate epicenters of seismic nuclear events could reduce the time needed to find the true epicenter location.

58 GEOSCIENCES↗
DOE OSTI2020

Imagery Applications for Advanced Event Analytics (WBS 24.3.1.3.3-IDC FY2020 Final Project Report)

Accurate event locations and replicability of location analyses are essential for assessing the nature of an event, its context, ambient site conditions, and proximity to relevant facilities and infrastructure. Additionally, accurate event locations provide valuable information that reduce uncertainties, improve confidence in event analyses, and inform in-field verification activities. However, event location/relocation and replicability are difficult due to a number of factors, including spatially-sparse network coverage in some areas of the globe and variability in seismic data processing. This team proposed that the incorporation of high-fidelity imagery as a data backbone to the analytical assessment of a suspected underground explosion and/or an advanced seismic event bulletin produced by the International Data Centre (IDC) of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO PrepCom) could reduce uncertainties and improve confidence in analyses. Specifically, temporally-separated images can reduce uncertainty by identifying areas where change has occurred (e.g., building construction or demolition, road or facilities improvements). The primary goal of this project was to develop an automated geospatial processing script for imagery change detection to better reflect needs of the technical community (including the IDC) and to make the use of such a tool accessible in a variety of settings across platforms. Technical experts at Los Alamos National Laboratory successfully built GAIA: the Geospatial Automated Imagery Analysis tool, to fill this need. GAIA combines five tool components to produce orthorectified time-separated imagery and imagery change detection maps. Our toolkit (1) reduces error by providing a standardized workflow for image analyses and (2) significantly reduces processing time from between 7 and 24+ hours to approximately 5 minutes. Technical experts at Sandia National Laboratories supported GAIA via beta-testing and by introducing a web-based system approach for increased applicability. To test the function, performance, broad application, and ease-of-use of GAIA, we applied it to four separate test cases. The results of this preliminary investigation show promise in reducing uncertainty in seismic event locations: if satellite imagery can show regions where operations that produce seismic activity likely occurred, then pursuing imagery to locate epicenters of seismic nuclear events could reduce the time needed to find the true epicenter location.

58 GEOSCIENCES↗
DOE OSTI2025

How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive

This dataset contains model output and input data, as well as source code examples for the Terrestrial Ecosystem Model with the Dynamic Vegetation Model and Dynamic Organic Soil (DVM-DOS-TEM) for the field sites Imnavait creek and the Bonanza creek Long Term Ecological Research Network (LTER). The data covers simulations from the last glacial maximum (LGM) until 2100 for a selection of paleo scenarios, setting the mean temperature of the LGM up to 10°C lower than pre-industrial conditions. The model structure was modulated to represent various model versions, and this dataset contains the relevant changes in the source code. The raw output data, the processed statistical data, the setup and processing scripts as well as parameter value distribution files from a parameter sensitivity analysis are included as well. Model outputs include active layer depth, organic soil carbon, soil layer depths, gross primary productivity (GPP) with and without nitrogen limitation, net primary productivity (NPP), soil liquid water content, heterotrophic, maintenance, and growth respiration, soil temperature, and vegetation carbon (*.nc files). The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research.Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Spatial Study 2022: Surface Water Samples, Cotton Strip Degradation, and Hydrologic Sensor Data across the Yakima River Basin, Washington, USA (v3)

This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. The dataset provides data and photos generated from sample collection during the same one-week period at 48 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. The contents include surface water geochemistry data; river substrate grain size photos; stream depth data; manual chamber open channel respiration data; and field metadata (including qualitative information on instream and river corridor characteristics). Grain size photos can be used to improve estimates of channel substrate D50 data. The dataset also includes tensile strength and photos from cotton strip field degradation experiments; five-week sensor time series temperature, dissolved oxygen, pressure, pH, specific conductance, chlorophyll A, and turbidity data; plots of the sensor data; and R scripts used to generate the plots. Samples collected during this study were labeled as “Second Spatial Study” or “SSS.” A subset of data from the SSS samples were published in the contiguous United States (CONUS)-Scale Model-Sample (CM) study data package available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689 that presents data from across the CONUS. SSS data published in the CM data package were not included in this data package. They include dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), total nitrogen (TN), grain size, aerobic sediment respiration, dissolved oxygen (DO), and temperature. Parent IDs and Site IDs are consistent between the SSS and CM data packages, and they can be mapped directly so data across packages can be used together. Additionally, sensor data from a similar 2021 spatial study can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052 and 2021 sample data can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1898914. The 2021 spatial study had some sites in common with this 2022 spatial study. This dataset is comprised of three photo folders and one main data folder with six subfolders. The photo folders contain photographs and videos of cotton strip retrieval and sediment quadrats. The main data folder consists of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) total suspended solids (TSS) data and cotton strip tensile strength data and averages; (5) field protocol; (6) readme; (7) methods codes; (8) international generic sample number (IGSN) mapping file; (9) sensor installation methods summary; (10) stream depth and averages; and (11) Ultrameter data and averages. The Sonar subfolder consists of Sonar time-series depth data and a processing script. The BarotrollAtm, DepthHOBO, MantaRiver, miniDOT, and miniDOTManualChamber subfolders contain time-series data, plots, and summary files. All files are .csv, .pdf, .txt, .R, .Rmd, .jpg, .jpeg, .AVI, .mp4, or .mov. The data package was originally published in April 2023. It was updated in August 2023 (v2; modified files) and September 2024 (v3; modified files). See the change history section in the readme for details. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of 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 OSTI2025

COMPASS-FME Synoptic Site Characterization

This dataset contains soil biogeochemical and physicochemical characterization data for the COMPASS-FME synoptic sites.This dataset also contains data for the paper Patel et al. 2025 "Transition zones at the changing coastal terrestrial-aquatic interface", https://doi.org/10.1029/2025JG008978.Coastal soils are a significant but highly uncertain component of global biogeochemical cycles. These systems experience unique spatial and temporal variability in biogeochemical processes, driven by wetland-to-upland gradients and hydrological fluctuations. We studied drivers of coastal soil variability (a) at regional scales and (b) across transects from upland forest to wetland, in two contrasting regions — Lake Erie, a freshwater lacustrine system, and Chesapeake Bay, a saltwater estuarine system. Salinity-related analytes were a key driver of soil variability, not just in the saltwater system, but surprisingly, also in the freshwater system. We had hypothesized linear trends in biogeochemical parameters along the TAI – however, contrary to expectations, transition soils were not consistently intermediate between upland and wetland endmembers; the non-monotonic trends of carbon, phosphorus, iron along our transects suggest that these are key analytes to study in our regions. Rapidly changing soil factors across coastal gradients provide insights into which soil processes may act as precursors to ecosystem shifts. Our comprehensive soil characterization across the coastal transects provides essential data for mechanistic modeling of ecosystem dynamics.The data are provided as processed, csv files. Raw data and processing scripts can be accessed on GitHub (https://github.com/COMPASS-DOE/cmps-soil_characterization).A note on the nomenclature: the experimental design represents three points along the coastal gradient -- upland, transition, and wetland. "wetland" is referred to as "marsh" in the corresponding paper. The two terms can be used interchangeably for the sites in this study.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

CysLT1R receptor complex with Zafirlukast (P21 space group) structure (SFX@LCLS)

This dataset has overall quality of 2.4 Å (CC*=0.5), P1 space group and cell parameters 41.58, 68.56, 87.37, 76.33, 76.69, 81.19. Structure obtained from this data have been deposited under PDB ID 6RZ5. Data processing scripts are described in detail [here](https://gist.github.com/marinegor/96102c9b7ce87509a0832649d11ba927)

CXI↗
DOE OSTI2021

Guideline 36 Savings Calculator v0.1

This software package contains a simple calculator that can be used to estimate savings from implementing a retrofit of HVAC controls to ASHRAE Guideline 36 in a multi-zone office building. Implemented in Excel, the calculator has a user-interface front-end to configure parameters of the case study, such as location, building size, and existing control strategies, and a back-end containing results from a large number of pre-run annual energy simulations. The software package also contains the simulation models and associated pre- and post-processing scripts to generate these results.

Blum, David↗
DOE OSTI2022

BESTEST-GSR (Building Energy Simulation Test - Generation Simulation and Reporting) 2023 [SWR 18-23]

Building Energy Simulation Test (BESTEST) is an NREL-developed method to validate the qualitative performance of different whole building simulations engines relative to each other. https://www.nrel.gov/docs/legosti/old/6231.pdf The purpose of this repository is to generate BESTEST test case models, run simulations, and populate data for ASHRAE Standard 140 reporting spreadsheets for EnergyPlus® based whole building simulation tools. It was originally setup for 2014 version of Standard 140. In May of 2022 it was updated to the 2020 version of Standard 140. This update included updates and additions to existing test suites, the bulk of which was in Section 5.2 (Building Thermal Envelope and Fabric Load Tests). We did not add Section 5.5 Airside HVAC Equipment Performance, but we hope to add that later in 2022. At some point we also hope to add Section 5.2.4 ground modeling, which is currently excluded. Supported Tools The default IDF generation is based on the OpenStudio® CLI, but the workflow supports a 'Bring your own IDF' use case. Additionally, for non-EnergyPlus® based tools the post processing scripts can be used if simulation results are provided as a CSV file.The scripts on this repository should work on Mac, Windows, and Linux. Dependencies Install OpenStudio® 3.4.0 make sure command line can recognize the 'openstudio' command This includes EnergyPlus® 22.1 Install Ruby on your system if it isn't already setup. 2.7 is used for development but other versions may work Since OpenStudio has its own embedded Ruby, which is used for running measures, you don't necessarily have to use a version of Ruby supported by OpenStudio. Install RubyXL Ruby gem This is used to modify Microsoft Excel spreadsheets Install Parallel Ruby gem This allows the CLI to run simulations in parallel

Goldwasser, David↗
DOE OSTI2022

clif

SAND2022-12903 O clif contains code to perform climate fingerprinting. It includes capabilities for principal component analysis of climate data, Fourier analysis, a collection of pre-processing scripts, and time series statistical tools. It uses a templated object-oriented programming approach to integrate all classes with the operator in Scikit Learn. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Nichol, Jeffrey↗
DOE OSTI2024

TEAMER: Twin Ocean Power Wave Energy Converter Comprehensive Overview

These files collectively provide a comprehensive overview of the testing process, data analysis, and validation for the Twin Ocean Power device tested at the O.H. Hinsdale Wave Research Laboratory, supported by TEAMER funding. This resource includes an overview of power results for a series of 7 trials. The files included in this comprehensive overview include a comprehensive log sheet for each trial, a summary of all trials, and processing scripts for the raw data. It includes all raw data in .tsv and MATLAB compatible formats, an average power chart, angular velocity charts for each trial, trial metrics, and power output files. This resource includes images of the Twin Ocean Power Wave Energy Converter device components and movement during testing and video recordings of each trial.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Data from: "Reply to ‘The challenge of defining effectively-no-snow’"

This repository contains the data and code associated with the paper titled "Reply to ‘The challenge of defining effectively-no-snow’" published in Nature Reviews Earth and Environment, 2026. In this reply, we argue that the 10th percentile of peak SWE (Snow Water Equivalent), which we propose in the original article, can be used as intended given it's a standardized, impact-based benchmark for comparing snow conditions across regions, not as a literal measure of snow absence. We present new evidence with SNOwpack TELemetry (SNOTEL) data showing that years meeting the threshold are overwhelmingly associated with subsequent drought (given United States Drought Monitor conditions), supporting its hydrologic and societal relevance. We conclude that while the distinction between "effectively no snow" and "zero snow" should be clearly communicated, the original definition remains appropriate for assessing impacts on snow-dependent water systems. The file code_nree_ML_reply_2026.Rmd contains the main processing scripts which analyze the SNOTEL data. Data from the US Drought Monitor was downloaded at: https://usdmdataservices using the Get Drought Severity Statistics By Area Percent' option, saved to the *_HUC4_delineated.csv files (Hydrologic Unit Code), which are labeled accordingly. 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.

EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SNOW/ICE↗
DOE OSTI2021

LIM1TR: Lithium-Ion Modeling with 1-D Thermal Runaway (V.1.0)

LIM1TR (Lithium-Ion Modeling with 1-D Thermal Runaway) is an open-source code that uses the finite volume method to simulate heat transfer and chemical kinetics on a quasi 1-D domain. The target application of this software is to simulate thermal runaway in systems of lithium-ion batteries. The source code for LIM1TR can be found at https://github.com/ajkur/lim1tr. This user guide details the steps required to create and run simulations with LIM1TR starting with setting up the Python environment, generating an input file, and running a simulation. Additional details are provided on the output of LIM1TR as well as extending the code with custom reaction models. This user guide concludes with simple example analyses of common battery thermal runaway scenarios. The corresponding input files and processing scripts can be found in the “Examples” folder in the on-line repository, with select input files included in the appendix of this document.

25 ENERGY STORAGE↗
DOE OSTI2022

Methods for Translating ReEDS Solutions to Production Cost Modeling Tools

Capacity expansion modeling tools are increasingly being utilized to investigate a wide range of potential future scenarios, particularly with high penetrations of variable and energy-constrained resources that may be operated differently than current dispatch paradigms. While capacity expansion models are particularly adept at making investment decisions for future years, they must make compromises in operational aspects to maintain computational tractability. However, it is of high interest to determine if such future systems would be able to maintain reliability during a variety of grid conditions, and to identify any potential operational challenges for these systems at finer temporal resolution than is typically captured in CEMs. As such, there is value in having an automated tool that can convert many CEM investment pathways into inputs for a more detailed production cost model, in this case the PLEXOS commercial software package. This paper describes the methodology used to make that translation for the NREL-developed ReEDS model, making use of two internally developed tools - PIDG and beetle - along with a set of processing scripts. We describe the current assumptions, data sets, and important operational characteristics of these translations along with an example of the extended analyses that may be done through this connection.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

A Python Tool for Reconstructing MCNP6 Particle Histories from an HDF5 PTRAC File [Slides]

A Python tool for converting the MCNP6 HDF5 PTRAC file to a list of Python trees is presented. The particle trees store MCNP6 simulated events for each history using parent-child relationships, which ensures that branching processes are accurately reproduced. A variety of post-processing scripts are presented and used in conjunction with the Python particle trees to make special tallies that are currently not available in the MCNP6 software and visualize the particle tracks.

97 MATHEMATICS AND COMPUTING↗