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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Supporting data for climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics

This data supports the conclusions found in climatic clustering and longitudinal analysis with impacts on food, bioenergy, and pandemics. Included here are (i) the binarized geolocation vectors used for exhaustive vector comparisons, (ii) the resulting climatic networks, (iii) the results of applying Markov clustering to the climatic networks, and (iv) the results of applying Correlation-of-Correlations (cor-cor) to the climatic networks. The set of binarized geolocation vectors that are used as inputs for the Combinatorial Metrics library (CoMet) are of the form comet-UUUUUxVVVVV-XXXX-YYYY.shuffled.tped where UUUUU is the number of vectors, VVVVV is the length of each vector, XXXX is the starting year, and YYYY is the ending year. Each line corresponds to a geolocation vector of binary elements A (i.e., 0) and T (i.e., 1). The set of climatic networks that are used for downstream network analysis are of the form network-U-way-XXXX-YYYY.parsed.txt where U is the order of the comparison (2-way or 3-way), XXXX is the starting year, and YYYY is the ending year. Each line corresponds to an edge linking two geolocations (defined by latitude and longitude) with its corresponding edge weight (i.e., DUO score). The set of cluster results are of the form clusters-U-way-XXXX-YYYY-thresh-VVVV-inflation-WWW.clustered.txt where U is the order of the comparison (2-way or 3-way), XXXX is the starting year, YYYY is the ending year, VVVV is the similarity threshold, and WWW is the Markov clustering inflation rate. Each line corresponds to a single cluster and is composed of a number of corresponding geolocations (defined by latitude and longitude). The set of cor-cor results are of the form corcor-U-way-XXXX-YYYY.cumulative.txt where U is the order of the comparison (2-way or 3-way), XXXX is the starting year, and YYYY is the ending year. Each line corresponds to a single geolocation with it's corresponding cor-cor value.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Large emissions of CO2 and CH4 due to active-layer warming in Arctic tundra: Supporting Data

Climate warming may accelerate decomposition of Arctic soil carbon, but few controlled experiments have manipulated the entire active layer. To determine surface-atmosphere fluxes of carbon dioxide and methane under anticipated end-of-century warming, we used heating rods to warm soil (by 3.8 °C) to the depth of permafrost in polygonal tundra over two growing seasons at the Barrow Environmental Observatory in Utqiaġvik (formerly Barrow), Alaska. This data product includes supporting data for the companion paper entitled "Large emissions of CO2 and CH4 due to active-layer warming in Arctic tundra", published in Nature Communications by Torn et al. This data package contains seven data files in csv format with corresponding data dictionaries and file-level metadata, describing vegetation biomass dry weight ("Utqiagvik_Vegetation_Biomass_2014.csv"), radiocarbon measurements of respired carbon dioxide ("Utqiagvik_Radiocarbon_2015_2016.csv"), surface-atmosphere fluxes of carbon dioxide and methane and associated soil temperature ("Utqiagvik_Fluxes_Temperature_2015_2016.csv"), and soil temperature measurements averaged every 15-min and 1-hr for 2015 and 2016 ("Utqiagvik_Temperature_15min_2015.csv", "Utqiagvik_Temperature_15min_2016.csv", "Utqiagvik_Temperature_1hr_2015.csv", "Utqiagvik_Temperature_1hr_2016.csv"). There are no specific software requirements to use these data. UIC Science Native Corporation facilitated our scientific research on the Barrow Environmental Observatory, which is Iñupiat land.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↗
DOE OSTI2026

Supporting Data: Synthesis and Computational Analysis of Uranium(III)-Pnictogen Bonds

This repository contains the supporting data for the publication titled "Synthesis and Computational Analysis of Uranium(III)-Pnictogen Bonds" by Lauren M. Lopez, Diana Perales, Allison N. Smolek, Matthias Zeller, Bess Vlaisavljevich and Suzanne C. Bart. The manuscript associated with these data was published in J. Am. Chem. Soc. (DOI: 10.1021/jacs.5c19802). For readers only seeking the XYZ files associated with the publication, a second zipped folder called "coordinates.tgz" is included. The README.txt file describes the contents of the folder containing the input and output files.

Vlaisavljevich, Bess [Department of Chemistry; Uni↗
DOE OSTI2021

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

Data supporting manuscript from L. Sheneman, G. Stephanopoulos, A.E. Vasdekis titled "Deep learning classification of lipid droplets in quantitative phase images" as currently under review at PLOS ONE. This includes: 1) raw and binary labeled Quantitative Phase Images (QPI) of Y. lipolytica cells used in the analyses described within the manuscript. 2) various derived data including classifier scores, etc.

ANN↗
DOE OSTI2024

Supporting Data: A Four‐Coordinate Pr 4+ Imidophosphorane Complex

Supporting computational data for the publication titled: A. C. Boggiano, S. Roy Chowdhury, M. D. Roy, M. G. Bernbeck, S. M. Greer, B. Vlaisavljevich,* and H. S. La Pierre* “A Four-Coordinate Pr4+ Imidophosphorane Complex" Angew. Chem. Int. Ed., 2024, e202409789. DOI: 10.1002/anie.202409789

Boggiano, Andrew C.↗
DOE OSTI2021

A novel physics-based and data-supported microstructure model for part-scale simulation of laser powder bed fusion of Ti-6Al-4V

The elasto-plastic material behavior, material strength and failure modes of metals fabricated by additive manufacturing technologies are significantly determined by the underlying process-specific microstructure evolution. In this work a novel physics-based and data-supported phenomenological microstructure model for Ti-6Al-4V is proposed that is suitable for the part-scale simulation of laser powder bed fusion processes. The model predicts spatially homogenized phase fractions of the most relevant microstructural species, namely the stable β-phase, the stable α s -phase as well as the metastable Martensite α m -phase, in a physically consistent manner. In particular, the modeled microstructure evolution, in form of diffusion-based and non-diffusional transformations, is a pure consequence of energy and mobility competitions among the different species, without the need for heuristic transformation criteria as often applied in existing models. The mathematically consistent formulation of the evolution equations in rate form renders the model suitable for the practically relevant scenario of temperature- or time-dependent diffusion coefficients, arbitrary temperature profiles, and multiple coexisting phases. Due to its physically motivated foundation, the proposed model requires only a minimal number of free parameters, which are determined in an inverse identification process considering a broad experimental data basis in form of time-temperature transformation diagrams. Subsequently, the predictive ability of the model is demonstrated by means of continuous cooling transformation diagrams, showing that experimentally observed characteristics such as critical cooling rates emerge naturally from the proposed microstructure model, instead of being enforced as heuristic transformation criteria. Eventually, the proposed model is exploited to predict the microstructure evolution for a realistic selective laser melting application scenario and for the cooling/quenching process of a Ti-6Al-4V cube of practically relevant size. Numerical results confirm experimental observations that Martensite is the dominating microstructure species in regimes of high cooling rates, e.g., due to highly localized heat sources or in near-surface domains, while a proper manipulation of the temperature field, e.g., by preheating the base-plate in selective laser melting, can suppress the formation of this metastable phase.

Inverse parameter identification↗
DOE OSTI2021

SPRUCE Warming Promotes the Use of Organic Matter as an Electron Acceptor in a Peatland: Supporting Data

These data are provided in support of the publication: Warming Promotes the Use of Organic Matter as an Electron Acceptor in a Peatland (Rush et al., 2021). Rush et al. (2021) reported on the effect of temperature on microbial organic matter reduction at the SPRUCE study site and S1 bog. Specifically, electron shuttling capacity (ESC), CH4 production, CO2 production, and acetate concentration were measured to explore both direct and indirect (through changes in soil quality and water-table level) effects of warming on microbial activity. Samples were collected in the summer of 2016 and 2017 depending on the experiment. This dataset contains five files in comma separated (*.csv) format. The SPRUCE Experiment Site is located in a Picea mariana [black spruce] Sphagnum spp. bog forest in northern Minnesota, 40 km north of Grand Rapids, in the USDA Forest Service Marcell Experimental Forest (MEF). This ecosystem, which is located at the southern margin of the boreal forest, is considered especially vulnerable to climate change and anticipated to be near its tipping point.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Supporting Data: Inverse Trans Influence and Uranium-Arene σ-Bonding Drive Molecular Geometry: Ligand Modification from Hard to Soft Flips the Oxide from Axial to Equatorial

Supporting Computational Data from the manuscript titled "ITI and Uranium-Arene s-Bonding Drive Molecular Geometry: Ligand Modification from Hard to Soft Flips the Oxide from Axial to Equatorial" by Benedikt Kestel, Daniel Pividori, Sabyasachi Roy Chowdhury, Frank W. Heinemann, Bess Vlaisavljevich, and Karsten Meyer published in J. Am. Chem. Soc. (DOI: 10.1021/jacs.5c05029).

Chowdhury, Sabyasachi Roy [Department of Chemistry↗
DOE OSTI2022

Integrating Very-High-Resolution UAS Data and Airborne Imaging Spectroscopy to Map the Fractional Composition of Arctic Plant Functional Types in Western Alaska: Supporting Data

Remote sensing maps of plant functional type (PFT) fractional cover (FCover), dominant PFT, and FCover uncertainty derived from NASA's Airborne Visible / Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG). The AVIRIS-NG imaging spectroscopy data (380-2510 nm) was collected as a part of the collaboration between NASA's Arctic-Boreal Vulnerability Experiment (ABoVE; Miller et al., 2019) and DOE's Next Generation Ecosystem Experiment in the Arctic (NGEE-Arctic). This package includes maps of the NGEE-Arctic Council watershed on the Seward Peninsula, Alaska, created using AVIRIS-NG imagery collected on July 9th, 2019. The map data and metadata are provided as GeoTIFF (*.tif), ENVI image (*.dat), and text (*.txt, *hdr) formats. Additional map quicklooks are provided as *.pdf files and GIS *.kml files. These datasets are provided in support of Yang et al., (2023), "Integrating Very-High-Resolution UAS Data and Airborne Imaging Spectroscopy to Map the Fractional Composition of Arctic Plant Functional Types in Western Alaska".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↗
DOE OSTI2021

Data supporting the article titled "Heterogeneous Multiphase Flow Properties of Volcanic Rocks and Implications for Noble Gas Transport from Underground Nuclear Explosions"

The submission is a Reproducible Research (RR) data archive that contains the supporting multiphase fluid flow and curve fitting results of the Vadose Zone Journal article titled: "Heterogeneous Multiphase Flow Properties of Volcanic Rocks and Implications for Noble Gas Transport from Underground Nuclear Explosions." The article's DOI number is the following: https://doi.org/10.1002/vzj2.20123. Please see the "Methods and Materials" section of the journal article and the ReadMe.txt in the RR_ARCHIVE folder for further details. 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. SAND Number: SAND2021-3407 O.

mercury injection capillary pressure↗
DOE OSTI2024

Substantial and overlooked greenhouse gas emissions from deep Arctic lake sediment - supporting data and code

This data package contains data, descriptions, and code-based analyses that were used to support conclusions drawn in “Substantial and overlooked greenhouse gas emissions from deep Arctic lake sediment”, by Freitas et al. (2025) (https://doi.org/10.1038/s41561-024-01614-y). The study evaluated greenhouse gas production along a deep sediment core (20 m) taken in 2018 from below Goldstream Lake, a field site approximately 15 km north of Fairbanks, Alaska.The file “ESSDive_NFreitas_2024_flmd.csv” includes an overview of all other csv files in this data package, namely: sediment descriptions (depth and type of sediment), sediment characterizations (bulk density, gravimetric water content, total carbon, etc.), calculated respiration and temperature sensitivity values associated with year-long incubations of the sediment core, and the R code used to process the dataset. Additional details regarding the content of these files and how the calculations were performed are described in the Methods section of this archive. The “data_dictionary_ESSDive_NFreitas_2024_dd.csv” is a data dictionary for all files included in the data package. Each row in the data dictionary represents a column name in a given file. 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).Data package updates- August 2024: Title was updated, R code now includes a process for calculating potential production across the whole sediment column, the "sediment_column_production_potentials_ESSDive_NFreitas_2024.csv" is the associated data output file, and the file level metadata and data dictionaries were updated to reflect the contents of the additional csv file.- October 2024: Title was updated, and a statement was added to Step 2 of the Methods that describes that samples were collected (and exported) in a responsible manner and in accordance with relevant permits and local laws.- January 2025: Updated the associated manuscript details in the Abstract (manuscript title, publication date, DOI link) and in the Related References section (full citation).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Historical Data Analysis Supporting the Data Quality Objectives for the INL Site Environmental Soil Monitoring Program

This document represents the initial evaluation and soil monitoring proposed by Battelle Energy Alliance, LLC (BEA) in 2015. The evaluation included analyses of historical soil monitoring data and soil inventories, current emission estimates, and modeled potential deposition/accumulation patterns. The initially proposed monitoring included a 5-year rotation of in-situ gamma measurements augmented by soil sampling with laboratory analyses near each major active and some inactive facilities. It also proposed rotational in-situ gamma measurements and soil sampling at two centrally located onsite air monitoring locations coinciding with sampling at the traditional offsite soil monitoring locations. The chosen alternative includes only physical soil sampling with laboratory analysis and only at the Radioactive Waste Management Complex (RWMC), the two air monitors and the offsite locations as documented in Data Quality Objectives Supporting the Environmental Soil Monitoring Program for the Idaho National Laboratory (INL) Site, INL/EXT-15-34909, Revision 0, February 2016. The data and evaluations in this document are valid for comparisons with future soil data that may be collected in many INL site locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Supporting data for "Frozen flume experiments indicate rapid permafrost riverbank erosion depends on bank roughness"

This dataset contains supporting files detailing five frozen flume experiments conducted at the Caltech Earth Surface Dynamics Laboratory to investigate rates of ablation-limited permafrost riverbank erosion under controlled conditions. Water flowed past a bank of saturated, frozen sand and ice and gradually eroded the bank by thawing pore ice and immediately entraining sand and washing it downstream. Experiments were scaled for flow hydraulics and heat transfer allowing comparisons between our results and natural permafrost riverbanks. For each experiment, we measured the initial and final sand bank topography using a Keyence laser scanner, water surface slope at 3-min intervals throughout the experiment using a Massa sonar scanner, bank erosion using 10-sec overhead timelapse imagery taken by an overhead camera, water and bank temperature using thermistors frozen into the sand bank and sampling at 2 Hz, and water discharge using an in-line flow meter. We include calibration data for the carriage (engineered by the Saint Anthony Falls Laboratory) used to make sonar and laser topography measurements. We also include calibration data for temperature sensors, water discharge measurements, and images of a regular grid placed in the flume to align overhead camera images with the carriage datum. Grain size analysis for the channel bed (gravel) was produced using a pebble count and bank sand was measured using a Camsizer X2. In addition to the five frozen experiments, we include sonar scans of water surface slope and Keyence scans of bed and bank topography for calibration experiments ran with an immobile gravel bank and bed.

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↗