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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 OSTI2021

Single-component-at-a-time variation study for glass-ceramic waste forms

Here, a 51-sample composition variation study was performed on glass-ceramic waste forms for a raffinate waste stream from aqueous reprocessing of used nuclear fuel containing high fractions of Mo, alkalis, alkaline earths (AEs), and rare earths (REs). The study was designed with a single-component-at-a-time variation approach off a centroid composition. The components that were varied included Al, B, Ca, Li, Mo, Na, REs, Si, Zr, and Others (containing minor components). Data analysis included crystallization curves, microstructure, and phase compositions. A number of components (i.e., Li 2 O, B 2 O 3 , REO x , MoO 3 , Na 2 O, and ZrO 2 ) significantly impacted the concentration and chemistry of phases, especially the primary phases of oxyapatite [i.e., Ca 2 RE 8 (SiO 4 ) 6 O 2 ] and powellite (i.e., AEMoO 4 ), precipitated in the slow-cool heat-treated waste forms; minor phases included cerianite [i.e., Ce x Zr (1-x) O 2 ], Ba-molybdate [i.e., Ba(Gd 0.67 Mo 0.33 O 3 ], noble metals, pollucite (i.e., CsAlSiO 4 ), and RE-borosilicate (i.e., RE 3 BSi 2 O 10 ).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Iron XANES measurements of carbonates and phyllosilicates in CM chondrites: A record of redox conditions during aqueous alteration

Multiple generations of calcite and dolomite precipitated in CM chondrites during ice melting events that led to episodes of liquid water. Models and laboratory analysis have suggested a long-term transition from oxidizing to reducing conditions during aqueous alteration on the CM parent body. We found that synchrotron X-ray absorption near edge spectroscopy (XANES) can detect relative differences in the oxidation state of trace iron within these carbonates. In CM chondrites, previous work interpreted Mn abundance in calcite as an indicator of relatively early or late formation, and dolomite is understood to form relatively late. In the CM1 chondrite Meteorite Hills 01070, XANES maps reveal that Mn-poor calcite contains more oxidized iron relative to Mn-rich calcite. While these measurements of carbonates support increasing iron reduction with progressive aqueous alteration in MET 01070, comparison among different CM chondrites suggests a complex picture of redox evolution. In addition to carbonates, we performed XANES measurements of the phyllosilicate-rich matrix of Allan Hills 83,100. Pre-edge centroid analysis indicates that this CM1/2 has an oxidation state similar to typical CM2 chondrites. While additional measurements are warranted to confirm the full span of redox trends in CM carbonates, our data do not support a correlation between redox state and petrologic type.

Bailey, Elizabeth [Univ. of California, Santa Cruz↗
DOE OSTI2020

Characterization of groove density variation of VLS gratings with ALS XROL LTP-II in different operation modes

The long trace profiler, LTP-II, available at the Advanced Light Source (ALS) X-ray Optics Laboratory (XROL), was recently upgraded by replacing a multimode diode laser light source with a single-mode, wavelength-stabilized, fibercoupled diode laser system. The upgrade enables us to reliably characterize the lateral variation of groove density of variable-line-spacing (VLS) x-ray diffraction gratings. Here, we discuss the LTP-II performance with an example of measurements with a VLS grating with the groove density at the grating center of 300 lines/mm. For the measurements, we use the LTP-II in two different operation arrangements, the single Gaussian beam and the pencil beam interferometer arrangements. For each operation arrangement, we apply two data processing algorithms: with calculating the centroid position and with determining the position of a characteristic features of the detected beam intensity distributions. Additionally, we discuss the observed strong correlation between the LTP-II modes of operation and the resulted (extracted) groove density variations. We also speculate on possible origin of the correlation.

42 ENGINEERING↗
DOE OSTI2023

New-generation geostationary satellite reveals widespread midday depression in dryland photosynthesis during 2020 western U.S. heatwave

Emerging new-generation geostationary satellites have broadened the scope for studying the diurnal cycle of ecosystem functions. We exploit observations from the Geostationary Operational Environmental Satellite-R series to examine the effect of a severe U.S. heatwave in 2020 on the diurnal variations of ecosystem photosynthesis. We find divergent responses of photosynthesis to the heatwave across vegetation types and aridity gradients, with drylands exhibiting widespread midday and afternoon depression in photosynthesis. The diurnal centroid and peak time of dryland gross primary production (GPP) substantially shift toward earlier morning times, reflecting notable water and heat stress. Our geostationary satellite-based method outperforms traditional radiation-based upscaling methods from polar-orbiting satellite snapshots in estimating daily GPP and GPP loss during heatwaves. These findings underscore the potential of geostationary satellites for diurnal photosynthesis monitoring and highlight the necessity to consider the increased diurnal asymmetry in GPP under stress when evaluating carbon-climate interactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Isoscalar and Isovector Giant Resonances in Closed Shells Nuclei and Bulk Properties of Nuclear Matter

Centroid energies, E CEN , of the isoscalar (T = 0) and isovector (T = 1) giant resonances of multipolarities L = 0 – 3 in 40,48 Ca, 68 Ni, 90 Zr, 116 Sn, 144 Sm and 208 Pb, were calculated within the fully self-consistent spherical Hartree-Fock (HF)-based random phase approximation (RPA) theory, using 33 different energy density functionals associated with Skyrme-type effective nucleon-nucleon interactions of the standard form commonly employed in the literature. Here, we also calculate the Pearson linear correlation coefficients between each E CEN and each bulk property of nuclear matter (NM), associated with the Skyrme interactions used in the calculations, and determine the sensitivity of E CEN to bulk properties of NM. By comparing the calculated values of E CEN to the experimental data, we constrain the values of the bulk NM properties. We find that interactions associated with the values of the NM effective mass, m* / m = 0.70 to 0.90, incompressibility coefficient, K NM = 210 to 240 MeV, and the enhancement coefficient of the energy weighted sum rule of the isovector giant dipole resonance, κ = 0.25 to 0.70, best reproduce the experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Single-neutron adding on $^{34}$S

For this work, single-neutron adding data was collected in order to determine the distribution of the single-neutron strength of the 0ƒ 7/2 , 1p 3/2 , 1p 1/2 and 0ƒ 5/2 orbitals outside of Z = 16, N = 18, 34 S. The 34 S(d,p) 35 S reaction has been measured at 8 MeV/u to investigate cross sections to excited states in 35 S. Outgoing proton yields and momenta were analyzed by the Super-Enge Split-Pole Spectrograph in conjunction with the CeBrA demonstrator located at the John D. Fox Laboratory at Florida State University. Angular distributions were compared with Distorted Wave Born Approximation calculations in order to extract single-neutron spectroscopic overlaps. Spectroscopic overlaps and strengths were determined for states in S up through 6 MeV in excitation energy. Each orbital was observed to have fragmented strength where a single level carried the majority. The single-neutron centroids of the 0ƒ 7/2 , 1p 3/2 , 1p 1/2 and 0ƒ 5/2 orbitals were determined to be 2360$^{+90}_{-40}$ keV, 3280$^{+80}_{-50}$ keV, 4780$^{+60}_{-40}$ keV, and ≳7500 keV, respectively. A previous discrepancy in the literature with respect to the distribution of the neutron 1p 1/2 strength was resolved. The integration of the normalized spectroscopic strengths, up to 5.1 MeV in excitation energy, revealed fully-vacant occupancies for the 0ƒ 7/2 , 1p 3/2 , and 1p 1/2 orbitals, as expected. The spacing in the single-neutron energies highlighted a reduction in the traditional N = 28 shell-gap, relative to both the 1p spin-orbit energy difference (N = 32) and the lower limit on the N = 34 shell spacing.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗
DOE OSTI2022

The Response of the Polar Vortex to Tropospheric Temperature Eddies in an Idealized General Circulation Model

Abstract A dry-core idealized general circulation model with a stratospheric polar vortex in the Northern Hemisphere is run with a combination of simplified topography and imposed tropospheric temperature perturbations, each located in the Northern Hemisphere with a zonal wavenumber of 1. The phase difference between the imposed temperature wave and the topography is varied to understand what effect this has on the occurrence of polar vortex displacements. Geometric moments are used to identify the centroid of the polar vortex for the purposes of classifying whether or not the polar vortex is displaced. Displacements of the polar vortex are a response to increased tropospheric wave activity. Compared to a model run with only topography, the likelihood of the polar vortex being displaced increases when the warm region is located west of the topography peak, and decreases when the cold region is west of the topography peak. This response from the polar vortex is due to the modulation of vertically propagating wave activity by the temperature forcing. When the southerly winds on the western side of the topographically forced anticyclone are collocated with warm- or cold-temperature forcing, the vertical wave activity flux in the troposphere becomes more positive or negative, respectively. This is in line with recent reanalysis studies that showed that anomalous warming west of the surface pressure high, in the climatological standing wave, precedes polar vortex disturbances.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

East Asian Rainbands and Associated Circulation over the Tibetan Plateau Region

Abstract Rainbands that migrate northward from spring to summer are persistent features of the East Asian summer monsoon. This study employs a machine learning algorithm to identify individual East Asian rainbands from May to August in the 6-hourly ERA-Interim reanalysis product and captures rainband events during these months for the period 1979–2018. The median duration of rainband events at any location in East Asia is 12 h, and the centroids of these rainbands move northward continuously from approximately 28°N in late May to approximately 33°N in July, instead of making jumps between quasi-stationary periods. Whereas the length and overall area of the rainbands grow monotonically from May to June, the intensity of the rainfall within the rainband dips slightly in early June before it peaks in late June. We find that extratropical northerly winds on all pressure levels over East China are the most important anomalous flow accompanying the rainband events. The anomalous northerlies augment climatological background northerlies in bringing low moist static energy air and thus generate the front associated with the rainband. Persistent lower-tropospheric southerly winds bring in moisture that feeds the rainband and are enhanced a few days prior to rainband events, but they are not directly tied to the actual rainband formation. The background northerlies could originate as part of the Rossby waves resulting from the jet stream interaction with the Tibetan Plateau. The ageostrophic circulation in the jet entrance region peaks in May and weakens in June and July and does not prove to be critical to the formation of the rainbands.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and evaluate potential diagnostic and therapeutic modalities. The description of the environment in which a biomedical simulation operates (model context) and parameterization of internal model rules (model content) requires the optimization of a large number of free parameters. In this work, we utilize a nested active learning (AL) workflow to efficiently parameterize and contextualize an ABM of systemic inflammation used to examine sepsis. Contextual parameter space was examined using four parameters external to the model’s rule set. The model’s internal parameterization, which represents gene expression and associated cellular behaviors, was explored through the augmentation or inhibition of signaling pathways for 12 signaling mediators associated with inflammation and wound healing. We have implemented a nested AL approach in which the clinically relevant (CR) model environment space for a given internal model parameterization is mapped using a small Artificial Neural Network (ANN). The outer AL level workflow is a larger ANN that uses AL to efficiently regress the volume and centroid location of the CR space given by a single internal parameterization. We have reduced the number of simulations required to efficiently map the CR parameter space of this model by approximately 99%. In addition, we have shown that more complex models with a larger number of variables may expect further improvements in efficiency.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An Automatic Learning Framework for Smart Residential Communities

Predictability has been foundational to matching supply and demand in the day-to-day operation of the electric power system. Demand predictability is eroding because of the increased use of renewable energy resources and more sophisticated loads, such as electric vehicles and smart appliances. In this paper, an automatic software framework is described which can be used for load forecasting in smart communities. A time-varying clustering-based Markov chain approach is used to predict the energy consumption of residential buildings in a smart community. The training data is based on 1-minute meter data of occupied homes over one month. The data points are first clustered based on the energy consumption and the time of the day. Then, the original data is converted using the Centroids of the clusters. A time-varying Markov chain is subsequently trained to model the energy consumption behavior of residents for each home using the transformed data. The trained model is shown to successfully predict load in 5-minute intervals over a 24 hours period.

Zandi, Helia↗
DOE OSTI2021

Model America - data and models of every U.S. building

The 5-year goal of the 'Model America' concept was to generate a model of every building in the United States. This data repository delivers on that goal. Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). There were 125,714,640 buildings detected in the United States and this dataset contains 122,930,327 (97.8%) buildings which resulted in a successful simulation. Future, annual updates have been proposed that may include additional buildings, data improvements, or other algorithmic enhancements. This dataset of 122.9 million buildings includes: Models (state_county.zip) - OpenStudio (v3.1.0) and EnergyPlus (v9.4) building energy models. Please note that the download requires the free Globus Connect Personal (https://www.globus.org/globus-connect-personal); Each model has approximately 3,000 building input descriptors that can be extracted. Please see the EnergyPlus(v9.4) 2,784-page Input/Output Reference Guide (https://energyplus.net/sites/all/modules/custom/nrel_custom/pdfs/pdfs_v9.4.0/InputOutputReference.pdf) for everything that can be retrieved or simulated from these models. These models were derived from the following metadata, which is not included in this dataset: 1. ID - unique building ID 2. County - county name 3. State - state name 4. CZ - ASHRAE Climate Zone designation 5. Clim_Zone - text label of climate zone 6. est_year - estimated year of construction 7. est_commercial - estimated building type (0=residential, 1=commercial) 8. Centroid - building center location in latitude/longitude (from Footprint2D) 9. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 10. Height - building height (meters) 11. Area2D - footprint area (ft2) 12. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 13. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 14. NumFloors - number of floors (above-grade) 15. Area - estimate of total conditioned floor area (ft2) 16. Standard - building vintage. These models are made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy's (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Please cite as: New, Joshua R., Adams, Mark, Bass, Brett, Berres, Anne, and Clinton, Nicholas (2021). 'Model America - data and models of every U.S. building. [Data set].' Constellation, doi.ccs.ornl.gov/ui/doi/339, April 14, 2021

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Energy density functional and sensitivity of energies of giant resonances to bulk nuclear matter properties

The development of a modern and more realistic nuclear energy density functional (EDF) for accurate predictions of properties of nuclei is the subject of enhanced activity, since it is very important for the study of properties of nuclear matter (NM), giant resonances and, in particular, of properties of rare nuclei with unusual neutron-to-proton ratios. Here, we provide a short review of the current status of the nuclear EDF and the theoretical results obtained for properties of nuclei and nuclear matter. We will first describe a method for determining the parameters of the EDF, associated with the Skyrme type effective interaction, by carrying out a Hartree-Fock based fit to extensive set of data of ground state properties and constraints. Next we will describe the fully self-consistent Hartree-Fock (HF) based random-phase-approximation (RPA) theory for calculating the strength functions S(E) and centroid energies E CEN of giant resonances and the folding model (FM) distorted wave Вorn approximation (DWBA) to calculate the excitation cross section of giant resonances by α scattering. Then we will provide results for: (i) the Skyrme parameters of the KDE0v1 EDF; (ii) consequences of violation of self-consistently in HF-based RPA; (iii) FM-DWBA calculation of excitation cross section; (iv) values of the E CEN of isoscalar and isovector giant resonances of multipolarities L=0–3 for a wide range of spherical nuclei, using 33 EDFs associated with standard form of the Skyrme type interactions, commonly employed in the literature; and (v) the sensitivities E CEN of the giant resonances to bulk properties of NM. We also determine constraints on NM properties, such as the incompressibility coefficient and effective mass, by comparing with experimental data on E CEN of giant resonances.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Oceanographic Conditions. 2007 - 2040. North Slope Alaska.

Complete representations of oceanographic conditions require spatial and temporal information about the significant wave height (Hs), peak wave period (Tp), wind speed, wind direction, wave direction, water level, salinity, and temperature. This data develops location-independent typologies to reduce the number of boundary conditions needed to assess nearshore oceanographic environments in both a Historical (2007-2019) and Future (2020-2040) timespan along the Alaskan North Slope. Wave information for both time spans were generated from WaveWatch III, Delft3D-FLOW, and Delft3D-WAVE simulations forced by wind conditions from reanalysis data (e.g., ASRv2 and ERA5) for the historical simulations while projected conditions were obtained from downscaled GFDL-CM3 forced under RCP8.5 conditions. Salinity was generated from GOFS 3.1 for the years between 2008-2015 and skin temperature of the ocean was obtained from ASRv2 reanalysis data for the years between 2007-2016. To identify generalized oceanographic typologies, K-means clustering was applied to the energy-weighted joint-probability distribution of Hs and Tp at six sites along the North Slope of Alaska. Distributions of wave and wind direction, wind speed, and water level associated with locaiton-indepndent centroids were assigned single values to describe a reduced order, typological rendition of offshore oceanographic conditions. These final typologies and their constituent data are provided here and can be used to evaluate the change in ocean energy over the next two decades in response to climate change and provide insight into expected consequences such as coastal erosion and flooding. A full assessment of the findings and techniques developed can be found in: Eymold, W.K., Flanary, C., Erikson, L., Nederhoff, K., Chartrand, C.C., Jones, C., Kasper, J., and Bull, D.L. Typological Representation of the Offshore Oceanographic Environment along the Alaskan North Slope. Continental Shelf Research (2022). 10.1016/j.csr.2022.104795

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Model America - Arizona extract from ORNL's AutoBEM v1.1

Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM).Two sets of sample data are provided for 2,555,152 buildings located within the boundary of Arizona in the United States:Data (846.3MB *.csv) - minimalist list of each building (rows) for the following fields (columns) • ID - unique building ID • Centroid - building center location in latitude/longitude (from Footprint2D) • Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) • State_abbr - state name • Area - estimate of total conditioned floor area (ft2) • Area2D - footprint area (ft2) • Height - building height (ft) • NumFloors - number of floors (above-grade) • WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) • CZ - ASHRAE Climate Zone designation • BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards • Standard - building vintage • Sample Models (114GB*.zip by county) - OpenStudio and EnergyPlus building energy models named according to IDThis data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Model America: Data and Models for every U.S. Building

The 5-year goal of the “Model America” concept was to generate a model of every building in the United States. This data repository delivers on that goal with "Model America v1". Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (bit.ly/AutoBEM). There were 125,715,609 buildings detected in the United States. Of this number, 122,146,671 (97.2%) buildings resulted in a successful generation and simulation of a building energy model. This dataset includes the full 125 million buildings. Future updates may include additional buildings, data improvements, or other algorithmic model enhancements in "Model America v2". This dataset contains OSM and IDF zip files for every U.S. county. Each zip file contains the generated buildings from that county. The .csv input data contains the following data fields: 1. ID - the Unique Building Identifier (UBID), generated using the Pacific Northwest National Laboratory (PNNL) BuildingID framework 2. Centroid - building center location in latitude/longitude (from Footprint2D) 3. Footprint2D - building polygon of 2D footprint (lat1/lon1_lat2/lon2_...) 4. State_abbr - state name 5. Area - estimate of total conditioned floor area (ft2) 6. Area2D - footprint area (ft2) 7. Height - building height (ft) 8. NumFloors - number of floors (above-grade) 9. WWR_surfaces - percent of each facade (pair of points from Footprint2D) covered by fenestration/windows (average 14.5% for residential, 40% for commercial buildings) 10. CZ - ASHRAE Climate Zone designation 11. BuildingType - DOE prototype building designation (IECC=residential) as implemented by OpenStudio-standards 12. Standard - building vintage This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL) Laboratory Directed Research and Development (LDRD), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), Biological and Environmental Research (BER), and National Nuclear Security Administration (NNSA). Update (September 23, 2025): We corrected the ID field in all state-level.csv input files to ensure one-to-one consistency with the corresponding .osm and .idf output files. The schema and file structure are unchanged; only the values in the ID column were modified. No files were added or removed, and the .zip bundles (containing .osm / .idf) are unchanged. The corrected .csv inputs were re-extracted in March 2025 from the original data generated ~ 2021 (Theta supercomputer runs), and published here to align input IDs with model outputs. Update (September 6, 2026): The Model America dataset was updated to replace the previous building ID field with the Unique Building Identifier (UBID), using the Pacific Northwest National Laboratory (PNNL) BuildingID framework. UBIDs provide standardized, location-based identifiers for individual building footprints and improve interoperability with other building and geospatial datasets. The data files containing the previous building identifiers were updated to include UBIDs. This update standardizes building identification; the underlying Model America building characteristics and energy simulation results were not recomputed as part of this update.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

CHESS 2025: Crown polygons and extracted reflectance for field sampling sites

This dataset contains (1) crown polygons for each tree, meadow, and shrub site sampled in the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign (in geojson format, .geojson) and (2) extracted reflectance, uncertainty, and shade estimates for each crown polygon from the 2018 National Ecological Observatory Network (NEON) and 2025 CHESS campaigns. (in CSV format, .csv). Additional metadata are provided in a data dictionary describing column names and definitions (dd.csv), and in a file-level metadata file (flmd.csv). Crown polygons were manually delineated for each site in the 2025 campaign using a combination of field-collected GPS data (doi:10.15485/3022418), RGB (red, green, blue) and false color reflectance mosaics (doi:10.15485/3013535), and LiDAR-derived (Light Detection and Ranging) canopy height (CHM) and digital surface (DSM) models (DOI and citation to be added upon publication). Where there was misalignment between the spectrometer- and LiDAR-derived data products, polygons prioritized alignment with the spectrometer-derived data products. Polygons were delineated conservatively to only select pixels representative of vegetation samples collected in the field. Crown polygons for 2018 are published at (doi:10.15485/1618130) and were developed using the same protocol. For each polygon, all pixels from all flightlines were extracted where the pixel centroid was contained within the polygon. For each pixel, we extracted the surface reflectance, uncertainty, and shade estimates. Details on the extracted datasets are available at (doi:10.15485/3013527, doi:10.15485/3013535). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2020

The Effects of Assumed Source Depth and Shear-Wave Velocity on Moment Tensors Estimated for Small, Contained Chemical Explosions in Granite

ABSTRACT The Source Phenomenology Experiment (SPE-Arizona) included of a series of chemical explosions detonated within a copper mine in Arizona. This study focuses on ground motions from detonations in the copper mine, which are analyzed to assess the uniqueness of the resulting source representation when the source region propagation characteristics have a range of possible models. P-wave velocities are well constrained by refraction data with less constraint of the S-wave velocities. The effects of explosion source depth and VS are assessed with Green’s functions for a range of models in which VP is held constant. Propagation models with a Poisson’s value of 0.25 and a source depth 30–60 m most accurately replicate the data. The explosion was detonated at a centroid depth of 30 m, so trade-offs in depth are demonstrated. The compensated linear vector dipole and explosion components of the Green’s functions convolved with a Mueller–Murphy source function are compared. Both produce significant energy in the 2–12 Hz band, due to surface-wave contributions with no clear depth dependencies above 20 Hz. The range of propagation models is used with the observational data to invert for the frequency-domain moment tensor. Fits to the data from these inversions have cross-correlation values of 0.64, demonstrating effectiveness in replicating the observations with the assumed propagation path effects and resulting source function. Inversions produce horizontal dipoles (Mxx and Myy), roughly half the maximum amplitude of Mzz, consistent with a compensated linear vector dipole source, which is frequency dependent. Denny and Johnson, Mueller–Murphy, Walter and Ford, and the revised Mueller–Murphy source models, parameterized for granite, are compared to the moment tensors. Despite a nonisotropic moment tensor source, the revised Mueller–Murphy isotropic source model best replicates the long-period moments, overshoot, and corner frequency.

Geochemistry & Geophysics↗