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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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66 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Discrete element modeling of irregular-shaped soft pine particle flow in an FT4 powder rheometer

Pine residues are a commonly used biomass feedstock that consists of different anatomical fractions, each with distinct particle characteristics. Here, in this work, an experiment-informed discrete element model is developed to investigate the flowability of pine residues in an FT4 rheometer. Multi-sphere particles with distinct particle attributes are created to model each anatomical fraction type. A systematic analysis of specimens with varying particle characteristics (e.g., anatomical fraction type, particle shape, and size) is conducted to elucidate the relationship between particle attributes and flowability. The results show that stems recorded the highest axial force and torque and, correspondingly, the highest flow energy, which is attributed to their high stiffness and interlocking effect. Increasing their percentage in the mixture increases the flow energy while increasing needles tends to decrease flow energy. Knowledge gained in this study on the flow of anatomical fractions is important for the efficient and robust processing of pine residues.

09 BIOMASS FUELS↗
DOE OSTI2024

Utah FORGE: Interferometric Synthetic Aperture Radar Data from 2023 and 2024

The dataset comprises Interferometric Synthetic Aperture Radar (InSAR) data from the TerraSAR-X and TanDEM-X satellite missions, covering the Utah FORGE site. This data includes interferometric pairs created using GMT-SAR processing software, chosen for their short orbital separations between May 1, 2023, and June 30, 2024. Included are various data and metadata, including Digital Elevation Models, unit vectors, and correlation coefficients. The dataset is packaged in several compressed tar files and formatted in NetCDF. To utilize this dataset, users will need software capable of handling NetCDF files and tools for decompressing tar files.

15 GEOTHERMAL ENERGY↗
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 OSTI2025

Multiple RGB ortho-mosaics and digital surface models in 2017 and 2018 across the Lower Montane site in the East River Watershed, Colorado

Aerial imagery was collected at the Lower Montane site (Pumphouse) in the East River Watershed, Colorado during the spring, summer, and fall seasons of 2017 and 2018 to improve the understanding of seasonal vegetation dynamics and their drivers. The datasets include Red-Green-Blue (RGB) ortho-mosaics and digital surface models (DSMs) inferred from the Unoccupied Aerial System (UAS) acquired aerial RGB imagery for June 3, June 19, July 7, and August 14, 2017, and for March 14, April 26, June 1, June 18, July 6, and August 7, 2018. Real-Time Kinematic Global Positioning System (RTK-GPS) surveyed Ground control points (GCPs) were used to increase the reconstruction accuracy. The reconstructed RGB mosaics and DSMs have been trimmed to cover a similar spatial domain. The accuracy of the RGB mosaics is considered high (~10 cm). DSM accuracy is highest (~10 cm) where sufficient GCPS are available, and more difficult to assess elsewhere (see reconstruction reports for uncertainty estimates). The dataset includes a total of 20 GeoTIFF (.tif) files, 10 PDF (.pdf) files, 3 data CSV (.csv) files, and 2 metadata CSV (.csv) files. Feel free to contact the authors with any questions or collaboration interests.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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed'

This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2025

Dense granular flows with MFIX-Exa

This report extends the linear spring dashpot collision model the discrete element method available in MFIX-Exa to include static a static tangential friction force. Additionally, two rolling friction models frequently used in the literature are also implemented. The governing equations are provided with an emphasis on the new terms. The new model is validated by comparison to existing experimental data of single particle oblique collisions. The model is then tested on three dense granular flow problems: the formation of static piles, the discharge from a flat-bottom hopper and the self-induced granular Rayleigh-Taylor instability.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Two-Way Coupled CFD/DEM Simulation of Electromagnetic Heating in a Fluidized Bed Reactor

A set of tools has been developed to couple a multiphase reacting computational fluid dynamics solver (MFIX) with two computational electromagnetic solvers (COMSOL and Elmer). In contrast to several recent investigations, which calculate a single static EM field prior to the CFD calculation, in the present work the electromagnetic field is periodically updated during the simulation. This enables the EM field and associated EM heating predictions to reflect the effects of evolving material properties (temperature, composition, size etc.) and the evolving distribution of material in the system. The present work uses effective medium theory (EMT) for the electromagnetic field calculations since the goal is to apply the tool for industrial scale reacting systems. Predictions using MFIX/COMSOL and MFIX/Elmer are compared to in bed thermocouple measurements from a fluidized bed experiment and recent simulation predictions and recent experiments performed at NETL. The NETL experiments also include packed bed operating conditions. Coupled MFIX/COMSOL simulations at these conditions are compared with similar simulations using only COMSOL to measure errors due to the solver coupling approach.

CFD↗
DOE OSTI2026

Onset of Fluidization in MP-PIC Simulations using MFIX-Exa

Fluidized bed reactors are used across a variety of industries, including for energy processes like pyrolysis that result in low-cost energy products. Design and scale-up of fluidized beds is de-risked by modeling and simulation, utilizing tools like NETL’s MFIX-Exa High-Performance Computing (HPC) code for reacting multiphase flow. This report summarizes an investigation into the breadth of problems to which MFIX-Exa may be applied, specifically with regard to low fluid velocities and the onset of fluidization. A simple fluidization study is conducted both experimentally and numerically for particles of interest, then reactor simulations are compared to cold flow experiments for uniform distributor plates. Approaches for modeling bubble caps are also presented.

discrete particle method↗
DOE OSTI2025

Convergence and Quantum Advantage of Trotterized MERA for Strongly-Correlated Systems

Strongly-correlated quantum many-body systems are difficult to study and simulate classically. We recently proposed a variational quantum eigensolver (VQE) based on the multiscale entanglement renormalization ansatz (MERA) with tensors constrained to certain Trotter circuits. Here, we determine the scaling of computation costs for various critical spin chains which substantiates a polynomial quantum advantage in comparison to classical MERA simulations based on exact energy gradients or variational Monte Carlo. Algorithmic phase diagrams suggest an even greater separation for higher-dimensional systems. Hence, the Trotterized MERA VQE is a promising route for the efficient investigation of strongly-correlated quantum many-body systems on quantum computers. Furthermore, we show how the convergence can be substantially improved by building up the MERA layer by layer in the initialization stage and by scanning through the phase diagram during optimization. For the Trotter circuits being composed of single-qubit and two-qubit rotations, it is experimentally advantageous to have small rotation angles. We find that the average angle amplitude can be reduced considerably with negligible effect on the energy accuracy. Benchmark simulations suggest that the structure of the Trotter circuits for the TMERA tensors is not decisive; in particular, brick-wall circuits and parallel random-pair circuits yield very similar energy accuracies.

Miao, Qiang [Duke Quantum Center, Duke University,↗
DOE OSTI2024

Exploring the Regimes of Particle Behavior upon Impact via the Discrete Element Method

Discrete element method simulations are conducted to probe the various regimes of post-impact behavior of particles with solid surfaces. The impacting particles are described as spherical agglomerates consisting of smaller constituent (or primary) particles held together via surface adhesion. Under the influence of a wide range of impact velocities and particle surface energies, five distinct behavioral regimes—rebounding, vibration, fragmentation, pancaking, and shattering—are identified, and force transmission patterns are linked to post-impact behavior. In the rebounding regime, the coefficient of restitution decreases linearly as impact velocity increases and the particle agglomerate experiences compaction. In the fragmentation regime, rebound velocity generally decreases with increasing fragment size. The rebound velocity of fragments decreases with time except for the smallest fragments, which can increase in velocity due to collisions with other fragments of high velocity. Particle breakage in the pancaking regime does not follow common mechanistic models of breakage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗