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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Automated Energy-Dispersive X-ray Spectroscopy Analysis for Multi-Modal Few-Shot Learning

Scanning transmission electron microscopy (STEM) is a powerful tool that allows for the atomic-scale analysis of a materials’ structure, chemistry, and defect domains (Akers et al. 2021). The current generation of microscopes generate vast amounts of data, surpassing the limits of effective manual analysis traditionally performed by domain experts (Spurgeon et al. 2021). While recent strides in machine learning have significantly enhanced the processing of large and intricate datasets acquired through electron microscopy, the prevalent use of proprietary software packages for initial data collection poses a challenge. In many cases, these software packages act as a ‘black box’, constraining user functionality and hindering the output of data in a format that is conducive to seamless integration into machine learning models. This work addresses these challenges by adapting HyperSpy, an open-source Python library, for the analysis and quantification of raw energy dispersive spectroscopy (EDS) data acquired through STEM. The modified HyperSpy code successfully facilitates user-defined segmentation of the data, enabling the integration of atomic %, weight %, and raw EDS spectra for each segmented region into an existing few-shot machine learning model. While initial results reveal discrepancies in quantified atomic and weight percentages when compared to proprietary software, ongoing efforts aim to rectify this issue by refining the fit of the HyperSpy model to the EDS spectra. Overall, this research underscores the potential of open-source tools like HyperSpy to enhance the accessibility of analytical tools, fostering a transparent and user-friendly environment for seamlessly incorporating electron microscopy data into machine learning models.

36 MATERIALS SCIENCE↗
DOE OSTI2026

FAIRLinked: Data FAIRification Tools for Materials Data Science

FAIRLinked is a software package created to support the FAIRification of materials science data, ensuring proper alignment with FAIR principles: Findable, Accessible, Interoperable, and Reusable. It is built to be compatible with MDS-Onto, an ontology designed to capture the semantics of various types of materials data, enabling integration and sharing across different research workflows. The package is subdivided into three subpackages: InterfaceMDS, RDFTableConversion, and QBWorkflow. The first subpackage, InterfaceMDS allows users to search for terms using either string search or various filters, explore different domains and subdomains, and add terms to MDS-Onto. RDFTableConversion is used for serialization and deserialization of data from CSV into JSONLDs and vice versa in a way that captures the semantics of the data using MDS-Onto. Lastly, QBWorkflow is a serialization and deserialization workflow that incorporates RDF Data Cube vocabulary, useful for working with multidimensional datasets. By offering these packages, FAIRLinked lowers the barrier of creating FAIR, machine-actionable data for researchers in the materials science community.

FAIR↗
DOE OSTI2024

Data and scripts associated with the manuscript "A Hydrogeophysical Framework to Assess Infiltration During a Simulated Ecosystem-Scale Flooding Experiment”

This package contains the data and scripts used in "A hydrogeophysical framework to assess infiltration during a simulated ecosystem-scale flooding experiment" (Adebayo et al., 2023). The Geophysics folder contains two subfolders containing Ground Penetrating Radar (GPR) and Electrical Resistivity Imaging (ERI) datasets acquired between August 23 and August 26, 2021. The GPR subfolder contains raw data (Tempest_Project.GPZ) and processed results using the Sensors and Software EKKO Project software. The ERI subfolder contains two additional subfolders named Static and Timelapse. The Static subfolder contains background resistivity datasets acquired before infiltration, both raw and inverted results using the ResIpy code. The Timelapse subfolder contains repeated resistivity datasets acquired during infiltration on two profiles, along with raw and inverted results using the ResIpy code. The Flow model folder contains ATS model simulation files. The Geophysics-informed subfolder contains simulation datasets for two profiles generated using information from the geophysics data. The National_database-informed subfolder contains simulation datasets for two profiles generated using information from a national soil database. Each subfolder contains initial simulation results, multiple ensembles, and final simulation files. The Sensitivity_analysis folder contains the observation files exported from the flow model, notebooks used in running the code, and sensitivity analysis results showing the sensitivity of different parameters over a 48-hour period. We used mutual information-based global sensitivity analysis for the sensitivity analysis, following Jiang et al., 2022. This study shows a novel approach that integrates geophysical data with hydrological modeling to understand subsurface flow during coastal flooding.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Small Angle Scattering Data Analysis Assisted by Machine Learning Methods

Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Data used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”

The package contains the data layers used in “Enguehard et al. 2022, Machine-Learning Functional Zonation Approach for Characterizing Terrestrial–Aquatic Interfaces: Application to Lake Erie”. Spatial data layers include: topography, wetland vegetation cover, time series of Landsat’s enhanced vegetation index (EVI) between 1990 and 2020. The study aims to characterize coastal wetlands with particular focus on the co-variability between plant dynamics, topography, soil, and other environmental factors. We proposed a functional zonation approach based on machine learning clustering to identify the spatial regions, i.e., zones that capture these co-varied properties. This approach was applied to publicly available datasets along Lake Erie, in the Great Lakes Region

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"

This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Soil Temperature and Moisture within the Kougarok Fire Complex, Kougarok Road Mile Marker 86, Seward Peninsula, Alaska, 2019-2023

Daily averages of soil temperature and moisture measured once every hour at different heights located at Intensive Monitoring Stations within the Kougarok Fire Complex, Kougarok Road Mile Marker 86 site. Data were retrieved annually from 2019-2023. Package contains 21 *.CSV data files plus a file level metadata *.CSV, data dictionary *.CSV, data file inventory *.CSV, and sensor location site map *.JPG. Data files have header rows, NaN fields indicate invalid or missing data, and negative vertical offsets are above ground.The Kougarok tundra fire complex (KFC) is located north of Nome and the Kigluaik Mountains, near Quartz Creek and the Kougarok River. The site is accessed by foot from the end of the Nome-Taylor Highway (mile marker 86; also called the Kougarok or Beam Road). The KFC burned in six major fires in the decades since 1950 (Alaska Interagency Coordination Center, unpublished data). Lightning ignited five of these fires (1971, 1997, 2015, and 2019) and one was human caused (2002). The mosaic of overlapping fire scars allows for the study of repeat fires in the tundra which, until recently, was not a common phenomenon outside the boreal forest in Alaska. Our reference unburned tundra fire site is south of the KFC located at mile marker 80 of the Nome-Taylor Highway.The two most recent fires are the Mingvk Lake (2015; 21,698 acres burned from 7/27/2015 to 9/28/2015) and Garfield Creek (2019; 422 acres burned from 7/31/2019 to 8/20/19). The Mingvk Lake fire scar includes areas that burned 1-4x (1971, 1997, 2002), while the entirety of the Garfield Creek fire scar has burned 2x previously (1971, 2002).Previous research at the KFC focused on permafrost (Liljedahl et al. 2007; Narita et al. 2015; Iwahana et al. 2016; Tsuyuzaki, Iwahana, and Saito 2017) and vegetation (Narita et al. 2015; Hollingsworth et al. 2021) response to fire. The central Seward Peninsula is characterized by continuous permafrost with a thickness of 15 to 30 m and a mean active layer thickness of 56 cm (Hinzman et al. 2003). Sloping hills with mixed shrub–tussock tundra and tussock tundra vegetation in the uplands are characteristic of the region. Three micrometeorological towers near the Kougarok field site recorded a mean annual temperature of −2.4°C, mean January temperature of −23.1°C, mean July temperature of +11°C, and mean summer rainfall (June–August) of 94 mm from 2000 to 2006 (Liljedahl et al. 2007).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 OSTI2025

Data-model files associated with the manuscript "Modeling the Effects of Wetland Restoration on Coastal Hydrology: A Case Study of Elkhorn Slough Watershed, California"

This package contains the data, simulation setups, notebooks and figures used in “Modeling the Effects of Wetland Restoration on Coastal Hydrology: A Case Study of Elkhorn Slough Watershed, California” (Xu et al., 2025). In this study, we selected Elkhorn Slough, a tidal estuary, in California, to investigate the impact of wetland restoration and sea level rise on coastal hydrology using the process-based coastal hydrologic model, Advanced Terrestrial Simulator (ATS), informed by site-specific data. We designed a novel modeling workflow for incorporating wetland restoration features into land cover and soil properties for the model parameterization. The validation results demonstrate a strong agreement between modeled and observed data. We studied the characteristics of coastal watershed hydrology, then focused on the surface water dynamics at two wetland sites within Elkhorn Slough, a reference site and a restored site. Our simulation results indicate that the restored site successfully maintains surface elevation, resulting in reduced surface inundation. We also examined the impact of wetland restoration under expected sea level rise over the next few decades. The low-lying Yampah Marsh, the reference site, is likely to be inundated due to future sea level rise when highest tides arrive; while a higher percentage of Hester Marsh, the restored site, would retain marsh vegetation in coming decades, regardless of tidal conditions. Our study provides important information for examining the outcome of restoration practices that include surface elevation in tidal wetlands under climate changes.Several files can be found from this data package.1. README.md: This file describes the title, journal, co-authors, abstract, repository structure and model version.2. Simulation_Setups.zip: The file contains the model configuration files (XML format) for ATS. 3. Notebooks.zip: The file contains the Jupyter notebooks for generating the pre- and post-restoration meshes and the meshes of future scenarios. 4. Figures.zip: The file contains the figures used in the manuscript.5. Data.zip: The file contains the data used to drive the model simulations, including watershed and wetlands boundaries, mesh files and references to additional datasets (e.g., meteorological forcing, tidal dataset, DEMs, land cover, soil properties). Also, it contains water level observations at the restored wetland.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

sdt (Solar Data Tools) [SWR-25-130]

Solar Data Tools (sdt) is an open-source Python library for analyzing PV power (and irradiance) time-series data. It was developed to enable analysis of unlabeled PV data, i.e. with no model, no meteorological data, and no performance index required, by taking a statistical signal processing approach in the algorithms used in the package’s main data processing pipeline. Solar Data Tools empowers PV system fleet owners or operators to analyze system performance a hundred times faster even when they only have access to the most basic data stream—power output of the system.

Meyers-Im, Bennet [National Laboratory of the Rock↗
DOE OSTI2021

Revisiting matrix-based inversion of scanning mobility particle sizer (SMPS) and humidified tandem differential mobility analyzer (HTDMA) data

Abstract. Tikhonov regularization is a tool for reducing noise amplification during data inversion. This work introduces RegularizationTools.jl, a general-purpose software package for applying Tikhonov regularization to data. The package implements well-established numerical algorithms and is suitable for systems of up to ∼ 1000 equations. Included is an abstraction to systematically categorize specific inversion configurations and their associated hyperparameters. A generic interface translates arbitrary linear forward models defined by a computer function into the corresponding design matrix. This obviates the need to explicitly write out and discretize the Fredholm integral equation, thus facilitating fast prototyping of new regularization schemes associated with measurement techniques. Example applications include the inversion involving data from scanning mobility particle sizers (SMPSs) and humidified tandem differential mobility analyzers (HTDMAs). Inversion of SMPS size distributions reported in this work builds upon the freely available software DifferentialMobilityAnalyzers.jl. The speed of inversion is improved by a factor of ∼ 200, now requiring between 2 and 5 ms per SMPS scan when using 120 size bins. Previously reported occasional failure to converge to a valid solution is reduced by switching from the L-curve method to generalized cross-validation as the metric to search for the optimal regularization parameter. Higher-order inversions resulting in smooth, denoised reconstructions of size distributions are now included in DifferentialMobilityAnalyzers.jl. This work also demonstrates that an SMPS-style matrix-based inversion can be applied to find the growth factor frequency distribution from raw HTDMA data while also accounting for multiply charged particles. The outcome of the aerosol-related inversion methods is showcased by inverting multi-week SMPS and HTDMA datasets from ground-based observations, including SMPS data obtained at Bodega Marine Laboratory during the CalWater 2/ACAPEX campaign and co-located SMPS and HTDMA data collected at the US Department of Energy observatory located at the Southern Great Plains site in Oklahoma, USA. Results show that the proposed approaches are suitable for unsupervised, nonparametric inversion of large-scale datasets as well as inversion in real time during data acquisition on low-cost reduced-instruction-set architectures used in single-board computers. The included software implementation of Tikhonov regularization is freely available, general, and domain-independent and thus can be applied to many other inverse problems arising in atmospheric measurement techniques and beyond.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Data, figures, animations, and scripts associated with the manuscript "Integrated Effects of Site Hydrology and Vegetation on Exchange Fluxes and Carbon Cycling at the Coastal Terrestrial Aquatic Interface"

This package contains the data, figures, animations, and scripts used in "Integrated Effects of Site Hydrology and Vegetation on Exchange Fluxes and Carbon Cycling at the Coastal Terrestrial Aquatic Interface" (Li et al., 2023). This study examines the interactions between soil, vegetation and hydrologic conditions in coastal areas, with a focus on the Chesapeake Bay region. The researchers used a mechanistic model called ATS-PFLOTRAN to explore how these interactions affect exchange process in different regions and the carbon/nitrogen cycle across the terrestrial-aquatic interface (TAI). Simulation scenarios isolate the effects of control factors and reaction constants derived from laboratory experiments. The results show a carbon cycle "hot zone" in coastal wetlands and in the transition zone between wetlands and uplands. Transpiration enhances fluxes between the surface and subsurface domains and increases dissolved oxygen in TAI. The decomposition of leaf-derived organic carbon provides an additional source of carbon for aerobic respiration and denitrification in the TAI. Microbial activity plays a key role in controlling redox conditions and their variability. This modeling study improves the understanding of complex TAI interactions and facilitates the representation of coastal ecosystems in larger-scale Earth system models.Several files can be found from this data package.1. Readme,md: This file describe the Title, Target Journal, Target submission date, Co-author, Science Questions, Hypotheses, Key words, Key message, Model and Data, Repo Structure. The user can read this file first and then go to details.2. mesh.zip: This file contains the mesh file for simulation cases. 3. Figure.zip: This file contains figures used in the manuscript . 4. animation.zip: This file contains animations used in the manuscript . 5. Data.zip: This file contains all required input data, such as concentration and flow boundaries. Also, it contains the DEM and processes results on area fraction. 6. Simulation_setup_and_results.zip: This file contains setup of cases used in the manuscript . The result files are too large, if you need that data, please contact to the Author. 7. notebooks.zip: This file contains the Jupyter notebooks for performing sensitivity analysis and other pre-process and post-process analyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Instantiation of the Damara Tern Platform for Advanced Materials and Manufacturing Technologies (AMMT) Program Collaborative Data Management

This work package focused on deploying an instance of the Damara Tern platform to support AMMT collaborative research activities. The objectives were to provide selected AMMT collaborators with access to a shared environment for capturing operations, trackables, and associated metadata, and to implement data entry functionalities that reflect site-specific procedures. Key activities included creating configurable, schema-driven entry forms and validating the data collection process. The report details the deployment process, the platform infrastructure, and the implemented data entry workflows, providing a reference for end users and establishing a foundation for future production-scale deployments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data used in manuscript "Quantifying Subsurface Flow and Solute Transport in a Snowmelt-recharged Hillslope with Multiyear Water Balance", submitted to Water Resources Research on 5-26-2022

Data in this package were used to calculate subsurface flow and solute (specifically dissolved nitrogen) transport in the Pumphouse Lower Montane (PLM) hillslope. These data are included in separate CSV files associated with each figure presented in the manuscript. The files describe site locations and elevations, weather data (air temperatures and precipitation), modeled evapotranspiration, time dependent groundwater elevations, calculated annual subsurface flow, hydraulic conductivity values, time trends in subsurface flow within different zones, pore water concentrations of total dissolve nitrogen (TDN), rates of TDN transport, soil water matric potentials following snowmelt, soil water contents during drainage, and evapotranspiration calculated by soil water mass balance versus by CLM4 model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Updates to the NuMI Flux Simulation at MicroBooNE (V.1.0)

The NuMI flux prediction being used by all experiments at Fermilab that are sensitive to the NuMI beam is based on Geant v4.9.2.03 (G4.9) and uses the default FTFP-BERT physics list with no custom cross sections. In order to correct these predictions to match world data, PPFX, the Package to Predict the FluX, is used to reweight the flux prediction. In kinematic regions of overlap between the prediction and data, mainly NA49 measurements are used to correct for various hadron production processes. Specifcally, PPFX looks at the entire ancestry chain of the neutrino, up to the incident 120 GeV proton beam, and applies corrections for various hadron interactions based on said data. These corrections are implemented as a function of the Feynman-x (x F ) and transverse momentum (p T ) of the incident hadron.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data

Mass spectrometry is a powerful tool for identifying and analyzing small molecules, such as metabolites and lipids, in com-plex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly in-volve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctua-tion, imprecise timing, column degradation and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for small molecule om-ics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package which consolidates eleven common batch effect correction methods for small molecule omics data into one place so users can easily implement and compare: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveI-CA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include batch effect cor-rection in a pmartR workflow without needing any additional data manipulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Machine learning based prediction of airflow maldistribution in air-to-refrigerant heat exchangers

Flow maldistribution is a common challenge in heat exchanger (HX) design and particularly important for air-to-refrigerant geometries where capacity losses can approach 65%. This has a major impact on central air conditioning systems, as compact duct design motivates the use of A-type HXs which are known to be affected by airflow maldistribution. Because velocity profiles are difficult to predict, components are often oversized leading to increased material cost, system footprint, and refrigerant charge. Several studies detail airflow maldistribution for individual HXs and packages, but findings cannot always be extrapolated to new designs. In this work, a machine learning (ML) based flow profile prediction framework is developed and applied to two common package configurations: (i) A-type and (ii) U-type HXs, across a broad range of HX geometries and flow rates. Porous media CFD simulations are validated against independent data for both package types as well as comprehensive in house measurements for a finless geometry with shape optimized non-round tubes, which validates the framework for new heat transfer surfaces. The ML models are trained on the porous media CFD simulations, predicting volumetric flow rate (VFR) within 1.1% and 1.9% with maximum relative L 2 norm errors of 0.48 and 0.65, respectively, while also delivering 10 5 speed up factor compared to full porous media CFD. HX level simulations show an up to 9% reduction in heat transfer from flow maldistribution, with greater losses occurring at smaller half apex angles. This framework enables rapid and highly accurate prediction of airflow maldistribution induced capacity degradation.

42 ENGINEERING↗
DOE OSTI2025

MAPS Wind Profile Data

This dataset includes 40km vertical wind profiles from late 2023 through 9/30/2024 for Oklahoma, Kansas, and the Northeastern US. The data are in text format. Key for interpreting data is included in this package. Additionally, a descriptor accompanies each data file.

54 ENVIRONMENTAL SCIENCES↗