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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

pyXPCSviewer : an open-source interactive tool for X-ray photon correlation spectroscopy visualization and analysis

pyXPCSviewer , a Python-based graphical user interface that is deployed at beamline 8-ID-I of the Advanced Photon Source for interactive visualization of XPCS results, is introduced. pyXPCSviewer parses rich X-ray photon correlation spectroscopy (XPCS) results into independent PyQt widgets that are both interactive and easy to maintain. pyXPCSviewer is open-source and is open to customization by the XPCS community for ingestion of diversified data structures and inclusion of novel XPCS techniques, both of which are growing demands particularly with the dawn of near-diffraction-limited synchrotron sources and their dedicated XPCS beamlines.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

PvaPy streaming framework for real-time data processing

User facility upgrades, new measurement techniques, advances in data analysis algorithms as well as advances in detector capabilities result in an increasing amount of data collected at X-ray beamlines. Some of these data must be analyzed and reconstructed on demand to help execute experiments dynamically and modify them in real time. In turn, this requires a computing framework for real-time processing capable of moving data quickly from the detector to local or remote computing resources, processing data, and returning results to users. In this paper, we discuss the streaming framework built on top of PvaPy, a Python API for the EPICS pvAccess protocol. We describe the framework architecture and capabilities, and discuss scientific use cases and applications that benefit from streaming workflows implemented on top of this framework. We also illustrate the framework's performance in terms of achievable data-processing rates for various detector image sizes.

EPICS pvAccess↗
DOE OSTI2021

Challenges and Solutions for Real-Time Phasor Modeling of Large-scale Distribution Network with High PV Penetration

The conversion process of a practical large-scale feeder data from a quasi-static time series (OpenDSS) model into a real-time phasor model (ePHASORSIM in Opal-RT) is discussed in the paper. The process is implemented using an open source Python software. Previous reported implementations for the conversion process lead to several errors when applied to a larger-scale system such as the one considered here. Hence in this work, we describe the common issues in this conversion and propose a customized solution to enhance the efficiency of the conversion and reduce the complexity in the process. A quantitative validation of the enhanced conversion process is presented in this work using an actual high PV penetration feeder model that consists of 2230 buses, and using actual load and PV profile data. After a detailed analysis, this customized conversion software will be made available as an open source tool and is expected to be helpful for researchers who want to pursue a similar conversion. Solutions to various observed issues such as identifying the lines due to islanded network, representation of full impedance model of transformer/lines as sequential models, complexity in the representation of single phase buses/lines as three phase buses/lines to make it compatible with the simulator platform are discussed. Comparison of power flow and time series simulation results obtained from both OpenDSS and ePHASORsim models show very low errors, validating the accuracy of the proposed conversion process.

14 SOLAR ENERGY↗
DOE OSTI2021

libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations

Almost all applications stop scaling at some point; those that don't are seldom performant when considering time to solution on anything but aspirational/unicorn resources. Recognizing these tradeoffs as well as greater user functionality in a near-term exascale computing era, we present libEnsemble, a library aimed at particular scalability- and capability-stretching uses. libEnsemble enables running concurrent instances of an application in dynamically allocated ensembles through an extensible Python library. Here, we highlight the structure, execution, and capabilities of the library on leading pre-exascale environments as well as advanced capabilities for exascale environments and beyond.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A software package for plasma facing component analysis and design: the Heat flux Engineering Analysis Toolkit (HEAT)

The engineering limits of plasma facing components (PFCs) constrain the allowable operational space of tokamaks. Poorly managed heat fluxes that push the PFCs beyond their limits not only degrade core plasma performance via elevated impurities, but can also result in PFC failure due to thermal stresses or melting. Simple axisymmetric assumptions fail to capture the complex interaction between 3D PFC geometry and 2D or 3D plasmas. This results in fusion systems that must either operate with increased risk or reduce PFC loads, potentially through lower core plasma performance, to maintain a nominal safety factor. High precision 3D heat flux predictions are necessary to accurately ascertain the state of a PFC given the evolution of the magnetic equilibrium. A new code, the Heat flux Engineering Analysis Toolkit (HEAT), has been developed to provide high precision 3D predictions and analysis for PFCs. HEAT couples many otherwise disparate computational tools together into a single open source python package. Magnetic equilibrium, engineering CAD, finite volume solvers, scrape off layer plasma physics, visualization, high performace computing, and more, are connected in a single web-based user interface. Linux users may use HEAT without any software prerequisites via an appImage. This manuscript introduces HEAT, discusses the software architecture, presents first HEAT results, and outlines physics modules in development.

divertor physics↗
DOE OSTI2020

CYPminer: an automated cytochrome P450 identification, classification, and data analysis tool for genome data sets across kingdoms

Background: Cytochrome P450 monooxygenases (termed CYPs or P450s) are hemoproteins ubiquitously found across all kingdoms, playing a central role in intracellular metabolism, especially in metabolism of drugs and xenobiotics. The explosive growth of genome sequencing brings a new set of challenges and issues for researchers, such as a systematic investigation of CYPs across all kingdoms in terms of identification, classification, and pan-CYPome analyses. Such investigation requires an automated tool that can handle an enormous amount of sequencing data in a timely manner. Results: CYPminer was developed in the Python language to facilitate rapid, comprehensive analysis of CYPs from genomes of all kingdoms. CYPminer consists of two procedures i) to generate the Genome-CYP Matrix (GCM) that lists all occurrences of CYPs across the genomes, and ii) to perform analyses and visualization of the GCM, including pan-CYPomes (pan- and core-CYPome), CYP co-occurrence networks, CYP clouds, and genome clustering data. The performance of CYPminer was evaluated with three datasets from fungal and bacterial genome sequences. Conclusions: CYPminer completes CYP analyses for large-scale genomes from all kingdoms, which allows systematic genome annotation and comparative insights for CYPs. CYPminer also can be extended and adapted easily for broader usage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Validation of MCNP Critical Benchmark Models of Moderated Highly Enriched Uranium Slabs

A new centralized repository of high-quality MCNP models of critical benchmark experiments is currently under development at Los Alamos National Laboratory (LANL). The benchmark experiments are described in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook, and the initial set of benchmark models are derived from the Whisper Suite provided with MCNP6.2. This effort is a collaboration among the Nuclear Criticality Safety, Nuclear Data, and Monte Carlo code development/application organizations at LANL. The goal is to build a single LANL benchmark collection that is up to date with the latest ICSBEP revision, has a formal review and revision process, is contained in an open-source repository, and utilizes new Python tools for improved input and output file review. This paper describes the validation of the models associated with HEU-MET-FAST-007, “Uranium Metal Slabs Moderated with Polyethylene, Plexiglas, and Teflon”. The Monte Carlo n-Particle (MCNP) models were compared to the second revision of HEU-MET-FAST-007. The experiment considered critical configurations of highly enriched uranium (HEU) metal slabs and various moderators in 43 unique cases. The slabs of uranium were separated by layers of moderating material, forming an assembly. The assembly was separated in two halves with one half placed on a stationary table and the other attached to a moveable table that could be raised and lowered via pulleys connected to the ceiling of the shielded room. To reach criticality, the two halves were brought together with a negligible gap between the assemblies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Validation of MCNP Critical Benchmark Models of PU-MET-FAST-016

A new centralized repository of high-quality MCNP models of critical benchmark experiments is currently under development at Los Alamos National Laboratory (LANL). The repository is the combined effort of the Nuclear Criticality Safety, Nuclear Data, and Monte Carlo code development/application organizations at LANL. The initial set of benchmark models in the repository are derived from the Whisper Suite provided with MCNP6.2 and are evaluated against the benchmark experiments described in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) Handbook. The goal is to build a single LANL benchmark collection that is up to date with the latest ICSBEP revision, has a formal review and revision process, is contained in an open-source repository, and utilizes new Python tools for improved input and output file review. This paper describes the validation of the models associated with PU-MET-FAST-016, “Flooded 3X3X3 Arrays of 3-kg Plutonium (Pu) Metal Cylinders – Phase I”. In this critical experiment, twenty-seven 3-kg Pu metal cylinders with a height of 46.33 ± 0.15 mm and diameter of 65.25 ± 0.05 mm were arranged in a cubic array. The Pu cylinders were sealed in aluminum cans, and three cans were placed in each of nine perforated aluminum sleeves, as shown in Fig. 1. The nine sleeves were supported by an aluminum frame that allowed for control of the lateral spacing of the cylinders, both in the X and Y directions. The benchmark experiment measured thirteen configurations. Of these configurations, six were modeled using MCNP.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

EGS Collab Experiment 1: Circulation Testing Processed data

This submission includes processed and reduced data for circulation testing that was conducted at the 164' fracture on the 4850 ft level of the Sanford Underground Research Facility. The circulation tests were done to test the flow through the 164' fracture in the EGS Collab Experiment 1 testbed on the 4850 ft level of the Sanford Underground Research Facility in order to validate computer models concerned with flow processes and heat exchange processes in EGS. This data was processed from raw data that is available in the GDR submission linked below: "EGS Collab Circulation Testing Raw Data and Documentation." Python scripts used to process and reduce the data are included. The scripts also serve as "documentation" as well as examples for data processing. More information about the test itself as well as the rationale and process of data processing is available at wiki page linked below.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to advance geothermal exploration and safe geothermal energy production. As part of the project, this submission provides data arrays for 149 microearthquakes between the year 2012 and 2013 at the Newberry EGS Site for use with the Deep Learning Algorithm that has been developed. The data provided includes raw waveform data, location data, normalized waveform data, and processed waveform data. Penn State Geothermal Team has shared the following files from the project: - 149 microearthquakes (MEQs) between 2012 and 2013 at Newberry EGS sites, 'Normalized Waveform Inputs.npz' are normalized waveforms. - labels of 149 MEQs: Processed Waveform Inputs.npz - location labels of 149 MEQs: Location Data.npz Note: .npz is the python file format by NumPy that provides storage of array data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Imperial Valley Dark Fiber Project Continuous DAS Data

The Imperial Valley Dark Fiber Project acquired Distributed Acoustic Sensing (DAS) seismic data on a ~28 km segment of dark fiber between the cities of Calipatria and Imperial in the Imperial Valley, Southern California. Dark fiber refers to unused optical fiber cables in telecommunications networks and is repurposed in this project for DAS applications. The objective, which is further detailed in the attached journal article from Ajo-Franklin et al., is to demonstrate dark fiber DAS as a tool for basin-scale geothermal exploration and monitoring. The included DAS data were recorded during two days at the beginning the project. Data is stored in the .h5 (HDF5) file format, readable using various software tools, including the 'h5read' and 'h5info' functions in Matlab. Provided here are examples of methods to read and use the data with the 'h5py' package in Python.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Utah FORGE: Composite 3D Seismic Velocity Model

This is a composite 3D seismic velocity that was constructed from compiled information from several local studies regarding seismic velocities and structural information. This seismic velocity model is provided in NonLinLoc format (slow_len), which is readily usable in NonLinLoc software. Other model formats and versions of the model can be produced using the Python script provided with this data set. Details on how the model was created and prior velocity and structural information was used is provided in the accompanying documentation.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Renewable Energy Potential Model: Priority Geothermal Leasing Areas ReEDs Results

This dataset contains the results of a study conducted by the National Renewable Energy Laboratory (NREL) to identify potential future priority geothermal leasing areas on Bureau of Land Management (BLM) and United States Forest Service (USFS) lands. The analysis uses the Regional Energy Deployment System (ReEDS) model to evaluate geothermal resource potential under different scenarios of resource depth and technology combinations through the year 2050. The study considers geothermal resource potential, natural resource conflicts, and transmission access to categorize areas into near, mid, and far deployment priorities. The dataset includes outputs from the ReEDS model, such as geothermal capacity, generation, system costs, and emissions under various economic and technical scenarios. Favorability site data with geographic coordinates and site-specific attributes (e.g., resource favorability, land type) are also provided. Supporting resources include a technical report detailing methodologies and assumptions, along with a link to the ReEDS model GitHub repository, which requires GAMS and Python software for execution.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

HERO WEC V1 Upgrade - 2023 Laboratory Testing (processed data)

The following submission includes processed laboratory data from NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of MATLAB workspaces. This dataset was created using NREL's Large Amplitude Motion Platform (LAMP) and collected between August and September 2023. Included with this submission is a test log of all the processed data "HERO WEC LAMP test run log.xlsx" so that the user can easily find the data of interest. Additionally, more detailed descriptions of the type of data and how it was processed, or calculated, can be found in the document titled "Lamp Data Description.docx". The MATLAB workspaces can be visualized using the file "LAMP_Data_Viewer_Ver2.m/mlx". The user simply needs to upload the workspace of interest and run the file "LAMP_Data_Viewer_Ver2.m/mlx". Both the .m and .mlx file format has been provided depending on the user's preference. The MATLAB workspaces have been separated into zip files corresponding to either Drivetrain, Hydraulic, or Electric configuration runs representing the respective test cases that were run. The drivetrain runs were used to characterize the drivetrain only (no pump or generator). The Hydraulic runs represent the configuration when the seawater pump is installed, and the Electric runs represents the configuration when the generator is installed. The following sub-categories of data are included for each type: - DW - Deep water sine wave profile (not run in drivetrain configuration) - Heave - Heave only sine wave profile - Heave_NoRO (hydraulic configuration only) - Heave_ACC (hydraulic configuration only) - IR - Surge and heave irregular wave profile (not run in drivetrain configuration) - RW - Heave only profile created from real world encoder data (not run in drivetrain configuration) For those interested in the raw, unprocessed, data the authors have created a separate submission, linked below. This submission includes the raw TDMS files and associated files necessary to translate the data into either python or MATLAB formats. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Wave Measurements taken NW of Culebra Is., PR, 2023

Wave and sea surface temperature measurements collected by a Sofar Spotter buoy in 2023. The buoy was deployed on July 27, 2023 at 11:30 UTC northwest of Culebra Island, Puerto Rico, (18.3878 N, 65.3899 W) and recovered on Nov 5, 2023 at 12:45 UTC. Data are saved here in netCDF format, organized by month, and include directional wave statistics, GPS, and SST measurements at 30-minute intervals. Figures produced from these data are provided here as well. They include timeseries of wave height/period/direction and SST, GPS location, wave roses, and directional spectra. Additionally, raw CSV files from the Spotter's memory card can also be found below. NetCDF files can be read in python using the netCDF4 or Xarray packages, or through MATLAB using the "ncread()" command.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

HERO WEC 2024 - Electrical Configuration Deployment Data

The following submission includes raw and processed electrical configuration deployment data from the in water deployment of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), in the form of parquet files, TDMS files, CSV files, bag files, and MATLAB workspaces. This dataset was collected in April 2024 at the Jennette's pier test site in North Carolina. Raw data as TDMS, CSV, and bag files are provided here alongside processed data in the form of MATLAB workspaces and Parquet files. This dataset includes the Python code used to process the data and MATLAB scripts to visualize the processed data. All data types, calculations, and processing is described in the included "Data Descriptions" document. All files in this dataset are described in detail in the included README. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

TEAMER: Pitching Foil Crossflow Turbine Efficiency Data

This dataset documents the efficiency testing of a pitching foil crossflow turbine, conducted at the University of New Hampshire's (UNH) Chase Ocean Engineering Laboratory tow tank facility. The tests explored various pitch phases and amplitudes, ranging from 0 to 18 degrees, across different flow speeds and turbine RPMs. Specifically, testing was performed at pitch amplitudes of 0,3,6,9,12,15 and 18 degrees. The results indicate that turbine efficiency improves with increased pitch amplitude, reaching optimal performance at 12 degrees. Modifications were made to the test frame by UNH to enhance the stiffness of the support structure. The testing specifically mapped the efficiency of a single quadrant of the Ocean Renewable Power Company (ORPC) Autonomous Turbine Generator Unit (ATGU), covering the full range of allowable pitching amplitudes and phases. The dataset provides both raw and processed data, including drag and torque measurements, and contains Python scripts used for data processing and visualization. This research was supported by funding from TEAMER RFTS 1 (Request for Technical Support).

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

TEAMER: Crossflow Turbine Fairing Geometry Optimization - Report and CFD Modeling Files

The dataset includes computational fluid dynamics (CFD) models and simulation files for crossflow turbines as well as a detailed project report. The report documents the project undertaken by the Ocean Renewable Power Company (ORPC) to design and optimize a modular fairing for the Modular RivGen Marine Hydrokinetic (MHK) turbine, which enhances the efficient deployment and operation of turbine arrays. The project focused on optimizing the hydrodynamic performance of the fairing using CFD, with an emphasis on two key geometric parameters: the fairing's cross-sectional shape and the spacing between the rotor and the fairing. The analysis aimed to maximize net power output while also assessing discretized loading to evaluate ultimate and fatigue loads on the turbine components. The numerical modeling was conducted using both the commercial CFD software Star-CCM+ and the open-source code openFOAM, with the latter utilizing the actuator line library, turbinesFOAM. This dual-code approach was intended to increase confidence in the results and demonstrate the viability of using open-source tools for high-fidelity marine energy modeling. This dataset includes all necessary files for actuator line simulations in openFOAM, as well as 2D blade-resolved CFD results, along with Python and Java scripts for setting up and post-processing simulations.

16 TIDAL AND WAVE POWER↗