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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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Modeling and performance data in Matlab data file (.mat) containing 3 structures (WEC model, simRes_sr and simRes_fix), and a pdf document describing the model, the simulations, and the analysis that has been carried out.
SAND2023-07249O The 2-D Surface Temperature Simulation Data and Matlab software can estimate 2D temperature fields from electrical measurements along the edge of the 2D domain. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
As researchers, we often focus more on data collection than data work-up. However, skillfully plotting data so that it can be effectively visualized is often an important step toward making it impactful. Data visualization in academic writing takes the form of plots, figures, graphs, spectra, etc., which are often combined with cartoons and schematics. An important goal of data visualization is to make complex data and concepts more accessible, understandable, and usable, and to do this in a concise manner – ‘a picture is worth a thousand words’ as the old cliché goes. Data visualization is both art and science, where, of course, data visualization depends on the type of data in question. Herein, we discuss ways of representing/plotting data using MATLAB from the MathWorks ® .
The graphical representation of numerical data plays a vital role in chemistry, materials science, and every other scientific field. No matter how good your results are, poorly represented data can ultimately make your work confusing. Indeed, with an increase in the amount of data that is being gathered, e.g., by the Materials Genome Initiative, or in X-ray photoelectron spectroscopy (XPS) or time-of-flight secondary ion mass spectrometry (ToF-SIMS) depth profiles, the importance of effectively representing data has increased. Good figures also matter because many researchers will not look at more than the title, abstract, and perhaps a few key figures from a paper.
Contains raw data for operations of Ocean Renewable Power Company (ORPC) RivGen Power System in Igiugig 2015 in Matlab data file format. Two data files capture the data and timestamps for data, including power in, voltage, rotation rate, and velocity. Location, Description, Format, Column 1, Date and time, MM/DD/YYYY, hh:mm:ss, Column 2, DC voltage (after DC filter), VDC, Column 3, DC current into inverters, IDC, Column 4, DC current into secondary resistive load, IDC, Column 5, Power into inverters, kW, Column 6, Power into secondary load, kW, Column 7, Frequency of single phase AC voltage from generator, Hz, Column 8, rotation rate of generator/turbine (rested at 1.1 when stationary), rad/s, Column 9, Velocity Algorithm 2, m/s, Column 10, Velocity Algorithm 3, m/s, Column 11, Rcodes for secondary load, see document "SL Controller PLC Memory", Column 12, Voltage measured between phase A and B, Vll,
The accompanying file include information regarding the set up of WRF simulations of power production and wake extents from offshore wind lease areas along the U.S. east coast, and also data presented in figures in the "Wind power production from the U.S. east coast offshore lease areas" paper and the associated MATLAB data processing code.
This submission of Utilization Analysis data to the Geothermal Data Repository (GDR) node of the National Geothermal Data System (NGDS) is in support of Phase 1 Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin (project DE-EE0006726). The submission includes data pertinent to the methods and results of an analysis of the Surface Levelized Cost of Heat (SLCOH) for US Census Bureau ‘Places’ within the study area. This was calculated using a modification of a program called GEOPHIRES, available at http://koenraadbeckers.net/geophires/index.php. The MATLAB modules used in conjunction with GEOPHIRES, the MATLAB data input file, the GEOPHIRES output data file, and an explanation of the software components have been provided. Results of the SLCOH analysis appear on 4 .png image files as mapped ‘risk’ of heat utilization. For each of the 4 image (.png) files, there is an accompanying georeferenced TIF (.tif) file by the same name. In addition to calculating SLCOH, this Task 4 also identified many sites that may be prospects for use of a geothermal district heating system, based on their size and industry, rather than on the SLCOH. An industry sorted listing of the sites (.xlsx) and a map of these sites plotted as a layer onto different iterations of maps combining the three geological risk factors (Thermal Quality, Natural Reservoir Quality, and Risk of Seismicity) has been provided. In addition to the 6 image (.png) files of the maps in this series, a shape (.shp) file and 7 associated files are included as well. Finally, supporting files (.pdf) describing the utilization analysis methodology and summarizing the anticipated permitting for a deep district heating system are supplied.
This submission of Utilization Analysis data to the Geothermal Data Repository (GDR) node of the National Geothermal Data System (NGDS) is in support of Phase 1 Low Temperature Geothermal Play Fairway Analysis for the Appalachian Basin. The submission includes data pertinent to the methods and results of an analysis of the Surface Levelized Cost of Heat (SLCOH) for US Census Bureau Places within the study area. This was calculated using a modification of a program called GEOPHIRES, available at http://koenraadbeckers.net/geophires/index.php. The MATLAB modules used in conjunction with GEOPHIRES, the MATLAB data input file, the GEOPHIRES output data file, and an explanation of the software components have been provided. Results of the SLCOH analysis appear on 4 .png image files as mapped risk of heat utilization. For each of the 4 image (.png) files, there is an accompanying georeferenced TIF (.tif) file by the same name. In addition to calculating SLCOH, this Task 4 also identified many sites that may be prospects for use of a geothermal district heating system, based on their size and industry, rather than on the SLCOH. An industry sorted listing of the sites (.xlsx) and a map of these sites plotted as a layer onto different iterations of maps combining the three geological risk factors (Thermal Quality, Natural Reservoir Quality, and Risk of Seismicity) has been provided. In addition to the 6 image (.png) files of the maps in this series, a shape (.shp) file and 7 associated files are included as well. Finally, supporting files (.pdf) describing the utilization analysis methodology and summarizing the anticipated permitting for a deep district heating system are supplied. UPDATE: Newer version of the Utilization Analysis has been added here: https://gdr.openei.org/submissions/878
Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported to R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.
Nanoindentation is a tool that allows the mechanical response of a variety of materials at the nano to micron length scale to be measured. Recent advances in spherical nanoindentation techniques have allowed for a more reliable and meaningful characterization of the mechanical response from nanoindentation experiments in the form on an indentation stress-strain curve. This code base, Spin, is written in MATLAB (The Mathworks, Inc.) and based on the analysis protocols developed by S.R. Kalidindi and S. Pathak [1, 2]. The inputs include the displacement, load, harmonic contact stiffness, harmonic displacement, and harmonic load from spherical nanoindentation tests in the form of an Excel (Microsoft) spreadsheet. The outputs include indentation stress-strain curves and indentation properties as well their variance due to the uncertainty of the zero-point correction in the form of MATLAB data (.mat) and figures (.png). [1] S. Pathak, S.R. Kalidindi. Spherical nanoindentation stress–strain curves, Mater. Sci. Eng R-Rep 91 (2015). [2] S.R. Kalidindi, S. Pathak. Determination of the effective zero-point and the extraction of spherical nanoindentation stress-strain curves, Acta Materialia 56 (2008) 3523-3532.
Nanoindentation is a tool that allows the mechanical response of a variety of materials at the nano to micron length scale to be measured. Recent advances in spherical nanoindentation techniques have allowed for a more reliable and meaningful characterization of the mechanical response from nanoindentation experiments in the form on an indentation stress-strain curve. This code base, Spin, is written in MATLAB (The Mathworks, Inc.) and based on the analysis protocols developed by S.R. Kalidindi and S. Pathak [1, 2]. The inputs include the displacement, load, harmonic contact stiffness, harmonic displacement, and harmonic load from spherical nanoindentation tests in the form of an Excel (Microsoft) spreadsheet. The outputs include indentation stress-strain curves and indentation properties as well their variance due to the uncertainty of the zero-point correction in the form of MATLAB data (.mat) and figures (.png). [1] S. Pathak, S.R. Kalidindi. Spherical nanoindentation stress–strain curves, Mater. Sci. Eng R-Rep 91 (2015). [2] S.R. Kalidindi, S. Pathak. Determination of the effective zero-point and the extraction of spherical nanoindentation stress-strain curves, Acta Materialia 56 (2008) 3523-3532.
The MATLAB (.mat) files (spatial grids of daily CDD data) are generated by a suit of WRF-UCM simulations over California at 4.5 km resolution (1.5 km resolution over cities) for 5 scenarios
The repository contains underwater noise measurements and associated metadata collected around C-Power's SeaRay wave energy converter on July 15, 2024 and July 16, 2024 while it was deployed at the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Measurements were obtained using Drifting Acoustic Instrumentation SYstems (DAISYs). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is a compressed archive (.zip format), containing multiple MATLAB binary data files (.mat format). The structure of each file is included in the repository as a Word document (Data Description MHK-DR.docx). Each file contains time series information for a single DAISY deployment (file naming convention: WETS_DAISY_[Drift #].mat) consisting of processed hydrophone data and associated metadata. During these measurements, C-Power's SeaRay was located at approximately 21.48112 N, 157.74451 W.
These are slides about extracting the data from the LCM vk4 formatted output file. The following is covered: vk4 file produced by Keyence VK Software, custom analysis, no off the shelf way to read the file, reading the binary data in a vk4 file, various offsets in decimal lines, finding the height image data, directly in MATLAB, binary output beginning of height image data, color image information, color image binary data, color image decimal and binary data, MATLAB code to read vk4 file (choose a file, read the file, compute offsets, read optical image, laser optical image, read and compute laser intensity image, read height image, timing, display height image, display laser intensity image, display RGB laser optical images, display RGB optical images, display beginning data and save images to workspace, gamma correction subroutine), reading intensity form the vk4 file, linear in the low range, linear in the high range, gamma correction for vk4 files, computing the gamma intensity correction, observations.
Matlab scripts and functions and data used to build Poly3D models and create permeability potential layers for 1) St. Helens Shear Zone, 2) Wind River Valley, and 3) Mount Baker geothermal prospect areas located in Washington state.
The soil thickness maps were created by using a hybrid model-data approach. Field sampling and remote sensing data of the spatial distribution of two hillslopes in the Pump House area in the East River Watershed in the CO., the U.S. The data package includes the geospatial data of the soil thickness maps at two hillslopes near the pumphouse, and the associated remote sensing data, including lidar DEM and a shape file of the boundary of the study area. The data can be viewed in GIS software such as QGIS or ArcGIS desktop. The geospatial data can also be viewed in Python or Matlab. The data were generated for the purpose of modeling surface hydrology and near-surface chemistry. This work shows how to combine sampling data and a process-based model to predict one of the highest uncertainty in the land surface process, the soil thickness.
This data set presents results testing the station keeping abilities of a tethered Seabotix vLBV300 underwater vehicle equipped with an inertial navigation system. These results are from an offshore deployment on April 20, 2016 off the coast of Newport, OR (44.678 degrees N, 124.109 degrees W). During the mission period, the sea state varied between 3 and 4, with an average significant wave height of 1.6 m. The vehicle utilizes an inertial navigation system based on a Gladiator Landmark 40 IMU coupled with a Teledyne Explorer Doppler Velocity Log to perform station keeping at a desired location and orientation.
This repository contains the underlying data from benchmark experiments for Drifting Acoustic Instrumentation SYstems (DAISYs) in waves and currents described in "Performance of a Drifting Acoustic Instrumentation SYstem (DAISY) for Characterizing Radiated Noise from Marine Energy Converters" (https://link.springer.com/article/10.1007/s40722-024-00358-6). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is three compressed archives (.zip format), each containing multiple MATLAB binary data files (.mat format). A table relating individual data files to figures in the paper, as well as the structure of each file, is included in the repository as a Word document (Data Description MHK-DR.docx). Most of the files contain time series information for a single DAISY deployment (file naming convention: [site]_DAISY_[Drift #].mat) consisting of processed hydrophone data and associated metadata. For a limited number of DAISY deployments, the hydrophone package was replaced with an acoustic Doppler velocimeter (file naming convention: [site]_DAISY_[Drift #]_ADV.mat). Data were collected over several years at three locations: (1) Sequim Bay at Pacific Northwest National Laboratory's Marine & Coastal Research Laboratory (MCRL) in Sequim, WA, the energetic tidal channel in Admiralty Inlet, WA (Admiralty Inlet), and the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Brief descriptions of data files at each location follow. - MCRL - (1) Drift #4 and #16 contrast the performance of a DAISY and a reference hydrophone (icListen HF Reson), respectively, in the quiescent interior of Sequim Bay (September 2020). (2) Drift #152 and #153 are velocity measurements for a drifting acoustic Doppler velocimeter in in the tidally-energetic entrance channel inside a flow shield and exposed to the flow, respectively (January 2018). (3) Two non-standard files are also included: DAISY_data.mat corresponds to a subset of a DAISY drift over an Adaptable Monitoring Package (AMP) and AMP_data.mat corresponds to approximately co-temporal data for a stationary hydrophone on the AMP (February 2019). - Admiralty Inlet - (1) Drift #1-12 correspond to tests with flow shielded DAISYs, unshielded DAISYs, a reference hydrophone, and drifting acoustic Doppler velocimeter with 5, 10, and 15 m tether lengths between surface expression and hydrophone recording package (July 2022). (2) Drift #13-20 correspond to tests of flow shielded DAISYs with three different tether materials (rubber cord, nylon line, and faired nylon line) in lengths of 5, 10, and 15 m (July 2022). - WETS - (1) Drift #30-32 correspond to tests with a heave plate incorporated into the tether (standard configuration for wave sites), rubber cord only, and rubber cord, but with a flow shielded hydrophone (November 2022). (2) Drift #49-58 and Drift #65-68 correspond to measurements around mooring infrastructure at the 60 m berth where time-delay-of-arrival localization was demonstrated for different DAISY arrangements and hydrophone depths (November 2022).