Search NASA⌕ Search

SEARCH · Search NASA

Results for “Matlab data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Echo

This document is a white paper marketing proposal for Echo™ is a data analysis platform designed for efficient, robust, and scalable creation and execution of complex workflows. Echo’s analysis management system refers to the ability to track, understand, and reproduce workflows used for arriving at results and decisions. Echo improves on traditional scripted data analysis in MATLAB, Python, R, and other languages to allow analysts to make better use of their time. Additionally, the Echo platform provides a powerful data management and curation solution allowing analysts to quickly find, access, and consume datasets. After two years of development and a first release in early 2016, Echo is now available for use with many data types in a wide range of application domains. Echo provides tools that allow users to focus on data analysis and decisions with confidence that results are reported accurately.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Kepler Mission's Focal Plane Characterization Models Implementation

The Kepler Mission photometer is an unusually complex array of CCDs. A large number of time-varying instrumental and systemic effects must be modeled and removed from the Kepler pixel data to produce light curves of sufficiently high quality for the mission to be successful in its planet-finding objective. After the launch of the spacecraft, many of these effects are difficult to remeasure frequently, and various interpolations over a small number of sample measurements must be used to determine the correct value of a given effect at different points in time. A library of software modules, called Focal Plane Characterization (FC) Models, is the element of the Kepler Science Data Pipeline (hereafter "pipeline") that handles this. FC, or products generated by FC, are used by nearly every element of the SOC processing chain. FC includes Java components: database persistence classes, operations classes, model classes, and data importers; and MATLAB code: model classes, interpolation methods, and wrapper functions. These classes, their interactions, and the database tables they represent, are discussed. This paper describes how these data and the FC software work together to provide the pipeline with the correct values to remove non-photometric effects caused by the photometer and its electronics from the Kepler light curves. The interpolation mathematics is reviewed, as well as the special case of the sky-to-pixel,pixel-to-sky coordinate transformation code, which incorporates a compound model that is unique in the SOC software.

mission↗
NASA NTRS2002

NASA Scientific Data Purchase Project: From Collection to User

NASA's Scientific Data Purchase (SDP) project is currently a $70 million operation managed by the Earth Science Applications Directorate at Stennis Space Center. The SDP project was developed in 1997 to purchase scientific data from commercial sources for distribution to NASA Earth science researchers. Our current data holdings include 8TB of remote sensing imagery consisting of 18 products from 4 companies. Our anticipated data volume is 60 TB by 2004, and we will be receiving new data products from several additional companies. Our current system capacity is 24 TB, expandable to 89 TB. Operations include tasking of new data collections, archive ordering, shipment verification, data validation, distribution, metrics, finances, customer feedback, and technical support. The program has been included in the Stennis Space Center Commercial Remote Sensing ISO 9001 registration since its inception. Our operational system includes automatic quality control checks on data received (with MatLab analysis); internally developed, custom Web-based interfaces that tie into commercial-off-the-shelf software; and an integrated relational database that links and tracks all data through operations. We've distributed nearly 1500 datasets, and almost 18,000 data files have been downloaded from our public web site; on a 10-point scale, our customer satisfaction index is 8.32 at a 23% response level. More information about the SDP is available on our Web site.

Nicholson, Lamar↗
DOE OSTI2021

All Optical Neural Networks for Low Power Edge Computing

We developed a simplistic physics-based model of an all-optical neural network that mimics the encoder part of an autoencoder neural network for image compression. Our approach relies on the generation of a MATLAB-based model for both data compression and decompression and utilizes MATLAB's built-in autoencoder networks in combination with simple propagation of optical fields between layers constituting phase elements via Fourier transform. We optimize the phase elements using the particle swarm optimization technique and using our model, we demonstrate a compression ratio of 25% for 2828-pixel input images containing numeric digits from 0 to 9.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1998

Versatile Software Package For Near Real-Time Analysis of Experimental Data

This paper provides an overview of a versatile software package developed for time- and frequency-domain analyses of experimental wind-tunnel data. This package, originally developed for analyzing data in the NASA Langley Transonic Dynamics Tunnel (TDT), is applicable for analyzing any time-domain data. A Matlab-based software package, TDT-analyzer, provides a compendium of commonly-required dynamic analysis functions in a user-friendly interactive and batch processing environment. TDT-analyzer has been used extensively to provide on-line near real-time and post-test examination and reduction of measured data acquired during wind tunnel tests of aeroelastically-scaled models of aircraft and rotorcraft as well as a flight test of the NASA High Alpha Research Vehicle (HARV) F-18. The package provides near real-time results in an informative and timely manner far exceeding prior methods of data reduction at the TDT.

Wieseman, Carol D.↗
DOE OSTI2022

Perovskite- and Dye-Sensitized Solar-Cell Device Databases Auto-generated Using ChemDataExtractor

The number of scientific publications reporting cutting-edge third-generation photovoltaic devices is increasing rapidly, owing to the pressing need to develop renewable-energy technologies that address the climate-change crisis. Consequently, the field could benefit from a central repository where photovoltaic-performance metrics, such as the power-conversion efficiency (η) are recorded. We present two automatically generated databases that contain photovoltaic properties and device material data for dye-sensitized solar cells (DSCs) and perovskite solar cells (PSCs), totalling 660,881 data entries representing 57,678 photovoltaic devices. The databases were generated by applying the text-mining toolkit ChemDataExtractor on a corpus of 25,720 articles. A multi-faceted evaluation, incorporating manual and automatic methods, was applied to ensure that the data contained therein were of the highest quality, with precision metrics ranging from 73.1% to 95.8%. The DSC database contains 475,045 entries representing 41,680 devices, and the PSC database contains 185,836 entries representing 15,818 devices. The databases are available in MongoDB and JSON formats, which can be queried in Python, R, Java and MATLAB for data-driven photovoltaic materials discovery.

14 SOLAR ENERGY↗
NASA NTRS2015

PISCES High Contrast Integral Field Spectrograph Simulations and Data Reduction Pipeline

The PISCES (Prototype Imaging Spectrograph for Coronagraphic Exoplanet Studies) is a lenslet array based integral field spectrograph (IFS) designed to advance the technology readiness of the WFIRST (Wide Field Infrared Survey Telescope)-AFTA (Astrophysics Focused Telescope Assets) high contrast Coronagraph Instrument. We present the end to end optical simulator and plans for the data reduction pipeline (DRP). The optical simulator was created with a combination of the IDL (Interactive Data Language)-based PROPER (optical propagation) library and Zemax (a MatLab script), while the data reduction pipeline is a modified version of the Gemini Planet Imager's (GPI) IDL pipeline. The simulations of the propagation of light through the instrument are based on Fourier transform algorithms. The DRP enables transformation of the PISCES IFS data to calibrated spectral data cubes.

The PISCES (Prototype Imaging Spectrograph for Cor↗
DOE OSTI2021

Load Deflection Data (LDDATA)

The current data set is load deflection data (Stress vs strain for compression stress and stress vs elongation for tension stress) from a material test. The goal of the test was to characterize this rubber-like material by looking at different variables: sample creation process, anisotropy, temperature, etc. The data will be used with the Texas A&M Online Research Experience for Undergraduates (O-REU) program. This program is joint with LANL. The student is not an official LANL student so they will not be able to access data and documents on the yellow. The load deflection data set is unclassified and will be sent to the student for analysis. Analysis will include curve fitting, scalar value analysis (stiffness, toughness, etc.) and analysis of variance to determine what variables statistically affect the response while correcting for covariates (temperature soak time and humidity). Data will be stored as Matlab structs and as HDF5 files. Same data, different formats.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Mid-frequency Band Dynamics of Large Space Structures

High and low intensity dynamic environments experienced by a spacecraft during launch and on-orbit operations, respectively, induce structural loads and motions, which are difficult to reliably predict. Structural dynamics in low- and mid-frequency bands are sensitive to component interface uncertainty and non-linearity as evidenced in laboratory testing and flight operations. Analytical tools for prediction of linear system response are not necessarily adequate for reliable prediction of mid-frequency band dynamics and analysis of measured laboratory and flight data. A new MATLAB toolbox, designed to address the key challenges of mid-frequency band dynamics, is introduced in this paper. Finite-element models of major subassemblies are defined following rational frequency-wavelength guidelines. For computational efficiency, these subassemblies are described as linear, component mode models. The complete structural system model is composed of component mode subassemblies and linear or non-linear joint descriptions. Computation and display of structural dynamic responses are accomplished employing well-established, stable numerical methods, modern signal processing procedures and descriptive graphical tools. Parametric sensitivity and Monte-Carlo based system identification tools are used to reconcile models with experimental data and investigate the effects of uncertainties. Models and dynamic responses are exported for employment in applications, such as detailed structural integrity and mechanical-optical-control performance analyses.

dynamics↗
DOE OSTI2023

Deep Lynx Matlab Adapter

The Deep Lynx MATLAB Adapter is a Python application that connects the Deep Lynx data warehouse with any MATLAB simulation.

Wilsdon, KatherineN [Idaho National Laboratory] (0↗
DOE OSTI2025

Current interrupt method for calculating the electrochemical impedance in a solid oxide electrolysis stack

Here, in this work the time domain response of Solid Oxide Electrolysis Cells (SOEC) to a current interruption was transformed into the frequency domain using a carrier function Laplace transform, which is fit to the experimental data using a MATLAB Complex Nonlinear Least Squares (CNLS) solver. The hardware implementation, consisting principally of a high-speed switch and a fast-logging Analog to Digital Converter (ADC), was assembled and tested using a calibration module to assess the accuracy, repeatability, and speed of acquisition of the prototype device as compared against a calibrated commercial impedance spectrometer. Additionally, the current interrupt device and commercial FRA were used to acquire the impedance spectra of a four cell SOEC stack with a large, 300 cm 2 , active cell area.

SOEC↗
DOE OSTI2018

Internal wave energy flux from density perturbations in nonlinear stratifications

Internal gravity wave energy contributes significantly to the energy budget of the oceans, affecting mixing and the thermohaline circulation. Hence it is important to determine the internal wave energy flux $\boldsymbol{J}=p\,\boldsymbol{v}$ , where $p$ is the pressure perturbation field and $\boldsymbol{v}$ is the velocity perturbation field. However, the pressure perturbation field is not directly accessible in laboratory or field observations. Previously, a Green’s function based method was developed to calculate the instantaneous energy flux field from a measured density perturbation field $\unicode[STIX]{x1D70C}(x,z,t)$ , given a constant buoyancy frequency $N$ . Here we present methods for computing the instantaneous energy flux $\boldsymbol{J}(x,z,t)$ for an internal wave field with vertically varying background $N(z)$ , as in the oceans where $N(z)$ typically decreases by two orders of magnitude from the pycnocline to the deep ocean. Analytic methods are presented for computing $\boldsymbol{J}(x,z,t)$ from a density perturbation field for $N(z)$ varying linearly with $z$ and for $N^{2}(z)$ varying as $\tanh (z)$ . To generalize this approach to arbitrary $N(z)$ , we present a computational method for obtaining $\boldsymbol{J}(x,z,t)$ . The results for $\boldsymbol{J}(x,z,t)$ for the different cases agree well with results from direct numerical simulations of the Navier–Stokes equations. Our computational method can be applied to any density perturbation data using the MATLAB graphical user interface ‘EnergyFlux’.

Mechanics↗
DOE OSTI2016

SU-F-T-477: Investigation of DEFGEL Dosimetry Using MRI

Purpose: The DEFGEL dosimeter/phantom allows for the measurement of 3D dose distributions while maintaining tissue equivalence and deformability. Although DEFGEL is traditionally read out with optical CT, the use of MRI would permit the measurement of 3D dose distributions in optically interfering configurations, like while embedded in a phantom. To the knowledge of the authors, this work is the first investigation that uses MRI to measure dose distributions in DEFGEL dosimeters. Methods: The DEFGEL (6%T) formula was used to create 1 cm thick, 4.5 cm diameter cylindrical dosimeters. The dosimeters were irradiated using a Varian Clinac 21EX linac. The MRI based transverse relaxation rate (R2) of the gel was measured in a central slice of the dosimeter with a Spin-Echo (SE) pulse sequence on a 3T GE SIGNA PET/MR scanner. The R2 values were fit to a monoexponential dose response equation using in-house software (MATLAB). Results: The data was well fit using a monoexponential fit for R2 as a function of absorbed dose (R{sup 2} = 0.9997). The fitting parameters of the monoexponential fit resulted in a 0.1229 Gy{sub −1}s{sub −1} slope. The data also resulted in an average standard deviation of 1.8% for the R2 values within the evaluated ROI. Conclusion: The close fit for the dose response curve shows that a DEFGEL based dosimeter can be paired with a SE MRI acquisition. The Type A uncertainty of the MRI method shows adequate precision, while the slope of the fit curve is large enough that R2 differences between different gel doses are distinguishable. These results suggest that the gel could potentially be used in configurations where an optical readout is not viable, such as measurements with the gel dosimeter positioned inside larger or optically opaque phantoms. This work is partially funded by NIH grant R01CA190298.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

DISCO GUI v.1

SAND2024-08619O The DISCO GUI software, written in MATLAB, allows for data calibration for X-ray diffraction work at Sandia National Labs’ Z Machine. The program manually identifies and manipulates images from seven fiber bundles based on a calibration reticle. 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.

Smith, Anthony↗
DOE OSTI2021

Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

TEAMER: Performance mapping of Re Vision's Persistance PTO

Contains datasets from experimental measurements that were used to validate Re Vision's Persistence PTO's efficiency and performance. These measurements were obtained using a dynamometer test bench setup. The data includes open-circuit voltage and loss measurements to validate machine characteristics, efficiency mapping tests to determine the generator's performance mapping, and efficiency mapping tests to determine the converter's efficiency over the feasible operating range. This data was collected between June 2023 and September 2023. The data was collected at the National Renewable Energy Laboratory's Flatirons Campus, Colorado, United States. The data was collected using NREL's 5-kW dynamometer test bench, equipped with a torque sensor and various voltage and current sensors fed to a dedicated data acquisition system. Units for the data are included in the data file headers for each data series. A text editor or spreadsheet software such as Excel is required to view the *.csv data. The data are also provided in *.mat files. To view data plots, a Matlab script with *.mat files are provided.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

The Sandia Matlab AnalysiS Hierarchy (SMASH) toolbox

The SMASH (Sandia Matlab AnalysiS Hierarchy) toolbox is a collection of MATLAB code for data analysis. The toolbox contains general purpose functions, custom class definitions, and self-contained programs aimed at the needs of experimental physicists working in pulsed power research. The initial release (version 1.0) supports file access, signal/image analysis, and user interface creation; custom graphics and generic system tools are also provided. Much of the package is object oriented, encouraging users to build new capabilities from established classes. Future releases will continue this goal, expanding capabilities and streamlining application development.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2000

Flight Test Measurements From The Tu-144LL Structure/Cabin Noise Follow-On Experiment

This follow-on flight experiment on the TU-144LL Supersonic Flying Laboratory, conducted during the period September 1998 to April 1999, was a continuation of previous Structure/Cabin Noise Experiment 2.1. Data was obtained over a wide range of altitudes and Mach numbers. Measured were: turbulent boundary layer pressure fluctuations on the fuselage over its length; structural response on skin panels using accelerometers; and flow direction over three windows using 'flow cones'. The effect of steps in the flow was also measured using two window blank pairs; each pair bridged by a plate which created small sharp forward and aft facing steps. The effect of transducer flushness with the exterior surface was also measured during flight. Height test points were chosen to cover much of the TU-144's flight envelope, as well as to obtain as large a unit Reynolds number range as possible at various Mach numbers: takeoff, subsonic, transonic, and supersonic cruise conditions up to Mach 2. Data on engine runups and background noise were acquired on the ground. The data in the form of time histories of the acoustic signals, together with auxiliary data and basic MATLAB processing modules, are available on CD-R disks.

Rizzi, Stephen A.↗