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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Bead-Tracking Toolbox for MATLAB

A collection of MATLAB scripts forming a toolbox for video (frame by frame) detection and tracking of beads imaged with fluorescence microscope for estimating bead flow and location tracks. Scripts accept tiff image stacks of video frames and provide bead locations and tracked positions over frames in stack. Various utility functions are also provided to assist in data I/O. A custom solution for specific data source - specifically, the software works well for noisy/out of focus florescence microscopy images.

Rose, DerekC.↗
DOE OSTI2022

2-D Surface Temperature Simulation Data and Matlab Scripts

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.

Sjoberg, Carl Magnus↗
DOE OSTI2018

ISAP-MATLAB Package for Sensitivity Analysis of High-Dimensional Stochastic Chemical Networks

Stochastic simulation and modeling play an important role to elucidate the fundamental mechanisms in complex biochemical networks. The parametric sensitivity analysis of reaction networks becomes a powerful mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, due to overwhelming computational cost, parametric sensitivity analysis is a extremely challenging problem for stochastic models with a high-dimensional parameter space and for which existing approaches are very slow. Here we present an information-theoretic sensitivity analysis in path-space (ISAP) MATLAB package that simulates stochastic processes with various algorithms and most importantly implements a gradient-free approach to quantify the parameter sensitivities of stochastic chemical reaction network dynamics using the pathwise Fisher information matrix (PFIM; Pantazis, Katsoulakis, and Vlachos 2013). The sparse, block-diagonal structure of the PFIM makes its computational complexity scale linearly with the number of model parameters. As a result of the gradientfree and the sparse nature of the PFIM, it is highly suitable for the sensitivity analysis of stochastic reaction networks with a very large number of model parameters, which are typical in the modeling and simulation of complex biochemical phenomena. Finally, the PFIM provides a fast sensitivity screening method (Arampatzis, Katsoulakis, and Pantazis 2015) which allows it to be combined with any existing sensitivity analysis software.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2011

ORNL ADCP Post-Processing Guide and MATLAB Algorithms for MHK Site Flow and Turbulence Analysis

Standard methods, along with guidance for post-processing the ADCP stationary measurements using MATLAB algorithms that were evaluated and tested by Oak Ridge National Laboratory (ORNL), are presented following an overview of the ADCP operating principles, deployment methods, error sources and recommended protocols for removing and replacing spurious data.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2012

MicroCT: X-Ray Radiograph Quality Assurance Through the Analysis of Copper Strip Data Using the Matlab CuStrip Analysis GUI

This Standard Operating Procedure (SOP) supplements the LEDP data acquisition SOPand reference material and specimen analysis SOP, and provides the specific procedural steps for analyzing copper strip (CuStrip) data using the Matlab CuStrip Analysis Graphical User Interface and preparing the tables and graphs to be used in the reporting of analytical results as required by the LEDP Quality Assurance Plan for MicroCT Scanning]. This procedure applies only to the analysis of two-slit data.

42 ENGINEERING↗
DOE OSTI2012

A MATLAB GUI for the prediction of coefficients of restitution

The motivation behind a MATLAB GUI to predict the coefficients of restitution is to enhance the fidelity of rigid body simulations. Currently, the common practice is to use the coefficient of restitution as a way to capture numerous factors of damping instead of using a coefficient true to the materials in contact. By switching to a model that predicts the coefficient of restitution as a function of velocity between any two materials, rigid body simulations will be more accurate due to higher fidelity models of the actual physics. Additionally, by packaging this software with a library of materials, all rigid body simulations can be run using consistent properties, eliminating any guesswork and “ballpark” figures. This report will therefore cover the existing code and how it delivers all of the features needed to easily estimate coefficients of restitution.

Volk, Christopher Patterson↗
DOE OSTI2014

Simulation for Wind Turbine Generators: With FAST and MATLAB-Simulink Modules

This report presents the work done to develop generator and gearbox models in the Matrix Laboratory (MATLAB) environment and couple them to the National Renewable Energy Laboratory's Fatigue, Aerodynamics, Structures, and Turbulence (FAST) program. The goal of this project was to interface the superior aerodynamic and mechanical models of FAST to the excellent electrical generator models found in various Simulink libraries and applications. The scope was limited to Type 1, Type 2, and Type 3 generators and fairly basic gear-train models. Future work will include models of Type 4 generators and more-advanced gear-train models with increased degrees of freedom. As described in this study, implementation of the developed drivetrain model enables the software tool to be used in many ways. Several case studies are presented as examples of the many types of studies that can be performed using this tool.

17 WIND ENERGY↗
DOE OSTI2014

Development of an improved MATLAB GUI for the prediction of coefficients of restitution, and integration into LMS.

In 2012, a Matlab GUI for the prediction of the coefficient of restitution was developed in order to enable the formulation of more accurate Finite Element Analysis (FEA) models of components. This report details the development of a new Rebound Dynamics GUI, and how it differs from the previously developed program. The new GUI includes several new features, such as source and citation documentation for the material database, as well as a multiple materials impact modeler for use with LMS Virtual.Lab Motion (LMS VLM), and a rigid body dynamics modeling software. The Rebound Dynamics GUI has been designed to work with LMS VLM to enable straightforward incorporation of velocity-dependent coefficients of restitution in rigid body dynamics simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

MATLAB Reference [Slides]

These slides are intended to be used as a reference for the MATLAB programming language and development environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Automated Installation Verification of COMSOL via LiveLink for MATLAB

Verifying that a local software installation performs as the developer intends is a potentially time-consuming but necessary step for nuclear safety-related codes. Automating this process not only saves time, but can increase reliability and scope of verification compared to ‘hand’ comparisons. While COMSOL does not include automatic installation verification as many commercial codes do, it does provide tools such as LiveLink™ for MATLAB® and the COMSOL API for use with Java® through which the user can automate the process. Here we present a successful automated verification example of a local COMSOL 5.0 installation for nuclear safety-related calculations at the Oak Ridge National Laboratory’s High Flux Isotope Reactor (HFIR).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Plotting XPS Narrow Scans (and Other data) with MATLAB, 1. Using the 'plot' and 'subplot' functions.

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 ® .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

An Unsteady State Gas Transport Model in Matrix Laboratory (MATLAB) for PWR Gas Management

Reactor Coolant System (RCS) hydrogen gas management can be a challenge at Pressurized Water Reactors (PWRs) during shutdown, startup, and steady state operation. Poor hydrogen control can result in higher general corrosion rates for RCS components and increases in critical path time for shutdown or startup. Also, performance indicator penalties may be accrued by entry into Electric Power Research Institute (EPRI) Action Level conditions or by accrual of Chemistry Effectiveness Index (CEI) penalties established by the Institute of Nuclear Power Operations (INPO). Furthermore, poor hydrogen control may increase industrial safety risk due to the creation of an explosive environment. Part of the hydrogen management difficulty arises from the interaction of several phenomena that require complex simulations to fully understand. While hydrogen gas transport models have been developed, the application of these models is limited by the computational intensity required to accurately model the dynamism inherent in plant shutdown or startup. Subsequently, a gas transport model is developed in a suitable computational program to meet these needs and provide input to an optimized RCS hydrogen management strategy for all operational modes. The gas transport model is used to model RCS hydrogen behavior for an example PWR during an End of Cycle (EOC) Forced Outage (FO) startup, a refueling outage (RFO) shutdown, and during nominal steady state operation. The computational software selected is Matrix Laboratory (MATLAB{sup R})

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Neural Network Code MATLAB

SAND2022-12606 O Neural Network Code MATLAB reads in an excel file, processes the data, trains a neural network, and tests the neural network. The code generates predictions of diffusion constants based on the data in the excel file. 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.

Allers, Joshua↗
DOE OSTI2015

Matlab Scripts and Sample Data Associated with Water Resources Research Article

Scripts and data acquired at the Mirror Lake Research Site, cited by the article submitted to Water Resources Research: Distributed Acoustic Sensing (DAS) as a Distributed Hydraulic Sensor in Fractured Bedrock M. W. Becker(1), T. I. Coleman(2), and C. C. Ciervo(1) 1 California State University, Long Beach, Geology Department, 1250 Bellflower Boulevard, Long Beach, California, 90840, USA. 2 Silixa LLC, 3102 W Broadway St, Suite A, Missoula MT 59808, USA. Corresponding author: Matthew W. Becker (matt.becker@csulb.edu).

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Plotting XPS Narrow Scans (and other Data) with MATLAB’s Powerful Plotting Capabilities, 2. An Introduction to Handle Graphics

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.

42 ENGINEERING↗