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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

baseflow: a MATLAB and GNU Octave package for baseflow recession analysis

baseflow is a MATLAB® toolbox designed for baseflow recession analysis, a technique used in hydrologic science to infer aquifer properties from streamflow. By leveraging widely available streamflow data, baseflow can be used to estimate aquifer properties such as hydraulic conductivity and drainable porosity over the modern instrumental stream gage record. The toolbox is intended for analysis of measured streamflow values recorded on a daily timestep, and is tailored for shallow, unconfined riparian aquifers that discharge groundwater laterally into adjacent streams. Additionally, baseflow can analyze the collective behavior of individual hillslope aquifers constituting hydrologic catchments, known as “watersheds”, from a nonlinear dynamical systems perspective. The toolbox incorporates recent advances in baseflow recession analysis to enable objective estimations of aquifer properties, and their sensitivity to methodological decisions, at both hillslope and catchment scales.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Matlab implementation of a novel semi-structured kinetic model for methanotroph-photoautotroph cocultures

This paper presents the MatLab implementation details of a novel semi-structured kinetic model for methanotroph-photoautotroph cocultures. This includes the parameterization of the modeling equations, and the initialization of the simulation based on experimental conditions. More importantly, it provides details on how the differential equations governing mass balances in both gas and liquid phases are integrated together to simulate the system dynamics over time. The semi-structured kinetic model for methanotroph-photoautotroph coculture is validated using a wide range of experimental conditions. The model: Accurate predicts both the coculture growth in liquid phase and the gas composition changes in head space over time; Explicitly models the exchange of in situ produced O 2 and CO 2 within the coculture; Considers the self-shading effect on the growth of photoautotroph.

42 ENGINEERING↗
DOE OSTI2023

Version 2.0.0 - M-SPARC: Matlab-Simulation Package for Ab-initio Real-space Calculations

M-SPARC is a Matlab code for performing ab initio Kohn–Sham Density Functional Theory simulations. Version 2.0.0 of the software further extends its capability to include relativistic effects, dispersion interactions, and advanced semilocal/nonlocal exchange–correlation functionals. These features significantly increase the fidelity of first principles calculations that can be performed using M-SPARC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Ultrafast Laser Pulse Generation by Mode Locking: MATLAB-Based Demonstrations

Ultrafast laser spectroscopy is a valuable and increasingly accessible technique for studies of rapid chemical reactions. Critical to ultrafast spectroscopy is the concept of mode locking, a technique that enables a fixed phase relationship between laser modes, resulting in laser pulses with very short duration (in the fs or ps range). Despite the increasing importance of ultrafast lasers in chemistry, the introduction of key concepts behind their operation into the undergraduate and graduate chemistry coursework has been limited. To help the incorporation of these topics into chemistry courses, we report here a hands-on activity that helps students develop an intuitive understanding of the factors that impact electromagnetic wave evolution in optical cavities and the process of mode locking. We first provide the theoretical background by introducing cavity modes and contrasting them with well-known propagating electromagnetic waves. We then explore what happens when modes are added and how the relative phase between the modes affects their behavior. In the second section of this report, three teaching modules are provided, along with associated MATLAB codes and animated images, that can be used in the classroom to introduce concepts of cavity modes and mode locking. These teaching modules start by contrasting propagating electromagnetic waves with cavity modes and then illustrate what happens when multiple modes are present in the cavity and how the relative phase between the modes affects the overall electromagnetic field in the cavity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Teaching Nonradiative Transitions with MATLAB and Python

Nonradiative transitions are changes in energy states of atoms, ions, or molecules that do not involve the emission or absorption of photons. Despite their importance in understanding luminescent properties and photochemical reaction mechanisms, nonradiative transitions are rarely given more than a qualitative overview in undergraduate and even graduate physical chemistry curricula. To supplement the coverage of nonradiative transition topics, we provide here a set of active learning exercises to help students develop an intuitive understanding of the factors that determine the rate of nonradiative transitions. Here, we start by outlining the theoretical background through the formulations of the Franck–Condon factor and its relation to the rate of nonradiative transition. We then introduce three teaching modules, with associated MATLAB and Python codes, to explore how (1) the excited state nuclear displacement, (2) the electronic energy gap between excited and ground state and (3) the excited/ground state vibrational mode frequencies affect the magnitude of the Franck–Condon factor and thereby the rate of nonradiative transitions. The wave function overlap plots that accompany all teaching modules provide direct visualization of the effect of input parameters on the magnitude of Franck–Condon overlap integral.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

ED-LPD, MATLAB standalone

This computer software is a MATLAB standalone code for implementation of the Lumped Particle Domain model. The model solves charge transfer kinetics, lithium diffusion dynamics, charge conservation and energy conservation occurring across the porous electrode pair unit cell sandwich to predict electrical and thermal response of a battery. Physico-chemical states of batteries are also evaluated by the model.

Kim, Gi-Heon↗
DOE OSTI2017

Tensor Toolbox for MATLAB v. 3.0

Tensors (also known as multidimensional arrays or N-way arrays) are used in a variety of applications ranging from chemometrics to network analysis. The Tensor Toolbox provides classes for manipulating dense, sparse, and structured tensors using MATLAB's object-oriented features. It also provides algorithms for tensor decomposition and factorization, algorithms for computing tensor eigenvalues, and methods for visualization of results.

Kolda, Tamara↗
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 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 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 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↗