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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Gravitation and Mesh Adaption

The Gravitation and Mesh Adaptation (GaMA) toolbox consists of a set of Matlab classes for modeling the environment around asteroids and comets. A variety of gravitational models and supporting algorithms from are consolidated alongside a custom meshing utility tailored for the application. This allows the user to work within a single streamlined environment to import and manipulate surface definitions, probe the dynamical environment, integrate trajectories, and post-process results. For other applications, modified surface meshes can be exported. Gravity Models: 1) Werner's analytic polyhedron model 2) Mascon model 3) Gottlieb's spherical harmonic model 4) Approximate polyhedron models 5) Curvilinear surface models 6) Custom composite models Meshing Features: 1) Array-based half-edge data structure 2) Supports curvilinear surface definitions up to degree 4 3) Ray-tracing 4) Coarsening 5) Feature-based refinement 6) Projection 7) Smoothing 8) Mesh quality validity tests 9) Mesh repair Additional Features: 1) Solar radiation pressure model 2) Distant 3rd body model 3) Collision detection 4) Trajectory integration and post-processing 5) Visualization of surface fields

Pearl, Jason↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
DOE OSTI2022

LiTrack

LiTrack is a fast 1D longitudinal phase-space tracking code written in MATLAB, which includes standard cavities, chicanes, wakefields, steady-state CSR, and longitudinal space charge.

Lou, William↗
DOE OSTI2017

A cascading failure analysis tool for post processing TRANSCARE simulations

This is a MATLAB-based tool to post process simulation results in the EPRI software TRANSCARE, for massive cascading failure analysis following severe disturbances. There are a few key modules available in this tool, including: 1. automatically creating a contingency list to run TRANSCARE simulations, including substation outages above a certain kV threshold, N-k (1, 2 or 3) generator outages and branch outages; 2. read in and analyze a CKO file of PCG definition, an initiating event list, and a CDN file; 3. post process all the simulation results saved in a CDN file and perform critical event corridor analysis; 4. provide a summary of TRANSCARE simulations; 5. Identify the most frequently occurring event corridors in the system; and 6. Rank the contingencies using a user defined security index to quantify consequences in terms of total load loss, total number of cascades, etc.

Source record↗
DOE OSTI2022

Learning Planar Ising Models Software

Learning Planar Ising Models is a software package written in Matlab for learning relationships among variable in a dataset using graphical models. The software package implements a generally-applicable algorithm for learning planar Ising models from any multivariate dataset. The code provides an algorithm for learning the best planar Ising model to approximate an arbitrary collection of binary random variables (possibly from sample data). Given the set of all pairwise correlations among variables, we select a planar graph and optimal planar Ising model defined on this graph to best approximate that set of correlations. The software includes demonstrations of the algorithm in simulations and for applications on publicly available datasets. Details of the algorithm, demonstration simulations, and applications are given in Johnson, et al; 2016. Reference: Johnson, J. K., Oyen, D., Chertkov, M., and Netrapalli, P. (2016). Learning planar Ising models. Journal of Machine Learning Research.

Oyen, Diane↗
DOE OSTI2022

pnnl/Chemometric_Toolbox

The PNNL Chemometric Toolbox is a software collection of common MATLAB scripts that implement core chemometric algorithms for regression analysis. The three core regression techniques within this toolbox are classical least squares (CLS), principal component regression (PCR), and partial least squares (PLS). This is demonstrated using a supplied dataset of infrared (FTIR) spectral data with their corresponding concentrations

Smith, Ian↗
DOE OSTI2023

Parameter Identification for Battery Abuse Reaction Kinetics Models Using DSC Analysis [SWR-23-87]

This code estimates the parameters as inputs for NREL’s abuse reaction kinetics model using Differential scanning calorimetry (DSC) experimental data. The parameters for calculating the rates of abuse reactions among battery cell components at elevated temperatures include frequency factor, activation energy, and reaction orders. The least-squared method is employed to fit the reaction model to DSC data. The code is implemented in MATLAB.

Yang, Chuanbo↗
DOE OSTI2010

MueMat Multicrid Toolbox

MueMat is intended for the research and development of multigrid algorithms used in the solution of sparse linear systems arising from systems of partial differential equations. The software can generate example linear systems and provides short programs to demonstrate the various interfaces for creating, accessing, and applying the solvers. MueMat currently includes two types of algebraic multigrid methods and many commonly used smoothers. However, the software is intended to be extensible, and new methods can be incorporated easily. MueMat also allows for advanced usage, such as combining multiple methods and segregated solves. The library supports point and block access to matrix data. MueMat has been designed for use within the programming environment of the Mathworks program MATLAB®. All algorithms and methods in MueMat have been or will be published in the open scientific literature.

Boman, ErikG.↗
DOE OSTI2012

Generalized Environment for Modeling Systems

GEMS is an integrated environment that allows technical analysts, modelers, researchers, etc. to integrate and deploy models and/or decision tools with associated data to the internet for direct use by customers. GEMS does not require that the model developer know how to code or script and therefore delivers this capability to a large group of technical specialists. Customers gain the benefit of being able to execute their own scenarios directly without need for technical support. GEMS is a process that leverages commercial software products with specialized codes that add connectivity and unique functions to support the overall capability. Users integrate pre-existing models with a commercial product and store parameters and input trajectories in a companion commercial database. The model is then exposed into a commercial web environment and a graphical user interface (GUI) is applied by the model developer. Users execute the model through the web based GUI and GEMS manages supply of proper inputs, execution of models, routing of data to models and display of results back to users. GEMS works in layers, the following description is from the bottom up. Modelers create models in the modeling tool of their choice such as Excel, Matlab, or Fortran. They can also use models from a library of previously wrapped legacy codes (models). Modelers integrate the models (or a single model) by wrapping and connecting the models using the Phoenix Integration tool entitled ModelCenter. Using a ModelCenter/SAS plugin (DOE copyright CW-10-08) the modeler gets data from either an SAS or SQL database and sends results back to SAS or SQL. Once the model is working properly, the ModelCenter file is saved and stored in a folder location to which a SharePoint server tool created at INL is pointed. This enables the ModelCenter model to be run from SharePoint. The modeler then goes into Microsoft SharePoint and creates a graphical user interface (GUI) using the ModelCenter WebPart (CW-12-04) created at INL to work inside SharePoint. The GUI tool links slider bars and drop downs to specific inputs and output of the ModelCenter model that is executable from SharePoint. The modeler also creates in SAS, dashboards, graphs and tables that are exposed by links and SAS and ModelCenter Web Parts into the SharePoint system. The user can then log into SharePoint, move slider bars and select drop down lists to configure the model parameters, click to run the model, and then view the output results that are based on their particular input choices. The main point is that GEMS eliminates the need for a programmer to connect and create the web artifacts necessary to implement and deliver an executable model or decision aid to customers.

Alessi, RandolphSamuel↗
DOE OSTI2024

Surface Method Raytracer

Matlab based experiments that implement multiple ray tracing methods for deterministic radiation transport, including the Surface Method as described in the PhD Thesis, "CONSERVATIVE FIRSTCOLLISION SOURCE TREATMENT FOR RAY EFFECT MITIGATION IN DISCRETE-ORDINATE RADIATION TRANSPORT SOLUTIONS" by Alex Christensen.

Christensen, AlexB↗
DOE OSTI2023

RadSim: Math, Utility & RTK

This package includes three parts, (1) gov.llnl.math, (2)gov.llnl.utility and (3)gov.llnl.rtk, which are utilized in the development of Radiation Detector Simulator (RadSim) project. RadSim is being developed to provide the capability to: (1) simulate radiation source emissions, (2) interpolate results from radiation transport tools into a common format to prepare incident flux, and (3) model radiation detector response to rapidly produce synthetic radiation measurement templates. RadSim is targeted for open-source release, which will enable researchers and industry partners to model gamma-ray detectors response to simulated flux from the transport tool of their choice. The techniques and implementation will be entirely transparent, which will allow for improvements and boutique modifications by future researchers beyond the lifespan of this specific project. The first tool of the package, gov.llnl.math, includes classes and functions to define and perform basic math operations. Some of the example features available in the package include defining statistical distributions and performing algebra and matrix operations, all of which are already accessible on publicly available software packages such as MATLAB and ROOT. The second package gov.llnl.utility includes tools commonly used to enable optimization and readability of various data structures such as Java lists and external xml files. Lastly, the gov.llnl.rtk package includes classes and functions to implement methods commonly used in radiation physics, such as data structures to represent and characterize photon spectra and tools to apply well-defined and published methods to calibrate a given spectra.

Cheung, Hoi Sing↗
DOE OSTI2022

Cylindrical battery design app

Software that delivers optimal design parameters and performance predictions for cylindrical cells, which can range in size from microbatteries to EV batteries, is developed. The software utilizes machine learning and includes a graphical user interface to enable rapid prototyping and to accelerate energy storage research, development, and manufacturing. The Cylindrical battery design V1.0 comprises of three types of cylindrical batteries, Microbattery (Primary), Microbattery (Secondary) and 18650/21700/xxxxx Cylindrical battery. The software was developed in MATLAB. The software has the capability to output the cell design with the capacity ranges from several mAh to several million Ah.

Xiao, Jie↗
DOE OSTI2018

pnnl/ZebrafishBMD

ZebrafishBMD: A software tool to perform benchmark dose calculations using toxicity screening data of high-throughput zebrafish assays. High-content imaging of larval zebrafish behavior can be used as a screening approach to rapidly evaluate the relative potential for chemicals to cause toxicity. However, most statistical methods applied to these data transform movement values to incidence-based “hits” and calculate lowest effect levels (LELs), which loses individual fish resolution of behavior and defies hazard ranking due to reliance on applied dose levels. We developed a parallelizable workflow to calculate benchmark dose (BMD) values from dynamic, high-content zebrafish behavior data that scales for high throughput chemical screening. To capture the zebrafish movement response from light to dark stimulus, we summarized time-dependent data using both area under the curve and the immediate change at the transition point into two novel metrics that characterized abnormal behavior as a function of chemical concentration. The BMD workflow was applied to calculate BMD10 values of 1,060 ToxCast chemicals for 24 zebrafish endpoints, including behavior, mortality and morphology. ZebrafishBMD implements the BMD workflow and provides Matlab scripts to read the data (stored in a specific structure) from a .mat file, to calculate the behavioral endpoint values from movement versus time data, to construct concentrationresponse curves, to perform curve-fitting, and to calculate BMD10 values.

Thomas, Dennis↗
DOE OSTI2019

JGCRI/hydro-emulator

The hydrological emulator (HE) was built to mimic complex global hydrological models (GHMs) at a range of spatial scales. The HE was written in MATLAB and utilizes a genetic algorithm to calibrate the a, b, c, d, m parameters from the ABCD runoff algorithm to observed runoff from GHMs when forced by the same climate data. The HE employs two methods to choose from when processing basin data: lumped (by basin) and distributed (gridded). Both methods can be evaluated using the built-in Kling-Gupta efficiency as a measure of model performance when compared to the outputs from the more complex GHM. Testing shows that the HE is 7 orders of magnitude faster then the widely used Variable Infiltration Capacity (VIC) GHM.

Vernon, Chris↗
DOE OSTI2019

Fast Combinatorial Nonnegative Least Squares v 1.0

Software that employs an algorithm that rapidly and rigorously solves nonnegative least squares problems with many right hand sides. This is the latest internal version of MATLAB pseudocode that was described in a published in M. H. Van Benthem and M. R. Keenan, "Fast algorithm for the solution of largescale non-negativity-constrained least squares problems," J. Chemom. 18(10), 441-450 (2004). Example code of this algorithm was included in the publication. This algorithm was also patented in M. H. Van Benthem and M. R. Keenan, "Fast combinatorial algorithm for the solution of linearly constrained least squares problems,"USPTO, Ed., Sandia Corporation, USA (2008). SAND2019-10098 M 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.

Van Benthem, Mark↗
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↗