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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data and Scripts associated with a manuscript on ecosystem responses to wildfires in the Columbia River Basin

This data package is associated with the publication “Ecosystem leaf area, gross primary production, and evapotranspiration responses to wildfire in the Columbia River Basin” submitted to Biogeosciences (Shi et al., 2024; doi: 10.22541/au.171053013.30286044/v1). In this research, data products, leaf area index (LAI), gross primary production (GPP), and evapotranspiration (ET), from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to quantify the resistance and resilience of different ecosystem types in the Columbia River Basin (CRB). A machine learning algorithm, random forest (RF), was used to examine the impacts of precipitation, vapor pressure deficit (VPD), and burn severity from Monitoring Trends in Burn Severity (MTBS) on ecosystem resilience. The data package includes the processed MODIS data products, precipitation, VPD, and burn severity in 138 fire regions in CRB and the input files for RF model training. This data package includes six folders. The MODIS products are included in three MODIS_* folders with shell scripts for data clipping and *ncl files for data processing: (1) “/MODIS_LAI_CRB”; (2) “/MODIS_GPP_CRB”; and (3) “/MODIS_ET_CRB”. All the processed data for each fire event are NetCDF formatted. The MTBS burn severity data and the shell and *ncl scripts used for data processing are in the folder named (4) “MTBS_fire”. The ERA meteorological fields and the data processing scritps are in (5) “ERA_Var_CR”. All the scripts for figure development are in the format of *ncl and in the folder (6) “paper_scripts”. See the file ending in “flmd.csv” for a list of all files contained in this data package and descriptions for each. Tabular column headers and units are described in the data dictionary file ending in “dd.csv”.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Damage Pseudo Cross Section Generator

This software is a python script for generating group damage pseudo-cross-sections for specific elements, and tabulating them in forms useful to python or to office-open workbooks (xlsx). The damage pseudo-cross-section is needed for calculating how much neutron radiation damage a structural material receives in a reactor measured in displacements per atom (DPA). The process of generating these pseudo-cross-sections is laborious as it requires running the nuclear data processing code, NJOY, for hundreds of isotopes, and then post-processing the data. This also allows many opportunities for human error to be involved. This script eliminates these problems by fully automating the process.

Gale, MicahD.↗
DOE OSTI2025

sourcePy

Pollutant source identification techniques (of which there are many variations) are either locked behind researchers writing their own code for each use case or GUI platforms that are easy to use but inflexible and opaque. The Python package sourcePy brings together many of the pollutant source identification algorithms, giving the user full control out of the box. It aims to create a platform for source identification experiments where the full analysis from beginning to end can be done in Python, with a level of specificity in design that isn't available in the GUI options. sourcePy provides users with a few key features: -A Python interface with HYSPLIT, which can be used to generate trajectories and concentration plumes -Several Python classes which standardize the preparation and processing of data related to source identification experiments -Example scripts and notebooks that allow even new python users to get started with their own experiments quickly -Visualization methods

Arseneau, Isaac [Oak Ridge National Laboratory (OR↗
DOE OSTI2025

pbd-industrial-limit-of-performance (PBD limit of performance) v1.0.0

Software repository that contains models used for a paper about Platform-Based Design with limit of performance analysis for an industrial pilot study. This repository contains process and control models in Modelica and IDAES and scripts to develop an ML based controller that computes the control function that maximizes the techno-economic performance of cost and energy computed by the model. This software is meant to be released to reproduce the work described in a Journal publication that is now drafted with the working title "Energy System Limit of Performance Analysis using an Online Machine Learning Multi-Resolution Optimization Framework".

Amusat, Oluwamayowa [Lawrence Berkeley National La↗
DOE OSTI2022

Tools for Visualization and Analysis of Small-Angle Neutron Scattering Data: Descriptions and Examples

A great deal of progress has been made in improving the data reduction experience for the SANS instruments at the SNS and HFIR at ORNL. The existing data reduction toolset, drtsans, makes it possible to integrate data analysis and visualization tools into the data reduction scripts, thereby providing new opportunities for more automated data processing for users of the SNS and HFIR. Here, the first set of tools developed is described with usage examples.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

A Graphical User Interface for the Deep Underground Neutrino Experiment Robotic Test Stand

In preparation for DUNE, Fermilab along with six other institutions are testing cold electronics for quality control before components placed in the far detector. We test them by using a robotic arm that places these chips into sockets on a computer board that will test their functionality. Up until now, the chips have been tested using a command line script that drives a state machine to conduct tests step-by-step. In order to lower the skill barrier to conduct tests and to speed up the quality control process, I was tasked to create a graphical user interface that would allow users to use buttons, text boxes, and drop-down menus to input information and tell the testing state machine how to operate. I had to learn about the Python package Tkinter to start the process of widget placement. I further developed a pause feature unused in the previous command line script that would allow the user to shut down testing gracefully, bring the robotic arm to go back to ground state, and go forward or backward a step in the testing process. After completing the basic functionality of the GUI, I started testing production chips with the GUI to debug. Some issues were found, which required me to further develop parts of the inherited state machine code. The code for the GUI has now been pushed into the copy the DUNE/FD_CE git repository and will soon be merged with the official DUNE/FD_CE repository so that the other institutions testing DUNE cold electronics can use and expand upon it.

Gutierrez Villanueva, Jaziel [Fermilab]↗
DOE OSTI2025

Elasto-viscoplastic fast Fourier transform modeling framework for assessing microstructural effects on stress intensity factors characterizing fracture toughness

A large-strain elasto-viscoplastic fast Fourier transform (LS-EVPFFT) model with non-periodic (NP) velocity-based boundary conditions is adapted to simulate the sensitivity of stress intensity factors on microstructure for 304L stainless steel. The material was characterized via electron backscattered diffraction (EBSD) serial-sectioning to obtain a measured 3-D microstructural cell to perform simulations. The NP-LS-EVPFFT model, including the simulation setup and boundary conditions, was verified using a crystal plasticity finite element (CPFE) model. To this end, the generation of meshes of notched specimens was developed, which involved creating Python scripts for mesh “cutting” in Abaqus, and Sculpt scripts in Cubit for meshing of the measured microstructural cell processed with DREAM.3D. The complexity of the mesh preparation highlighted the advantages of the FFT-based model, which circumvents the mesh generation process. Given the efficiency of the FFT-based model, statistical distribution of stress intensity factors in function of crystal orientation at the crack tip, grain structure, and crystallographic texture surrounding the crack tip were predicted. Further, the distributions reveal about 10% variation of stress intensity factors with microstructure with the most significant sensitivity found to be the crystal orientation at the crack tip. The methodology developed in this work is discussed as a practical simulation tool for predicting the sensitivity of stress intensity factors on microstructural variability in metallic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2019

CICE Consortium Progress Report, June 2018 - December 2019

The CICE Consortium accomplished a number of major upgrades to our sea ice model code, scripts and testing infrastructure, while strengthening our visibility in the international modeling community. We released CICE versions 6.0.0, 6.0.1, 6.0.2 and 6.1.0, all of which include updated releases of Icepack. Major new capabilities include a floe size distribution, new coupling infrastructure, improvements to all of CICE’s dynamics options, a new atmospheric data set for historical simulations, overhauled documentation, and more robust and flexible scripts for running and testing the code. Several key testing processes were automated this year. In addition, the team is actively engaged in planning our first user workshop and tutorial in February 2020. In addition to overseeing modifications to the Consortium’s resources and code releases, management tasks included meeting with and updating the Consortium’s Sponsors group in October 2018, prioritizing tasks by mapping them onto potential release targets, and rescuing early CICE releases (version 1 through 3.14) from a disappearing LANL archive.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

SPND Sensitivity Calculations Using MCNP and Experimental Data from ACRR

The use of the Monte Carlo N-Particle Transport Code (MCNP) to calculate detector sensitivity for Self-Powered Neutron Detectors (SPNDs) in the Annular Core Research Reactor (ACRR) could be a vital tool in the effort to optimize the design of next-generation SPNDs. Next-generation SPND designs, which consider specific materials and geometry, may provide experimenters with capabilities for advanced mixed field dosimetry. These detectors will need to be optimized for configuration, materials, and geometries and the ability to model and iterate must be available in order to decide on the ideal. SPND design. SPNDs were modeled in MCNP which closely resembled the dimensions and location of actual detectors used in the ACRR. Tallies were used to calculate detector sensitivity. Using metrics from a previous report, oscilloscope data from pulses were manipulated in a Matrix Laboratory computing environment (MATLAB) script to calculate experimental detector sensitivity. This report outlines the process in which experimental data from ACRR pulses verified results from tallies in an MCNP ACRR model. The sensitivity values from experiments and MCNP calculations agreed within one standard deviation. Parametric studies were also performed with MCNP to investigate the effects of materials and dimensions of different SPNDs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

ManiPIO - Manipulate Process I/O for Industrial Control Systems

The Manipulate Process Input/Output (IO) (ManiPIO) program allows users to develop custom scripts to execute Industrial Control System (ICS) manipulations. The driving development principles of ManiPIO are modularity and ease of use. Currently the program can utilize the Modbus TCP communication protocol, but its modular programming structure allows other protocols to be quickly and easily implemented. Additional functionality can be added to fit specific user needs, due to the usage of Python classes. The input configuration instructions are human readable and allow the user to create a complex series of control system manipulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Another Set of Python Tools for Visualizing and Manipulating Small-Angle Neutron Scattering Data: Descriptions and Examples

The GP-SANS, Bio-SANS and EQ-SANS instruments at ORNL utilize drtsans for data reduction. drtsans is built on Python, and it can be run using python scripts and Jupyter notebooks. The flexibility afforded by Python makes it possible to incorporate additional actions into the scripts used for data reduction, such as analysis and visualization. Here, a new set of tools for visualizing and manipulating SANS data that can be incorporated into the data reduction scripts for the ORNL SANS instruments, or employed during post–processing, is presented that expands the capabilities of the two previously-released tool sets.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Reconstructing parent microstructures in martensitic and pearlitic Ti-Cu

Evaluating the microstructural evolution of parent phases has long been a challenge in metals with a partial or complete solid state transformation. Most parent microstructure evolution has to be inferred from the product microstructure, with some information permanently lost. Parent microstructure reconstructions relying on orientation relationships and EBSD data are a useful tool to overcome this challenge, though much background knowledge is required to do so. Here, this work introduces a reconstruction process for a eutectoid composition Ti-Cu binary alloy to evaluate the high temperature β-Ti phase from martensitic and pearlitic product microstructures. Martensitic microstructures were accurately reconstructed, enabling elucidation of β-Ti grain size and texture, and the employed script included for scientific reference. Pearlitic microstructures did not reconstruct effectively with the current process. However, new insight into the interfacial crystallographic orientations of pearlitic Ti-Cu microstructures was gleaned via this analysis, and future investigations of interest are discussed accordingly. Ultimately, these findings demonstrate tools widely implemented on conventional titanium and ferrous alloys can also be applied to understand parent microstructures in Ti-Cu and other alloys of emerging interest. This is especially of use as new manufacturing processes for such materials require understanding how parent phase microstructures respond to new, novel material processing such as AM.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Simulink Model of a New Energy EVG-005 5kW Hydropower Turbine

Simulink model for a New Energy 5kW hydropower turbine. ADCP data ("ds_streamwise_7_13.nc") and DC voltage, DC current, and rotor rotation observed from the New Energy EVG-005 Current Energy Converter (CEC) ("electrical_7_13_10ohms.nc") were collected at the Tanana River Test Site (TRTS) near Nenana Alaska. - Data was collected on July 13th, 2023 with a constant 10 ohms resistance applied with a DC load bank. - Simulink model is meant to resemble the electrical setup at the TRTS. - Model is initialized by running the "NewEnergy_2023_10hz.m" Matlab script. Then the Simulink model ("New_Energy_Model_PMSM.slx") can be run. - Results are processed with the "NewEnergy_2023_processing.m" Matlab code. The Matlab results are also saved in the "New_Energy_7_13_model_validation_results.mat" Matlab file. This can be directly loaded into the Matlab Workspace using the "Load()" command. - The timetable variables "electrical_model_downsampled" is the model results and the "electrical_limited" is the experimental data from the TRTS.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Finite Element Analysis System Workflow Tools

A collection of MATLAB functions and class definitions called System Workflow Tools (SWFT) are available to semi-automate steps in the simulation process. Some of these steps are often simple and routine for smaller finite element models, but if done directly by an analyst can quickly become labor intensive, cumbersome, and error prone for larger, system level models. Some of SWFT’s capabilities demonstrated in this report includes writing Sierra input decks and processing Quantities of Interest (QOI) from results files. SWFT also writes scripts in order to utilize other software programs such as Cubit (separating system level CAD into subassemblies and components, creating nodesets and sidesets), DAKOTA (ensemble management), and ParaView (contour plots and animations). Detailed commands and workflows from mesh generation to report generation are provided as examples for analysts to utilize SWFT capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Measurement of Grain Size by Electron Beam Backscattered Diffraction

The method of electron back-scatter diffraction provides an analytic means to identify crystalline phase and the grain size of structural materials. The need to utilize imaging parameters consistent with revealing the contributing microstructural scale is often overlooked through packaged imaging scripts. It is shown that imaging parameters consistent with a post processing assessment of low misorientation angles between boundaries are achievable as using pixel sizes on the order of 10 1 nm.

36 MATERIALS SCIENCE↗
DOE OSTI2021

HyPerPy (Hydrogen Extraction and Parabolic Trough Plant Performance Models using Python) [SWR-21-53]

The HyPerPy package consists of three scripts: 1) Model for Hydrogen Tracking in Parabolic Trough Power Plants, 2) Model for Receiver Performance, and 3) Model for Hydrogen Extraction Process. The power plant model (1) tracks hydrogen generation and transport within the circulating heat transfer fluid (HTF) of the power plant. This script is a transient, initial value simulation, in which the hydrogen concentration in the circulating HTF is 0 moles per cubic meter everywhere at time 0 seconds. Hydrogen concentration is calculated at discrete locations within the circulating HTF with 4-second time resolution. During each time step, the change in hydrogen concentration due to hydrogen generation and permeation is calculated for each location according to the local HTF temperature, vessel or piping properties, hydrogen concentration and partial pressure. This model predicts hourly hydrogen concentrations for typical operating days in the spring, summer, fall, and winter seasons. Data is used to create hourly mappings of hydrogen concentrations for a typical operating year. The receiver model (2) uses a 1-hour time step to estimate getter loading and annulus pressure. For each time step, the model uses HTF and ambient temperature data to estimate absorber tube, bellows, and getter temperatures for the time step. In addition, the model uses hourly HTF hydrogen concentrations that are generated by the power plant model (1), and annulus hydrogen pressure from the previous time step. With these data, the model calculates the moles of hydrogen permeating across the absorber tube and bellows during the time step. The net change in moles hydrogen is added or subtracted from the getter loading for the previous time step, and the hydrogen pressure is re-calculated based on getter loading and temperature. The model uses this algorithm to simulate hydrogen permeation and loading 24 hours per day, 365 days per year using seasonal temperature data. The model repeats these calculations for 25 years to create a mapping of receiver getter loading and annulus hydrogen pressure for four seasons of each year. The model for hydrogen extraction (3) estimates hydrogen extraction rates for a specific separation module configuration. The rate depends primarily on membrane area, vacuum pump performance, headspace gas flowrate to the membrane, and headspace gas hydrogen partial pressure. This model has two versions. The steady-state version predicts hydrogen extraction rates when the module is operating in separation mode. The dynamic version predicts hydrogen transfer through the membrane when the module is operating in sensor mode. The steady-state version is used with the plant model (1) to predict hydrogen partial pressures in the power plant when the extraction process is operating.

Glatzmaier, Gregory↗
DOE OSTI2026

Zero Resistance Ammetry (ZRA) Measurements of Sediment Electrochemical Gradients, Old Woman Creek, Ohio, USA, June–November 2022

This dataset contains raw and processed zero resistance ammetry (ZRA) measurements collected from wetland sediments at Old Woman Creek, a freshwater estuary on Lake Erie, Ohio, USA, between June and November 2022. Measurements were obtained using a vertically deployed electrode array positioned at multiple depths within the sediment profile to capture electrochemical gradients associated with microbial activity and sediment geochemistry. The raw dataset consists of parsed instrument log files containing timestamps, electrode pair identifiers, and measured electrical potential (mV). The processed dataset includes standardized and quality-controlled values with instrument saturation limits removed and timestamps converted to ISO 8601 format. Electrode line identifiers were mapped to physical depths, enabling interpretation of depth-resolved electrochemical gradients. Instrument saturation values (−2048, −2047, 2047, and 2048 mV) were identified as measurement limits and excluded from quantitative analyses. All data parsing, processing, and quality control steps are documented in an accompanying R Markdown script, ensuring full reproducibility from raw instrument logs to final datasets.

EARTH SCIENCE > AGRICULTURE > SOILS > ELECTRICAL C↗