Search NASA⌕ Search

SEARCH · Search NASA

Results for “Manuscript”

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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Data and interactive visualization for "The economic accessibility of CO2 sequestration through bioenergy with carbon capture and sequestration (BECCS) in the US" submitted to the journal "Land", 2020

Detailed outputs and interactive visualization to accompany the manuscript titled "The economic accessibility of CO2 sequestration through bioenergy with carbon capture and sequestration (BECCS) in the US" submitted to the journal "Land" in 2020. https://public.tableau.com/profile/billion.ton.2016#!/vizhome/ORNLBECCS2020/CO2EMITANDCAPTURE2

Langholtz, Matthew↗
DOE OSTI2021

A software package for plasma facing component analysis and design: the Heat flux Engineering Analysis Toolkit (HEAT)

The engineering limits of plasma facing components (PFCs) constrain the allowable operational space of tokamaks. Poorly managed heat fluxes that push the PFCs beyond their limits not only degrade core plasma performance via elevated impurities, but can also result in PFC failure due to thermal stresses or melting. Simple axisymmetric assumptions fail to capture the complex interaction between 3D PFC geometry and 2D or 3D plasmas. This results in fusion systems that must either operate with increased risk or reduce PFC loads, potentially through lower core plasma performance, to maintain a nominal safety factor. High precision 3D heat flux predictions are necessary to accurately ascertain the state of a PFC given the evolution of the magnetic equilibrium. A new code, the Heat flux Engineering Analysis Toolkit (HEAT), has been developed to provide high precision 3D predictions and analysis for PFCs. HEAT couples many otherwise disparate computational tools together into a single open source python package. Magnetic equilibrium, engineering CAD, finite volume solvers, scrape off layer plasma physics, visualization, high performace computing, and more, are connected in a single web-based user interface. Linux users may use HEAT without any software prerequisites via an appImage. This manuscript introduces HEAT, discusses the software architecture, presents first HEAT results, and outlines physics modules in development.

divertor physics↗
DOE OSTI2022

Supplemental material for: Verification, validation, and results of an approximate model for the stress of a Tokamak toroidal field coil at the inboard midplane

This is the supplemental material for the manuscript "Verification, validation, and results of an approximate model for the stress of a Tokamak toroidal field coil at the inboard midplane" submitted to Fusion Engineering and Design. This material includes PDF writeups of the derivations of the axisymmetric extended plane strain model, the elastic properties smearing model, and 20+ MATLAB scripts and functions which implement the model and generate the figures in the paper.

Swanson, CPS↗
DOE OSTI2021

DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]

DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.

Gasper, Paul↗
DOE OSTI2022

LBC (Learning Building Control)

LBC encompasses the source code and data to reproduce and extend results for a manuscript that compares demand responsive control schemes for multi-zone buildings. It includes examples of model predictive control (MPC), value function approximation via CVXPYLAYERS, and differentiable predictive control (DPC). The goal of the study is to evaluate state-of-the-art controllers and establish the efficacy (if any) of learning-based approaches that leverage deep neural networks in one way or another.

Zhang, Xiangyu↗
DOE OSTI2022

pnnl/doa

This is a set of Python scripts designed to implement a method of determining the domain of applicability for a trained machine model for molecular property prediction. Additionally, the scripts implement analysis methods to evaluate and explore the results of the method. These scripts are designed to support the submission of a manuscript describing the methods for publication.

Panapitiya, Gihan↗
DOE OSTI2022

Continuous-Time Models for Longitudinal Data

This software is used to estimate a probabilistic continuous-time model from data. The models capture nonlinear associations between variables that change over time. They can be used to infer future values of variables. These tools were used in the manuscript entitled "Continuous-Time Probabilistic Models for Longitudinal Electronic Health Records."

Kaplan, AlanD↗
DOE OSTI2023

Semi-Analytical Leakage Solutions for Aquifers (SALSA) v1

SALSA is a semi-analytical modeling tool that can provide assessments of pressure perturbations and brine leakage because of fluid injection and extraction activities in sedimentary basins. Sedimentary basins typically contain vertical sequences of near-horizontal aquifers separated by less-permeable aquitards. In the context of geologic carbon sequestration (GCS), while aquitards are relied upon to contain injected buoyant free-phase CO2 and limit inter-aquifer brine flow, the large number of leaky wells (e.g., unused/unsuccessful exploration wells, or improperly plugged water or oil/gas wells) that are present in sedimentary basins creates concerns for potential leakage and groundwater contamination. The mathematical theory and solution method included in SALSA is presented in our recent manuscript under review. SALSA is the first to account for the coupled leakage through aquitards and leaky wells, with geologic pressurization sources. SALSA based on semi-analytical solutions can be computationally very efficient for problems containing many injection and leaky wells in multilayered sedimentary systems, compared to any numerical simulation software requiring local mesh refinement around each well to obtain accurate results. Therefore, stakeholders (e.g., regulators, operators) for site screening, injection and post-injection pressure behavior and assessing leakage risks can use SALSA as a fast-predictive tool in GCS applications in multilayered aquifer systems.

Cihan, Abdullah↗
DOE OSTI2024

Codes for "Shallow Soil Response to a Buried Chemical Explosion with Geophones and Distributed Acoustic Sensing" DAG - 01101646

The codes reproduce the figures of the manuscript entitled "Shallow Soil Response to a Buried Chemical Explosion with Geophones and Distributed Acoustic Sensing" submitted to Journal of Geophysical Research - Solid Earth. Geophone data and Distributed acoustic sensing (DAS) data recorded during the Phase II of the The Source Physics Experiment (SPE) along a fiber-optic cable offshore were processed to understand the response of the shallow subsurface to an explosion. This Ground-based Nuclear Detonation Detection (GNDD), Low Yield Nuclear Monitoring (LYNM), and Source Physics Experiment (SPE) research was funded by the National Nuclear Security Administration, Defense Nuclear Nonproliferation Research and Development (NNSA DNN R&D).

Viens, Loic↗
DOE OSTI2024

pnnl/hyperedge-triplets

This software implements an algorithm for finding triplets of hyperedges with specified size relations. Specifically, this is an implementation of the pseudo-code in Algorithm 2 of our preprint manuscript "Size-aware hypergraph motifs", see: https://arxiv.org/pdf/2311.07783.pdf

Niu, Jason↗
DOE OSTI2023

Gamma-ray Spectrum Explanations (GRSE) v0.1

This code repository provides tools for producing various types of machine learning model explanations trained on gamma-ray spectra. It includes implementations of saliency mapping, Grad-CAM, LIME, and Kernel SHAP, including improvements and clarifications to some of the approaches that are specific to their use with gamma-ray spectral data. These tools are in support of the manuscript M.S. Bandstra et al., "Explaining machine-learning models for gamma-ray detection and identification," under review at PLOS ONE.

Bandstra, Mark↗
DOE OSTI2024

RGM: Random Geological Model Generation Package

This Fortran code is to accompany a manuscript to be submitted to Computers & Geosciences, a high-impact, peer-reviewed journal in computer methods for geosciences research. This Fortran code focuses on generation of synthetic geological models using a multi-randomization strategy. Generating high-fidelity synthetic geological models, including realistic seismic reflector migration images, faults, salt bodies, and relative geological time images, is the key for many supervised machine learning methods that aim to delineate faults and other geological properties of interest from seismic migration images. Our package contains two major functionalities: generating 2D synthetic random geological models and generating 3D synthetic random geological models. In each step of the generation process, we set random values for key properties of a geological model to improve the fidelity of the resulting geological model. The package also includes example codes on how to use the random geological model generation subroutines. We name this package RGM – Random Geological Model generation package.

Gao, Kai↗
DOE OSTI2023

Quasi-Newton Variational Bayes v.3.0

SAND2024-09052O Quasi-Newton Variational Bayes (QNVB) is a training algorithm that performs high-dimensional variational inference on machine learning models. It provides mechanisms to calibrate and control model uncertainty during training. The PyTorch implementation of projective integral updates for Gaussian mean-fields supports the manuscript, "Projective Integral Updates for High-Dimensional Variational Inference." 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.

Duersch, Jed↗
DOE OSTI2024

cjohnson-LANL/GRL_Kilauea

The python routines are outlined in detail to perform the methods and results in the manuscript under review in the journal Geophysical Research Letters titled “Seismic features predict ground motions during repeating caldera collapse sequence” with LA-UR-23-33345. All routines are written in open source python and were applied to publicly available data sets. The codes formats the data into the appropriate structure required to train a boosted tree regression model. Other codes produce figure results.

Johnson, Christopher W↗
DOE OSTI2024

cheny-lanl/PreMevE-MEO-2024

This package contains R&D python codes that can nowcast >2 MeV electron distributions in the Earth's outer radiation belt, using LANL's GPS electron data (publicly available) as the primary driver. Functions of this PreMevE-MEO model has been described with details in a manuscript titled "PreMevE-MEO: Predicting Ultra-relativistic Electrons Using Observations from GPS Satellites" (LA-UR-24-23843), which has been submitted to Space Weather Journal.

Chen, Yue↗