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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MoorPy (Quasi-Static Mooring Analysis in Python)

MoorPy is a quasi-static mooring model and a suite of associated functions for mooring system analysis. The core model supports quasi-static analysis of moored floating systems including any arrangement of mooring lines and floating platforms. It solves the distributed position and tension of each mooring line segment using standard catenary equations. Floating platforms can be represented with linear hydrostatic characteristics. MoorPy automatically computes a floating system's equilibrium state and can be queried to identify a mooring system's nonlinear force-displacement relationships. Linearized stiffness matrices are efficiently computed using semi-analytic Jacobians. MoorPy also includes plotting functions and a library of mooring component property and cost coefficients. MoorPy can be used directly from Python scripts to perform mooring design and analysis tasks, or it can be coupled with other tools to compute quasi-static mooring reactions as part of a larger simulation.

Hall, Mathew↗
DOE OSTI2020

pySMARTS: SMARTS Python Wrapper (Simple Model of the Atmospheric Radiative Transfer of Sunshine)

The pySMARTS module contains functions for calling SMARTS: Simple Model of the Atmospheric Radiative Transfer of Sunshine, from NREL, developed by Dr. Christian Gueymard. SMARTS software can be obtained from: https://www.nrel.gov/grid/solar-resource/smarts.html Users will be responsible to obtain a copy of SMARTS from NREL, honor it's license, and download the SMART files into their PVLib folder. This wrapper is shared under a BSD-3-Clause License, and was originally coded in Matlab by Juan Russo (2001), updated and ported to python by Silvana Ayala (2019-2020).

Ayala Pelaez, Silvana↗
DOE OSTI2021

Python Library For Vehicular Emission Estimation

PyEmission is a Python library for estimation of vehicular emissions and fuel consumption. This tool covers a wide range of light duty motor vehicles including passenger car, SUV, passenger truck, and light commercial truck. The tool only takes second-by-second driving cycle and vehicle characteristics data as inputs and generate results of vehicular emissions (CO2, CO, NOx, and HC) and fuel consumption.

Rahman, MamunurMD↗
DOE OSTI2021

Hydrogenerate: Open Source Python Tool To Estimate Hydropower Generation Time-series

Hydropower is one of the most mature forms of renewable energy generation. The United States (US) has almost 103 GW of installed, with 80 GW of conventional generation and 23 GW of pumped hydropower [1]. Moreover, the potential for future development on Non-Powered Dams is up to 10 GW. With the US setting its goals to become carbon neutral [2], more renewable energy in the form of hydropower needs to be integrated with the grid. Currently, there are no publicly available tool that can estimate the hydropower potential for existing hydropower dams or other non-powered dams. The HydroGenerate is an open-source python library that has the capability of estimating hydropower generation based on flow rate either provided by the user or received from United States Geological Survey (USGS) water data services. The tool calculates the efficiency as a function of flow based on the turbine type either selected by the user or estimated based on the “head” provided by the user.

Mitra, Bhaskar↗
DOE OSTI2022

Python catemis package for DARHT Axis-II Dispenser-Cathode Emissivity and Temperature Analysis

The catemis python package includes the analysis methods used in paper "DARHT Axis-II Dispenser-Cathode Emissivity and Temperature", which was presented at the 2021 Weapons Engineering Symposium and Journal (WESJ) hosted by Los Alamos National Laboratory. The paper (LA-UR-21-25647) and presentation (LA-UR-21-28231) report on a temperature and emissivity model that has been developed and applied to data from diagnostics used to measure the surface temperature of the DARHT Axis-II hot dispenser cathode over more than a decade of operation. The catemis package provides all the methods used in reporting these results along with the data files used to create the plots and tables in the paper. This includes methods for loading and processing FAR spectral pyrometer data along with methods for temperature calibration of cathode images.

Koglin, Jason↗
DOE OSTI2020

PyEnvBuilder - Python Environment Builder

PyEnvBuilder is a python package that can be used to parse a well-defined YAML file and generate a packed conda environment, which can be later deployed and unpacked on different machines that do not require conda/python.

Slepicka, Hugo↗
DOE OSTI2022

BLAST aka: BLAST-Py see also BLAST-Lite (Battery Lifetime Analysis and Simulation Tool Suite - Python) [SWR-22-69]

Battery Lifetime Analysis and Simulation Tool Suite (BLAST or BLAST-Py) developed in the Python programming language. BLAST-Py predicts the evolution of lithium-ion battery performance metrics over their lifetime, using models trained on lab-based accelerated aging data to predict battery performance in dynamic, real-world use. BLAST-Py contains existing models for a variety of lithium-ion battery chemistries (NMC/Gr and LFP/Gr). See also the open-source version of this software tool known as "BLAST-Lite" at: https://github.com/NREL/BLAST-Lite

Smith, Kandler↗
DOE OSTI2021

PyBLM (Python-based Battery Life Model) [SWR-21-50]

PyBLM is a battery life model to predict the capacity fade of two home battery energy storage systems, manufactured by LG and Tesla. The battery life model is built from experimental test data, and accounts for calendar and cycling aging mechanisms as functions of cell environmental conditions and use. PyBLM is developed in Python and formatted to work in conjunction with another NREL software named TEMPEST (Thermo-Electric Model for Powering Energy Storage Technologies).

Mishra, Partha↗
DOE OSTI2022

Python ISMAGS subgraph isomorphism algorithm

SAND2022-14468 O The Python ISMAGS subgraph isomorphism algorithm is a translation of a java-based algorithm. It used for finding unique occurrences of a motif subgraph within a larger network graph. 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.

DeBonis, Mark↗
DOE OSTI2023

MOOSE-Python Binder

This code is a wrapper for Python code so that it can be accessed by MOOSE and the two can pass variables back and forth. Since this code was developed by ORNL, it must be independently approved before being added to the MOOSE repository.

Cheniour, Amani↗
DOE OSTI2023

Space Situational Awareness for Python

SSAPy is a python package allowing for fast and precise orbital modeling. SSAPy is designed with speed and accuracy in mind and offers the following capabilities: - A variety of integrators, including Runge-Kutta, SciPy, SGP4, etc. - Customizable force propagation models, including a variety of Earth gravity models, lunar gravity, radiation pressure, etc. - Multiple-hypothesis tracking (MHT) UCT linker - Vectorized computations - Short arc probabilistic orbit determination - Conjunction probability estimation - Uncertainty quantification - Monte Carlo data fusion - Support for multiple coordinate frames (with coordinate frame conversions)

Schlafly, Edward↗
DOE OSTI2023

Python Urban Deployment Model (PyUDM) v1.0.0

The Python Urban Deployment Model (PyUDM) is a simulation tool used to investigate networks of static radiation detectors in urban environments. PyUDM simulates traffic, stationary NaI gamma-ray detectors, and moving radioactive sources on simulated vehicles. The analyzed output of the simulations contain valuable insights into the performance of different configurations of urban radiological detector configurations such as their ability to detect sources moving through the environment. To accurately simulate radioactive material moving through urban environments, PyUDM combines Monte Carlo simulation tools, publicly available map and traffic data, and measured gamma-ray spectra from urban environments.

Rofors, Emil↗
DOE OSTI2022

Useful python scripts

SAND2023-11904O This collection of original scripts is made up of Python, Makefile, and Vim. Each script performs a single commonly used function in an encapsulated manner that can easily integrate into larger application-specific software. 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.

Penney, Keith↗
DOE OSTI2024

Python wrapper library and analysis functions for Geotab Altitude API [SWR-24-77]

This software library serves as a Python wrapper for Geotab's Altitude API. It streamlines querying of the API, converts loosely structured API outputs into a standardized tabular data format, and enables analysis of the resulting data tables. It also includes example notebooks showing how to use the library.

Bruchon, Matthew↗
DOE OSTI2021

Python HITMIX

SAND2024-10158O Python HITMIX is a software package for computing hitting time moments of vertices in a graph. Hitting time moments can be used to rank the strengths of relationships between vertices in a graph. Examples are provided for computing and using hitting time moments on generic graphs and similarity graphs arising from the analysis of text documents in information retrieval applications. 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.

Dunlavy, Daniel↗
DOE OSTI2025

Python Measurement-Informed Modelling Method (PythonMIMM) v1.02

This Python program implements the calculations for electrical energy efficiency and electrical losses in the paper "Energy and power quality measurement for electrical distribution in AC and DC microgrid buildings" (https://www.sciencedirect.com/science/article/abs/pii/S0306261921015658). The inputs are metered voltage, current, or power data of meters located throughout the DC system. In the absence of metered data, the user must input some other information about the building such as the types of power converters or wire lengths. The program will build a loss model of the DC system based on metered data and electrical information. It will also generate an equivalent AC system and determine the losses and efficiency.

Gerber, Daniel [Lawrence Berkeley National Laborat↗
DOE OSTI2025

ATcT — Active Thermochemical Tables Python Interface

SF-25-140 atct is a lightweight, Python client for the ATcT v1 API that enables programmatic access to high-accuracy thermochemical data and turnkey reaction-enthalpy analysis. The package implements full v1 endpoint coverage (species lookup by ATcT ID, name, formula, SMILES, InChI, CAS RN; covariance queries; health checks) with robust error handling, retries, and environment-based configuration for local/production endpoints. Beyond data retrieval, atct provides rigorously implemented reaction calculators that propagate uncertainties via either (i) a conventional independent-errors method (0 K or 298.15 K) or (ii) covariance-aware propagation using provided covariances at 298.15 K. Typed data classes ensure transparent, reproducible data structures and carry ATcT Thermochemical Network (TN) version identifiers for provenance. Dual import paths and comprehensive examples facilitate integration into research pipelines, enabling reproducible thermochemical calculations, automated validation, and downstream method development.

Bross, DavidHamilton [Argonne National Laboratory ↗