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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

High Performance Computing Innovation Center Open Source Developer Tools

The High Performance Computing Innovation Center (HPCIC) aims to ease the transition for developers to use open source software provided by the lab. HPCIC Developer Tools is a collection of software, containers, cloud configurations, and associated documentation that make it easy to deploy tutorials or small apps to demonstrate lab-developed software. For example, building a tutorial container that includes lab software and interactive interfaces; a command line or web-based tool that accepts user preferences for the tutorial; supporting tools and software development kits (SDKs) for developer interactions or productivity in different languages embraced by the larger developer community such as Go, Rust, and Python; and automation in version control to support continued update of software and associated resources. These tools are best developed in an open source environment such as GitHub, not only to champion the lab's open source software, but for purposes of branding and demonstrating the lab's leadership in open source. Such an effort that brings in more developers to use and contribute to lab software can further improve the quality of the software, and developer experience at the lab.

Beckingsale, DavidA↗
DOE OSTI2023

CEGANN: CRYSTAL EDGE GRAPH ATTENTION NEURAL NETWORK

SF-22-156 Machine learning (ML) models and applications in materials design and discovery typically involve the use of feature representations or descriptors followed by a learning algorithm that maps them to user desired properties of interest. Most popular mathematical formulation-based descriptors are not unique across atomic environments and suffer from transferability issues across different application domains and/or material classes. The CEGANN code provides a unified interface to facilitate material characterization across materials across multiple scales (from atomic to mesoscale) and diverse classes of materials ranging from metals oxides, non-metals, and even hierarchical materials such as zeolites and semi ordered materials such as mesophases. CEGANN implements a Graph Attention Network (GAT) type convolution architecture. The details of network architecture can be found in the paper https://doi.org/10.48550/arXiv.2207.10168. The software comes with pretrained examples and dataset for the classification of the following representative systems: (1) Structure-level representation such as space group (2) Structural dimensionality (e.g., bulk, 2D, clusters etc.) (3) Grain boundary identification (4) Nucleation and growth of a zeolite polymorph (5) Characterization of binary mesophases and their phase transitions (6) Growth of ice. The code is written in python programming language.

CHAN, HENRYT↗
DOE OSTI2022

PARMOO

ParMOO is a Python library for solving multiobjective simulation optimization problems, while exploiting problem structure. ParMOO stands for "parallel multiobjective optimization".

WILD, STEFAN↗
DOE OSTI2023

pnnl/i2x

Interconnection Innovation e-Xchange (i2x) Test Systems allows for scripted customizations of social behavior, recovery operations, investment decisions, and metrics in a simplified Python module. Public test systems allow for replication of results by other stakeholders

McDermott, Tom↗
DOE OSTI2022

Greedy Latent Space Dynamics Identification

This is a variant of LaSDI where it minimizes both reconstruction loss terms of auto encoder and dynamics regression loss terms simultaneously. Plus, an active learning framework is adopted so that a physics-informed greedy sampling point can be added to enhance the performance of gLaSDI. It is written in python and several examples are included in this software.

He, Xiaolong↗
DOE OSTI2023

GHEDesigner [SWR-23-33]

GHEDesigner is a Python package for designing ground heat exchangers (GHE) used with ground source heat pump (GSHP) systems.

Mitchell, Matt↗
DOE OSTI2022

SING (Synthetic dIstribution Network Generator) [SWR-22-57]

Synthetic dIstribution Network Generator is a standalone python module that is able to create synthetic distribution models for OpenDSS using GIS datasets. The software uses road and building information from OpenStreetMaps to generate these synthetic models.

Latif, Aadil↗
DOE OSTI2023

PNNL-m-q/lipidoz

A Python tool for determination of lipid double bond locations from OzID data

Ross, Dylan↗
DOE OSTI2023

PVplr-stGNN 0.1.10

PV Performance Loss Rate Estimation using Spatio-temporal Graph Neural Networks PVplr-stGNN is a Python 3 package developed by the SDLE Research Center at Case Western Reserve University in Cleveland OH. This repository contains the full source PVplr-stGNN package. The package contains the PV-stGAE for missingness data detection and imputation and PV-DynGNN for PLR estimation.

Fan, Yangxin [Case Western Reserve Univ., Clevelan↗
DOE OSTI2023

DSS-SimPy-RL (Open-DSS and SimPy based Cyber-Physical RL environment) [SWR-23-29]

Recently, numerous data-driven approaches to control an electric grid using machine learning techniques have been investigated. With the advancement of reinforcement learning (RL) based techniques, gradually the conventional optimization based solvers are being replaced with RL approach where there is uncertainty in the environment such as renewable generation or cyber system emulation. However, to train an agent efficiently, it requires numerous interactions with an environment to learn the best policies. There are numerous RL environments for the power systems based on some well-known simulators, similarly there are environment for communication domains. While majority of the cyber emulators are based in an UNIX environment, the power simulators are based in the Windows-based operating system, the generation of cyber-physical mixed domain RL environment has been challenging. Existing co-simulation methods are efficient but resource and time intensive to generate large scale data set for training RL agents. Hence, this software focuses on development and validation of a mixed domain RL environment using Open DSS for the physical side and leverages a discrete event simulator python package, SimPy, for cyber-side emulation which is Operating Systems agnostic. Further utilizing this software co-simulation and training RL agents for re-routing based resilient control for network reconfiguration and volt-var control in power distribution feeder are performed.

Sahu, Abhijeet↗
DOE OSTI2022

Algorithms for Forward and Inverse Problem Solutions in Phase Contrast Tomography

PhaseTorch is a python package that implements algorithms for forward and inverse problem solutions in X-ray phase contrast tomography (XPCT). XPCT is useful to reconstruct the 3D distribution of the refractive index decrement and absorption index of an imaged object. PhaseTorch implements algorithms for simulation, reconstruction, and analysis of XPCT data. PhaseTorch provides algorithms for simulation of XPCT data of an object with known refractive and absorption indices. It contains algorithms for reconstructing the projections of the absorption index and refractive index decrement.

Sridhar, Venkatesh↗
DOE OSTI2023

sfapi_client v1.0

This software is a client designed to interact with the Superfacility API developed at NERSC. It allows users to easily access the api in python and programmatically interact with the compute resources available at NERSC. Other implementations are ad-hoc and made by our user base, the goal of the project is to encourage more users to adopt the API by making it easier to start building complex HPC workflows.

Tyler, Nicholas↗
DOE OSTI2023

optimas v0.1

optimas is a Python library that can coordinate a large number of numerical simulations on high-performance computing resources, with the aim of optimizing a given simulation result. More specifically, this library is currently used for design optimization of laser-plasma particle accelerators. In this context, the performance of particle accelerator designs are often evaluated with large-scale simulation codes, and many separate simulations need to be run - with different design parameters - in order to find the most performant design. optimas facilitates this process by providing a convenient interface to advanced optimizers (e.g. Bayesian optimization), and by coordinating the execution of the different simulations on HPC resources. (This is done by leveraging the library libensemble.) Compared to other open-source optimization libraries (e.g. Ax), optimas is more tailored towards execution on DOE HPC resources (e.g. Perlmutter, Summit, etc.) and is specialized for the type of simulation codes and workflows that are used in the community of laser-plasma acceleration.

Lehe, Remi↗
DOE OSTI2023

Low Energy Analysis of NuclEar Reactions v0.0.1

This is a lightweight, easy to use Python analysis package for analyzing public low-energy nuclear reaction data relevant to understanding basic nuclear processes, often of relevance to Solar Fusion, to improve our theoretical understanding of them, and enable connections with input from lattice QCD. It performs the analysis in a Bayesian Framework and supports Bayesian Model Averaging to provide a robust uncertainty quantification.

Walker-Loud, André↗
DOE OSTI2023

BERTeley v0.0.1

This python package allows for the grouping of scientific articles based on their content, and generates topic words based on the groups. Overall this program can take in a large collection of articles and create a profile of them, displaying what disciplines and areas are covered in the collection. Researchers will be able to input another researcher's published works and get a summary of the kind of content available in the publications.

Ushizima, Daniela↗
DOE OSTI2023

Training Data Regarding Optimal Power Flow For Efficient Machine Learning

The code is intended to take the data outputted by the MATPOWER OPF solver tools and restructures the data in a way that is best suited for machine learning. The variables are read and the desired values are taken and added to an array in the correct format. This array is then converted into a python array for future use. The code also utilizes MATPOWER's ability to construct different power flow scenarios and repeat them a chosen number of times. Each iteration will add a new line to the formatted array so that the final output is a matrix has a height equal to the number of repetition used.

Seidel, RachaelI↗
DOE OSTI2023

Noble-Lab/crema

Confidence Estimation for Mass Spectrometry Proteomics. Crema is an open-source Python tool that estimates the false discovery rate (FDR) of a set of peptide-spectrum matches that result from proteomics data analysis. Crema implements standard FDR estimation methods that are widely used within the field. This package is meant to be simple, easy to use, stand-along, and easy to maintain.

See, Donavan↗
DOE OSTI2021

NSU Hack-a-Thon Workshop v.2.0

SAND2021-6722 O This is a fitness tracking mobile application and server created to train college-level students to build full stack software. The front end is written using an open-source framework called Flutter. The API server is written using an open-source python framework called Flask. The app functions as a workout tracker and is intentionally partially functional for the purpose of training students during the live workshops hosted by Sandia's HR recruiting teams. 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.

Carpenter, Logan↗