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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Smart Spectral Matching (SSM)

Smart Spectral Matching (SSM) catalogs spectroscopic data and, within the platform, investigates subtle attributes of spectral signatures from Raman and infrared spectroscopic data and enables statistical identification of connections between underlying structural units and spectroscopic information, particularly in fuel cycle materials that are amorphous or a mixture of several phases. Catalogs spectroscopic data, provides UIs for machine learning training either via JupyterHub for notebooks or domain scientist-specific views, machine learning and catalog REST API Python client libraries, and ability to identify features in files uploaded using pre-trained machine learning models. This is a "service-based" architecture with multiple applications represented by each repository in the group https://github.com/smart-spectral-matching

McDonnell, Marshall [Oak Ridge National Lab. (ORNL↗
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

HOMP (Hybrid Operation and Maintenance Platform) [SWR-22-81]

The HOMP software module is contained within NREL's Hybrid Optimization and Performance Platform (HOPP) at this URL: https://github.com/NREL/HOPP/tree/feature/HOMP. HOMP is a python-based software module which can be used concurrently with the Hybrid Optimization and Performance Platform (HOPP) to model, simulate, and optimize degradation, reliability, and operations and maintenance of hybrid power plant components. The program currently models lithium ion battery, PEM electrolyzer, wind turbine, and solar PV array components, which enables users to understand the design and operation tradeoffs of hybrid power plants. For instance, HOMP enables users to answer questions like: how large does a battery bank need to be to achieve optimal charge/discharge rates for performance and reliability? How does plant design change for different objectives (e.g., profit versus resilience)? Under what conditions is it better to produce electricity versus hydrogen? How do we optimally control hybrid power plants to reduce downtime and increase performance?

Clark, Caitlyn↗
DOE OSTI2023

pnnl/mlprotein

The molecular graphics program, PyMol, was employed to produce all images in the training/validation/testing datasets. A PyMol script identified the clusters of interest from a list of all proteins containing 1) Fe (III) and Fe (II) ion and 2) FeS, respectively. A PyMol script and a Python webscraper were also used to classify [2Fe-2S] cofactors into three classes: standard, Rieske, and other and to identify iron atoms with four cysteine residues. We also used the pyMol script to rotate amino acids for training

Oostrom, Marjolein↗
DOE OSTI2023

ase

[Atomic Simulation Environment](https://wiki.fysik.dtu.dk/ase/): A Python library for working with atoms

Mazeau, EmilyJ [Oak Ridge National Lab. (ORNL), Oa↗
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↗