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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Radiological Anomaly Detection And Identification (RADAI) v1.0

The Radiological Anomaly Detection and Identification (RADAI) software package is a python library for implementing, training, and storing algorithms that detect and identify anomalies in gamma-ray spectra. The library defines a general framework for implementing detection (binary) and identification (classification) algorithms, objects to encapsulate the results of analyses, a variety of temporal filtering tools that can be used in constructing algorithms, and conceptual design that allows easy reading and writing of algorithms (and their time dependent state). In addition to this framework, the library includes implementations of a variety of algorithms from the scientific literature including: gross-counts k-sigma, SPRT, N-SCRAD, Region of Interest, and Censored Energy Window. The implementation of these algorithms within the RADAI package was done to facilitate user-initiated training and configuration to by applied to different gamma-ray detector types. Finally, benchmarked and synthetic datasets will be made available for standardized algorithm characterization with corresponding utilities in the RADAI package for data access and processing.

Joshi, Tenzing↗
DOE OSTI2021

FunFact v0.6

FunFact is a Python package for functional factorization of matrices and higher-order tensors

Tang, Yu-Hang↗
DOE OSTI2021

Wrapper for the optimization of cross-section generation in OpenMC

The "Wrapper for the optimization of cross-section generation in OpenMC" is Python-based software that runs the open-source Monte Carlo code OpenMC (https://docs.openmc.org/en/stable/ ) to generate cross-sections for any reactor geometry. The wrapper then optimizes those cross sections. The optimization aims to choose an energy group structure and a scattering representation that maximize accuracy with respect to continuous-energy results while avoiding significant computational expense. It then outputs these cross-sections in an ISOXML format readable by the Idaho National Lab code suite MOOSE (Olin William Calvin, Mark D DeHart, “Architecture for the Performance of Nuclear Fuel Depletion Calculations”, Idaho National Laboratory report, November 2019). The expected use-cases of this software include: -finding the best group structure and scattering representation for a specific reactor -testing the appropriateness of energy group structures for different reactor types -comparing energy group structures and scattering representations to each other -generating cross sections for use in deterministic codes, including ones found in the MOOSE suite The example reactor geometry included in this release is a generic reactor design, not based on any reactor in existence or in development. It was fabricated for the sole purpose of being a “testbed-geometry” upon which to develop this tool. Since the tool is designed to be generic, the nature of the test geometry is not very important, however, it is valuable to include as an example for users who are unfamiliar with developing reactor geometries for OpenMC.

Kreher, Miriam↗
DOE OSTI2021

METRICS FOR INTERCOMPARISON OF REMAPPING ALGORITHMS (MIRA)

The MIRA project comprises of a set of Python drivers that enable rigorous intercomparison of remapping algorithms by providing the infrastructure for computing the numerical metrics of interest for ESM. This work is funded by the SciDAC Coupling Approaches for Next-Generation Architectures (CANGA) project.

SciDAC↗
DOE OSTI2021

RadTraQ (Radar Tracking and Quality Toolkit

RadTraQ is a python library housing routines related to assessing and monitoring the quality of a radar's quality and calibration. This library is a work in progress, currently housing scripts to plot radar CFADs, plot and analyze corner reflector scans, calculate noise floors, create cloud masks, and plot up average profiles for comparisons.

THEISEN, ADAMK↗
DOE OSTI2021

Bulb-style Kaplan Turbine - Run-of-the-river (ror) Hydro Governor And Turbine Model

This software package includes hydro governor and turbine models developed in Simulink (Mathworks Inc.) and RSCAD (RTDS Technologies) for low-head bulb-style Kaplan turbine hydropower plants. The model developed in Simulink has been designed and tuned to match the governor-turbine response for a real-world hydropower unit owned by Idaho Falls Power (IFP). The Simulink and RSCAD models enable real-time testing in a hardware-in-the-loop (HIL) implementation using OPAL-RT and RTDS digital real-time simulators, respectively. An automatic initialization for dynamic simulation has also been integrated to each model in both Simulink and RSCAD. The RSCAD initialization involves a novel python-based interfacing for automatic modification of the initial conditions in the model. The parameters of these models can be tuned to match other hydropower plants in this class.

Alam, SMShafiul↗
DOE OSTI2021

OptML

OptML is an open-source Python package that provides modeling and solution capabilities for optimization problems with embedded machine learning models. OptML is a collaborative open-source project that includes advanced algorithms from the academic community for efficient solution of these problems. Sandia contributions to this package include the development of interfaces between these algorithms and Pyomo. 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. SAND2021-10861 O

Jalving, Jordan↗
DOE OSTI2021

spdlayers

Symmetric Positive Definite (SPD) enforcement layers for PyTorch. Regardless of the input, the output of these layers will always be a SPD tensor! The `Cholesky` layer uses a cholesky factorization to enforce SPD, and the `Eigen` layer uses an eigendecomposition to enforce SPD. Both layers take in some tensor of shape `[batch_size, input_shape]` and output a SPD tensor of shape`[batch_size, output_shape, output_shape]`. The relationship between input and output is defined by the following. ```python input_shape = sum([i for i in range(output_shape + 1)]) ``` The layers have no learnable parameters, and merely serve to transform a vector space to a SPD matrix space.

Jekel, CharlesF.↗
DOE OSTI2021

CausalFS

SAND2021-4723 O CausalFS is a Python package that evaluates causal relationships between dataset features and the metric to be predicted. 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.

Nguyen, Bernard↗
DOE OSTI2021

WISDEM® v3.4.1(Wind Plant Integrated System Design and Engineering Model) [SWR-14-05]

The Wind Plant Integrated System Design and Engineering Model (WISDEM®) is a set of models for assessing overall wind plant cost of energy (COE). The models use wind turbine and plant cost and energy production as well as financial models to estimate COE and other wind plant system attributes. WISDEM® is accessed through Python, is built using OpenMDAO, and uses several sub-models that are also implemented within OpenMDAO. These sub-models can be used independently but they are required to use the overall WISDEM® turbine design capability. Please install all of the pre-requisites prior to installing WISDEM® by following the directions below. For additional information about the NWTC effort in systems engineering that supports WISDEM® development, please visit the official NREL systems engineering for wind energy website. https://www.nlr.gov/wind/systems-engineering

Dykes, Katherine↗
DOE OSTI2020

Multi-genome Phage Annotation Toolkit and Evaluator

Summary: To address the need for improved tools for annotation and comparative genomics of bacteriophage genomes, we developed multiPhATE2. As an extension of the multiPhATE code, multiPhATE2 includes comparative genomics codes for gene matching among sets of input bacteriophage genomes, and scales well to large input data sets due to incorporation of multiprocessing in the functional annotation and comparative genomics subsystems. Furthermore, additional search algorithms and databases have been added to the functional annotation subsystem. MultiPhATE2 was implemented in Python 3.7, and runs as a command-line code under Linux or MAC-OS.

Kimbrel, JeffreyA.↗
DOE OSTI2022

LANL Contributions to pyiron: An integrated development environment for computational materials science

To support and accelerate the development of simulation protocols in atomistic modelling, we introduce an integrated development environment (IDE) for computational materials science called pyiron (http://pyiron.org). The pyiron IDE combines a web based source code editor, a job management system for build automation, and a hierarchical data management solution. The core components of the pyiron IDE are pyiron objects based on an abstract class, which links application structures such as atomistic structures, projects, jobs, simulation protocols and computing resources with persistent storage and an interactive user environment. The simulation protocols within the pyiron IDE are constructed using the Python programming language. To highlight key concepts of this tool as well as to demonstrate its ability to simplify the implementation and testing of simulation protocols we discuss two applications. In these examples we show how pyiron supports the whole life cycle of a typical simulation, seamlessly combines ab initio with empirical potential calculations, and how complex feedback loops can be implemented. While originally developed with focus on ab initio thermodynamics simulations, the concepts and implementation of pyiron are general thus allowing to employ it for a wide range of simulation topics.

Janssen, Jan↗
DOE OSTI2022

Link Parser Library

Link Parser Library is a Python library for parsing HTTP Link header format. Link Parser extracts information about relationships to other resources from Link Header and translates it into an easy to use Json structure.

Balakireva, Lyudmila↗
DOE OSTI2022

DistGANS- Distributed Generative Adversarial Neural Networks

DistGANs is a Python package to perform distributed training of conditional generative adversarial neural networks for multi-class labeled image data. DistGANs partitionins the training data according to data labels, and enhances scalability by performing a parallel training where multiple generators are concurrently trained, each one of them focusing on a single data label.

Lupo Pasini, Massimiliano [Oak Ridge National Lab.↗
DOE OSTI2022

PIO Tools

This is a set of utilities for reading and manipulating PIO files written in python and C++. These files are intended to be building blocks for other scripts / programs that will use them to do great things. The PIO format itself is described in LA-UR-05-7425 at page 102 and embodied in code LA-CC-05-052. In addition to reading the raw PIO files, the bundled utilities will expand variable that use a compressed sparse row notation

Swaminarayan, Sriram↗
DOE OSTI2022

ccp137/SUGAR

This package (SUrface-wave Grader with ARtificial intelligence or SUGAR) automatically assigns a quality score to surface-wave seismograms (SAC format) using a trained artificial neural network model (included). Specifically, the python script 01_apply_ann.py calculates probability scores for a list of SAC files. You may consider seismograms with probability scores larger than 0.5 as acceptable data. Note no scores will be given to seismograms that do not pass an initial check (e.g., insufficient number of data points). See https://doi.org/10.1002/essoar.10507941.3 for more details.

Chai, Chengping [Oak Ridge National Lab. (ORNL), O↗
DOE OSTI2022

CMEC Module Manager

The CMEC module manager package is designed install and update CMEC modules (https://cmec.llnl.gov/). CMEC modules are independent climate model analysis packages. Individual scripts are provided in the module manager that outline the installation process for each module in a robust way, and a wrapper python script is provided that lets users install a chosen CMEC package with a single command.

Ordonez, AnaC↗