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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data and scripts associated with the manuscript "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning"

This package contains the data and scripts used in "Encoding Diel Hysteresis and the Birch Effect in Dryland Soil Respiration Models through Knowledge-Guided Deep Learning" (Jiang et al., 2022). The data.zip file contains the flux tower and automated chamber observations used for developing the deep learning model for modeling soil respiration. The scripts.zip file contains the Jupyter notebooks and python scripts for preprocessing the data, training the deep learning models, and postprocessing the results. The src.zip contains the source code for training the deep learning model, performing mutual information analysis, and plotting functions. The trained_models.zip contains multiple folders used for hosting the trained deep-learning models and the associated soil respiration predictions. The whole process is performed using python. We include the REAMD.md to document the python package requirements.Soil respiration in dryland ecosystems is challenging to model due to its complex interactions with environmental drivers. Knowledge-guided deep learning provides a much more effective means of accurately representing these complex interactions than traditional Q10-based models. Mutual information analysis revealed that future soil temperature shares more information with soil respiration than past soil temperature, consistent with their clockwise diel hysteresis. We explicitly encoded diel hysteresis, soil drying, and soil rewetting effects on soil respiration dynamics in a newly designed Long Short Term Memory (LSTM) model. The model takes both past and future environmental drivers as inputs to predict soil respiration. The new LSTM model substantially outperformed three Q10-based models and the Community Land Model when reproducing the observed soil respiration dynamics in a semi-arid ecosystem. The new LSTM model clearly demonstrated its superiority for temporally extrapolating soil respiration dynamics, such that the resulting correlation with observational data is up to 0.7 while the correlations of both Q10-based models and the Community Land Model (CLM) are less than 0.4. Our results underscore the high potential for knowledge-guided deep learning to replace Q10-based soil respiration modules in Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Django Remote Submission

The Django Remote Submission (DRS) is a Django (Django, n.d.) application to manage long running job submission, including starting the job, saving logs, and storing results. It is an independent project available as a standalone pypi package (PyPi, n.d.). It can be easily integrated in any Django project. The source code is freely available as a GitHub repository (django-remote-submission, n.d.). To run the jobs in background, DRS takes advantage of Celery (Celery, n.d.), a powerful asynchronous job queue used for running tasks in the background, and the Redis Server (Redis, n.d.), an in-memory data structure store. Celery uses brokers to pass messages between a Django Project and the Celery workers. Redis is the message broker of DRS. In addition DRS provides real time monitoring of the progress of Jobs and associated logs. Through the Django Channels project (Channels, n.d.), and the usage of Web Sockets, it is possible to asynchronously display the Job Status and the live Job output (standard output and standard error) on a web page.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

easyaccess: Enhanced SQL command line interpreter for astronomical surveys

easyaccess is an enhanced command line interpreter and Python package created to facilitate access to astronomical catalogs stored in SQL Databases. It provides a custom interface with custom commands and was specifically designed to access data from the Dark Energy Survey Oracle database, including autocompletion of tables, columns, users and commands, simple ways to upload and download tables using csv, fits and HDF5 formats, iterators, search and description of tables among others. It can easily be extended to another surveys or SQL databases. The package was completely written in Python and support customized addition of commands and functionalities. Visit https://github.com/mgckind/easyaccess to view installation instructions, tutorials, and the Python source code for easyaccess.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

CMinx: A CMake Documentation Generator

This manuscript introduces CMinx, a program for generating application programming interface (API) documentation written in the CMake language, and CMake modules in particular. Since most of CMinx’s intended audience is comprised of C/C++ developers, CMinx is designed to operate similar to Doxygen, the de facto C/C++ API documentation tool. Specifically, developers annotate their CMake source with “documentation” comments, which are traditional CMake block comments starting with an extra “[” character. The documentation comments, written in reST, describe to the reader how the functions, parameters, and variables should be used. Running CMinx on the annotated source code generates reST files containing the API documentation. The reST files can then be converted into static websites with tools such as Sphinx or easily converted to another format via Pandoc.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

pvOps: a Python package for empirical analysis of photovoltaic field data

The purpose of pvOps is to support empirical evaluations of data collected in the field related to the operations and maintenance (O&M) of photovoltaic (PV) power plants. pvOps presently contains modules that address the diversity of field data, including text-based maintenance logs, current-voltage (IV) curves, and timeseries of production information. The package functions leverage machine learning, visualization, and other techniques to enable cleaning, processing, and fusion of these datasets. These capabilities are intended to facilitate easier evaluation of field patterns and extraction of relevant insights to support reliability-related decision-making for PV sites. The open-source code, examples, and instructions for installing the package through PyPI can be accessed through the GitHub repository.

14 SOLAR ENERGY↗
DOE OSTI2013

Parallel tools GUI framework-DOE SBIR phase I final technical report

Many parallel performance, profiling, and debugging tools require a graphical way of displaying the very large datasets typically gathered from high performance computing (HPC) applications. Most tool projects create their graphical user interfaces (GUI) from scratch, many times spending their project resources on simply redeveloping commonly used infrastructure. Our goal was to create a multiplatform GUI framework, based on Nokia/Digia’s popular Qt libraries, which will specifically address the needs of these parallel tools. The Parallel Tools GUI Framework (PTGF) uses a plugin architecture facilitating rapid GUI development and reduced development costs for new and existing tool projects by allowing the reuse of many common GUI elements, called “widgets.” Widgets created include, 2D data visualizations, a source code viewer with syntax highlighting, and integrated help and welcome screens. Application programming interface (API) design was focused on minimizing the time to getting a functional tool working. Having a standard, unified, and userfriendly interface which operates on multiple platforms will benefit HPC application developers by reducing training time and allowing users to move between tools rapidly during a single session. However, Argo Navis Technologies LLC will not be submitting a DOE SBIR Phase II proposal and commercialization plan for the PTGF project. Our preliminary estimates for gross income over the next several years was based upon initial customer interest and income generated by similar projects. Unfortunately, as we further assessed the market during Phase I, we grew to realize that there was not enough demand to warrant such a large investment. While we do find that the project is worth our continued investment of time and money, we do not think it worthy of the DOE's investment at this time. We are grateful that the DOE has afforded us the opportunity to make this assessment, and come to this conclusion.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Water Security Toolkit User Manual Version 1.2.

The Water Security Toolkit (WST) is a suite of open source software tools that can be used by water utilities to create response strategies to reduce the impact of contamination in a water distribution network . WST includes hydraulic and water quality modeling software , optimizati on methodologies , and visualization tools to identify: (1) sensor locations to detect contamination, (2) locations in the network in which the contamination was introduced, (3) hydrants to remove contaminated water from the distribution system, (4) locations in the network to inject decontamination agents to inactivate, remove, or destroy contaminants, (5) locations in the network to take grab sample s to help identify the source of contamination and (6) valves to close in order to isolate contaminate d areas of the network. This user manual describes the different components of WST , along w ith examples and case studies. License Notice The Water Security Toolkit (WST) v.1.2 Copyright c 2012 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000, there is a non-exclusive license for use of this work by or on behalf of the U.S. government. This software is distributed under the Revised BSD License (see below). In addition, WST leverages a variety of third-party software packages, which have separate licensing policies: Acro Revised BSD License argparse Python Software Foundation License Boost Boost Software License Coopr Revised BSD License Coverage BSD License Distribute Python Software Foundation License / Zope Public License EPANET Public Domain EPANET-ERD Revised BSD License EPANET-MSX GNU Lesser General Public License (LGPL) v.3 gcovr Revised BSD License GRASP AT&T Commercial License for noncommercial use; includes randomsample and sideconstraints executable files LZMA SDK Public Domain nose GNU Lesser General Public License (LGPL) v.2.1 ordereddict MIT License pip MIT License PLY BSD License PyEPANET Revised BSD License Pyro MIT License PyUtilib Revised BSD License PyYAML MIT License runpy2 Python Software Foundation License setuptools Python Software Foundation License / Zope Public License six MIT License TinyXML zlib License unittest2 BSD License Utilib Revised BSD License virtualenv MIT License Vol Common Public License vpykit Revised BSD License Additionally, some precompiled WST binary distributions might bundle other third-party executables files: Coliny Revised BSD License (part of Acro project) Dakota GNU Lesser General Public License (LGPL) v.2.1 PICO Revised BSD License (part of Acro project) i Revised BSD License Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of Sandia National Laboratories nor Sandia Corporation nor the names of its con- tributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IM- PLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL SANDIA CORPORATION BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUD- ING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ii Acknowledgements This work was supported by the U.S. Environmental Protection Agency through its Office of Research and Development (Interagency Agreement # DW8992192801). The material in this document has been subject to technical and policy review by the U.S. EPA, and approved for publication. The views expressed by individual authors, however, are their own, and do not necessarily reflect those of the U.S. Environmental Protection Agency. Mention of trade names, products, or services does not convey official U.S. EPA approval, endorsement, or recommendation. The Water Security Toolkit is an extension of the Threat Ensemble Vulnerability Assessment-Sensor Place- ment Optimization Tool (TEVA-SPOT), which was also developed with funding from the U.S. Environ- mental Protection Agency through its Office of Research and Development (Interagency Agreement # DW8992192801). The authors acknowledge the following individuals for their contributions to the devel- opment of TEVA-SPOT: Jonathan Berry (Sandia National Laboratories), Erik Boman (Sandia National Laboratories), Lee Ann Riesen (Sandia National Laboratories), James Uber (University of Cincinnati), and Jean-Paul Watson (Sandia National Laboratories). iii Acronyms ATUS American Time-Use Survey BLAS Basic linear algebra sub-routines CFU Colony-forming unit CVAR Conditional value at risk CWS Contamination warning system EA Evolutionary algorithm EDS Event detection system EPA U.S. Environmental Protection Agency EC Extent of Contamination ERD EPANET results database file GLPK GNU Linear Programming Kit GRASP Greedy randomized adaptive sampling process HEX Hexadecimal HTML HyperText markup language INP EPANET input file LP Linear program MC Mass consumed MILP Mixed integer linear program MIP Mixed integer program MSX Multi-species extension for EPANET NFD Number of failed detections NS Number of sensors NZD Non-zero demand PD Population dosed PE Population exposed PK Population killed TAI Threat assessment input file TCE Tailed-conditioned expectation TD Time to detection TEC Timed extent of contamination TEVA Threat ensemble vulnerability assessment TSB Tryptic soy broth TSG Threat scenario generation file TSI Threat simulation input file VAR Value at risk VC Volume consumed WST Water Security Toolkit YML YAML configuration file format for WST iv Symbols Notation Definition Example { , } set brackets { 1,2,3 } means a set containing the values 1,2, and 3. [?] is an element of s [?] S means that s is an element of the set S . [?] for all s = 1 [?] s [?] S means that the statement s = 1 is true for all s in set S . P summation P n i =1 s i means s 1 + s 2 + * * * + s n . \ set minus S \ T means the set that contains all those elements of S that are not in set T . %7C given %7C is used to define conditional probability. P ( s %7C t ) means the prob- ability of s occurring given that t occurs. %7C ... %7C cardinality Cardinality of a set is the number of elements of the set. If set S = { 2,4,6 } , then %7C S %7C = 3. v

Klise, Katherine A.↗
DOE OSTI2015

An Exact, Compressible One-Dimensional Riemann Solver for General, Convex Equations of State

This note describes an algorithm with which to compute numerical solutions to the one- dimensional, Cartesian Riemann problem for compressible flow with general, convex equations of state. While high-level descriptions of this approach are to be found in the literature, this note contains most of the necessary details required to write software for this problem. This explanation corresponds to the approach used in the source code that evaluates solutions for the 1D, Cartesian Riemann problem with a JWL equation of state in the ExactPack package [16, 29]. Numerical examples are given with the proposed computational approach for a polytropic equation of state and for the JWL equation of state.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

An Elastic Plastic Contact Model with Strain Hardening for the LAMMPS Granular Package

The following details the implementation of an analytical elastic plastic contact model with strain hardening for normal im pacts into the LAMMPS granular package. The model assumes that, upon impact, the co llision has a period of elastic loading followed by a period of mixed elastic plas tic loading, with contributions to each mechanism estimated by a hyperbolic seca nt weight function. This function is implemented in the LAMMPS source code as the pair style gran/ep/history. Preliminary tests, simulating the pouring of pure nickel spheres, showed the elastic/plastic model took 1.66x as long as similar runs using gran/hertz/history.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Documentation for MeshKit - Reactor Geometry (&mesh) Generator

This report gives documentation for using MeshKit’s Reactor Geometry (and mesh) Generator (RGG) GUI and also briefly documents other algorithms and tools available in MeshKit. RGG is a program designed to aid in modeling and meshing of complex/large hexagonal and rectilinear reactor cores. RGG uses Argonne’s SIGMA interfaces, Qt and VTK to produce an intuitive user interface. By integrating a 3D view of the reactor with the meshing tools and combining them into one user interface, RGG streamlines the task of preparing a simulation mesh and enables real-time feedback that reduces accidental scripting mistakes that could waste hours of meshing. RGG interfaces with MeshKit tools to consolidate the meshing process, meaning that going from model to mesh is as easy as a button click. This report is designed to explain RGG v 2.0 interface and provide users with the knowledge and skills to pilot RGG successfully. Brief documentation of MeshKit source code, tools and other algorithms available are also presented for developers to extend and add new algorithms to MeshKit. RGG tools work in serial and parallel and have been used to model complex reactor core models consisting of conical pins, load pads, several thousands of axially varying material properties of instrumentation pins and other interstices meshes.

Jain, Rajeev↗
DOE OSTI2015

KAYENTA: Theory and User's Guide

The physical foundations and domain of applicability of the Kayenta constitutive model are presented along with descriptions of the source code and user instructions. Kayenta, which is an outgrowth of the Sandia GeoModel, includes features and fitting functions appropriate to a broad class of materials including rocks, rock-like engineered materials (such as concretes and ceramics), and metals. Fundamentally, Kayenta is a computational framework for generalized plasticity models. As such, it includes a yield surface, but the term (3z(Byield(3y (Bis generalized to include any form of inelastic material response (including microcrack growth and pore collapse) that can result in non-recovered strain upon removal of loads on a material element. Kayenta supports optional anisotropic elasticity associated with joint sets, as well as optional deformation-induced anisotropy through kinematic hardening (in which the initially isotropic yield surface is permitted to translate in deviatoric stress space to model Bauschinger effects). The governing equations are otherwise isotropic. Because Kayenta is a unification and generalization of simpler models, it can be run using as few as 2 parameters (for linear elasticity) to as many as 40 material and control parameters in the exceptionally rare case when all features are used. For high-strain-rate applications, Kayenta supports rate dependence through an overstress model. Isotropic damage is modeled through loss of stiffness and strength.

58 GEOSCIENCES↗
DOE OSTI2015

ASC Tri-lab Co-design Level 2 Milestone Report 2015

In 2015, the three Department of Energy (DOE) National Laboratories that make up the Advanced Sci- enti c Computing (ASC) Program (Sandia, Lawrence Livermore, and Los Alamos) collaboratively explored performance portability programming environments in the context of several ASC co-design proxy applica- tions as part of a tri-lab L2 milestone executed by the co-design teams at each laboratory. The programming environments that were studied included Kokkos (developed at Sandia), RAJA (LLNL), and Legion (Stan- ford University). The proxy apps studied included: miniAero, LULESH, CoMD, Kripke, and SNAP. These programming models and proxy-apps are described herein. Each lab focused on a particular combination of abstractions and proxy apps, with the goal of assessing performance portability using those. Performance portability was determined by: a) the ability to run a single application source code on multiple advanced architectures, b) comparing runtime performance between \native" and \portable" implementations, and c) the degree to which these abstractions can improve programmer productivity by allowing non-portable implementation details to be hidden from the application developer. This report captures the work that was completed for this milestone, and outlines future co-design work to be performed by application developers, programming environment developers, compiler writers, and hardware vendors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

New Routes for the Production of Fission Products and Their Separation from the Source Material for the Development of Realistic Nuclear Forensic Debris

In order to fully characterize the NIF neutron spectrum, SAND-II-SNL software was requested/received from the Radiation Safety Information Computational Center. The software is designed to determine the neutron energy spectrum through analysis of experimental activation data. However, given that the source code was developed in Sparcstation 10, it is not compatible with current version of FORTRAN. Accounts have been established through the Lawrence Livermore National Laboratory’s High Performance Computing in order to access different compiles for FORTRAN (e.g. pgf77, pgf90). Additionally, several of the subroutines included in the SAND-II-SNL package have required debugging efforts to allow for proper compiling of the code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

ASC/NGC Gitlab Tutorial

This document summarizes version control systems and the source code it manages.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2016

Subcontract B613202# - Flang Error Handling

A Report that summarizes the potential for evolution of the PGI Fortran front-end source code into a form that offers error/warning messages similar in scope and quality to those provided by CLANG.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Detection with Enhanced Energy Windowing Phase I Report

This document reviews the progress of Phase I of the Detection with Enhanced Energy Windowing (DEEW) project. The DEEW project is the implementation of software incorporating an algorithm which reviews data generated by radiation portal monitors and utilizes advanced and novel techniques for detecting radiological and fissile material while not alarming on Naturally Occurring Radioactive Material. Independent testing indicated that the Enhanced Energy Windowing algorithm showed promise at reducing the probability of alarm in the stream of commerce compared to existing algorithms and other developmental algorithms, while still maintaining adequate sensitivity to threats. This document contains a brief description of the project, instructions for setting up and running the applications, and guidance to help make reviewing the output files and source code easier.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Who watches the watchers?: preventing fault in a fault tolerance library

The Scalable Checkpoint/Restart library (SCR) was developed and is used by researchers at Lawrence Livermore National Laboratory to provide a fast and efficient method of saving and recovering large applications during runtime on high-performance computing (HPC) systems. Though SCR protects other programs, up until June 2017, nothing was actively protecting SCR. The goal of this project was to automate the building and testing of this library on the varying HPC architectures on which it is used. Our methods centered around the use of a continuous integration tool called Bamboo that allowed for automation agents to be installed on the HPC systems themselves. These agents provided a way for us to establish a new and unique way to automate and customize the allocation of resources and running of tests with CMake’s unit testing framework, CTest, as well as integration testing scripts though an HPC package manager called Spack. These methods provided a parallel environment in which to test the more complex features of SCR. As a result, SCR is now automatically built and tested on several HPC architectures any time changes are made by developers to the library’s source code. The results of these tests are then communicated back to the developers for immediate feedback, allowing them to fix functionality of SCR that may have broken. Hours of developers’ time are now being saved from the tedious process of manually testing and debugging, which saves money and allows the SCR project team to focus their efforts towards development. Thus, HPC system users can use SCR in conjunction with their own applications to efficiently and effectively checkpoint and restart as needed with the assurance that SCR itself is functioning properly.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Domain Specific Language Support for Exascale Application Development

The University of Oregon has produced a Fortran parser based on an SDF (Syntax Definition Formalism) grammar. Primary partners in this effort were Rice University and Lawrence Livermore National Laboratory. The developed Fortran parser incorporates the full Fortran 2008 standard with extensions from the more recent document, TS 29113, “Further Interoperability of Fortran with C,” (to be included in the Fortran 2018 standard). In the final year of the project, the Fortran parser was updated to comply with the complete 2018 standard, as specified in the ISO/IEC document, JTC 1/SC 22/WG 5/N2146. The research explored in this project provided the expression of the Fortran grammar in SDF form for integration with the Rosebud DSL framework developed by Rice University. The Rosebud framework is a cohesive interface allowing modular DSL plugins to be developed independently and then distributed and combined in HPC applications. These plugins implement a form of generalized DSL which includes both concrete languages used to write application source code and abstract languages used within the compilation process. ROSE’s predefined DSL Building Blocks (for example the Fortran base language) are represented as abstract language plugins and DSL developers can use Rosebud to construct additional building blocks in the same way.

97 MATHEMATICS AND COMPUTING↗