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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Livermore Computed Tomography Input/Output library

Read, write, and connect visualizers to data for LLNL radiography and CT tools (e.g. LTT: https://softwarelicensing.llnl.gov/product/livermore-tomography-tools-ltt).

Vardar-Irrgang, MichaelE [Lawrence Livermore Natio↗
DOE OSTI2023

Pickaxe: a Python library for the prediction of novel metabolic reactions

Abstract Background Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user’s application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation. Results Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset. Conclusion Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The Virtual Test Bed (VTB) repository: a library of multiphysics reference reactor models using NEAMS tools

With the next generation of nuclear reactors under development, modeling and simulation (MS) tools are being developed by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in order to support their design, licensing, and future operation. Mirroring the physical test beds currently under construction (i.e., EBR-II and ZPPR), the Virtual Test Bed (VTB) was launched by the National Reactor Innovation Center (NRIC) in collaboration with NEAMS to support the advanced reactor community. This collaborative effort, which involves multiple teams at both Idaho National Laboratory and Argonne National Laboratory aims to use NEAMS tools to model a wide range of reactor designs. Those models are automatically tested to ensure their continued functionality as the tools are further developed. Examples are extensively documented, each acting as a tutorial for applying the relevant NEAMS tools to that reactor design. Currently, five advanced reactor types (with a total of eight specific design variants) are simulated by a variety of different models. These models range from steady-state, core multiphysics simulations to integrated plant analysis during loss-of flow transients. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

ParMOO: A Python library for parallel multiobjective simulation optimization

A multiobjective optimization problem (MOOP) is an optimization problem in which multiple objectives are optimized simultaneously. The goal of a MOOP is to find solutions that describe the tradeoff between these (potentially conflicting) objectives. Such a tradeoff surface is called the Pareto front. Real-world MOOPs may also involve constraints – additional hard rules that every solution must adhere to. In a multiobjective simulation optimization problem, the objectives are derived from the outputs of one or more computationally expensive simulations. Such problems are ubiquitous in science and engineering.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Non-Cartesian Coordinate Systems in the Portage Library

In order to be more broadly useful, the Portage framework needs to be able to support non-Cartesian coordinate systems. This document will lay out the most important details of implementing non-Cartesian coordinate systems in Portage. The most immediate need for this feature is integration with the EAP code base. Towards that end, the focus will be on certain curvilinear coordinate systems. However, this will provide a framework that can be used to implement other coordinate systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Classified library critical in Lab’s Annual Assessment of weapons to U.S. President. National Security Resource Center’s collections are the foundation to stockpile confidence

One of the most important accomplishments every year at LANL is a letter sent by the Laboratory Director that ultimately reaches the President of the United States. The subject is the current state of the weapons for which the Lab is responsible. Known as the Annual Assessment letter, it is a culmination of nearly 14 months’ worth of work and the contributions of more than 1,000 Lab staff members. Its classified contents come from the Lab’s Annual Assessment, which is an approximately 100-page document evaluating the safety, security and effectiveness of the stockpile.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

PAO 1.0: A Python Library for Adversarial Optimization

PAO is a Python-based package for Adversarial Optimization. The goal of this package is to provide a general modeling and analysis capability for bilevel, trilevel and other multilevel optimization forms that express adversarial dynamics. PAO integrates two different modeling abstractions: 1. Algebraic models extend the modeling concepts in the Pyomo algebraic modeling language to express problems with an intuitive algebraic syntax. Thus, we expect that this modeling abstraction will commonly be used by PAO end-users. 2. Compact models express objective and constraints in a manner that is typically used to express the mathematical form of these problems (e.g. using vector and matrix data types). PAO denes custom Multilevel Problem Representations (MPRs) that simplify the implementation of solvers for bilevel, trilevel and other multilevel optimization problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Preparation of Nuclear Data Libraries for Web Release [Slides] and Tutorial for Generating Correlated Random Samples and Propagation of Uncertainty [Slides]

The first presentation discuses moving distribution of nuclear data to an online platform which allows ore frequent nuclear data updates, greater ease of acquiring nuclear data, and user flexibility in what nuclear data to download. The second presentation illustrates uncertainties without correlation, uncertainties without correlation with negative samples, uncertainties with correlation, uncertainties with a χ-like constraint, uncertainties with a Σ tot -like constraint, sampling using different distributions, and dealing with negative eigenvalues in a covariance matrix.

42 ENGINEERING↗