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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

l3d2vtk: An MCNPTools Utility to Enable LNK3DNT File Visualization & Post-Processing

This paper describes recent work to develop and incorporate a utility program into the MCNPTools package that enables LNK3DNT file visualization & post-processing. A LNK3DNT file is a binary Cartesian, cylindrical, or spherical structured mesh file that describes material and material relative density distributions by mesh voxel. This mesh file format is principally used by the PARTISN discrete-ordinates radiation-transport code. However, the MCNP ® Monte Carlo radiation-transport code can use LNK3DNT files to specify geometry, and the MCNP code can produce LNK3DNT files representing a user-defined region of the MCNP constructive solid geometry (CSG). Because of the use of LNK3DNT files as MCNP input and output and with other computer codes, improving the ability to visualize and/or quantitatively post-process LNK3DNT files benefits end users. In particular, it is helpful to visually verify that the material and density distributions are as expected. Also, an analyst can orient viewers with respect to other in formation, e.g., by co-plotting a voxelized representation of MCNP CSG with mesh tally and/or particle track results. However, until now, the only capability to interactively interrogate LNK3DNT files is through a 2-D slice plotter provided by the Monte Carlo Application ToolKit (MCATK), which is not currently distributed outside of Los Alamos National Laboratory. The utility created as part of this work converts LNK3DNT binary files to an ASCII XML-based StructuredMesh VTK (.vts) file. The geometries supported are 1-D spherical and cylindrical ( r ), 1-D Cartesian ( x ), 2-D cylindrical ( r-z and r-θ ) and Cartesian ( x-y ), and 3-D cylindrical ( r-z-θ ) and Cartesian ( x-y-z ). This file format is chosen to minimize external dependencies (e.g., on an HDF5 library) and to provide a file that can be interrogated by the user in a text editor or standard visualization applications (e.g., ParaView or VisIt). However, other file formats may provide more utility such as XDMF, which can use HDF5 and is discussed further as something to explore as future work. This paper is organized as follows: a brief introduc tion to the MCNPTools library and utility programs is given, the LNK3DNT-to-VTK (l3d2vtk) utility functionality is de scribed, and then an example post-processing workflow using the ParaView application is given.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

rcsb-api : Python Toolkit for Streamlining Access to RCSB Protein Data Bank APIs

The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.

FAIR principles↗
DOE OSTI2021

A software package for plasma facing component analysis and design: the Heat flux Engineering Analysis Toolkit (HEAT)

The engineering limits of plasma facing components (PFCs) constrain the allowable operational space of tokamaks. Poorly managed heat fluxes that push the PFCs beyond their limits not only degrade core plasma performance via elevated impurities, but can also result in PFC failure due to thermal stresses or melting. Simple axisymmetric assumptions fail to capture the complex interaction between 3D PFC geometry and 2D or 3D plasmas. This results in fusion systems that must either operate with increased risk or reduce PFC loads, potentially through lower core plasma performance, to maintain a nominal safety factor. High precision 3D heat flux predictions are necessary to accurately ascertain the state of a PFC given the evolution of the magnetic equilibrium. A new code, the Heat flux Engineering Analysis Toolkit (HEAT), has been developed to provide high precision 3D predictions and analysis for PFCs. HEAT couples many otherwise disparate computational tools together into a single open source python package. Magnetic equilibrium, engineering CAD, finite volume solvers, scrape off layer plasma physics, visualization, high performace computing, and more, are connected in a single web-based user interface. Linux users may use HEAT without any software prerequisites via an appImage. This manuscript introduces HEAT, discusses the software architecture, presents first HEAT results, and outlines physics modules in development.

divertor physics↗
NASA NTRS2016

MONTE: the Next Generation of Mission Design and Navigation Software

The Mission Analysis, Operations and Navigation Toolkit Environment (MONTE) is an astrodynamic toolkit produced by the Mission Design and Navigation Software Group at the Jet Propulsion Laboratory. It provides a single integrated environment for all phases of deep space and Earth orbiting missions. Capabilities include: trajectory optimization and analysis, operational orbit determination, flight path control, and 2D/3D visualization. MONTE is presented to the user as an importable Python language module. This allows a simple but powerful user interface via CLUI or script. In addition, the Python interface allows MONTE to be used seamlessly with other canonical scientific programming tools such as SciPy, NumPy, and Matplotlib. MONTE is the prime operational orbit determination software for all JPL navigated missions.

Optimization↗
DOE OSTI2022

The DSA Toolkit Shines Light Into Dark and Stormy Archives

Web archive collections are created with a particular purpose in mind. A curator selects seeds, or original resources, which are then captured by an archiving system and stored as archived web pages, or mementos. The systems that build web archive collections are often configured to revisit the same original resource multiple times. This is incredibly useful for understanding an unfolding news story or the evolution of an organization. Unfortunately, over time, some of these original resources can go off-topic and no longer suit the purpose for which the collection was originally created. They can go off-topic due to web site redesigns, changes in domain ownership, financial issues, hacking, technical problems, or because their content has moved on from the original topic. Even though they are off-topic, the archiving system will still capture them, thus it becomes imperative to anyone performing research on these collections to identify these off-topic mementos. Hence, we present the Off-Topic Memento Toolkit, which allows users to detect off-topic mementos within web archive collections. The mementos identified by this toolkit can then be separately removed from a collection or merely excluded from downstream analysis. The following similarity measures are available: byte count, word count, cosine similarity, Jaccard distance, Sørensen-Dice distance, Simhash using raw text content, Simhash using term frequency, and Latent Semantic Indexing via the gensim library. We document the implementation of each of these similarity measures. We possess a gold standard dataset generated by manual analysis, which contains both off-topic and on-topic mementos. Using this gold standard dataset, we establish a default threshold corresponding to the best F1 score for each measure. We also provide an overview of potential future directions that the toolkit may take.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

ASCENDS: Advanced Data SCiENce Toolkit for Non-Data Scientists

Data science capabilities are becoming important more and more. Even though there exist many data science and machine learning tools, they are not intuitive for many traditional scientists who are not familiar with programming. ASCENDS provides easy and intuitive interfaces (command-line interface, graphical user interface) to users so they can use data science capabilities without having to understand programming language. ASCENDS performs data visualization, machine learning model training, storing and prediction (regression problem), automatic hyperparameter tuning, and supports various machine learning models including random forest, kernel ridge, nearest neighbor, neural network, etc. Users can use standard CSV (comma separated values) format data set and investigate the feature columns that are highly correlated to a target column; further, they can learn ML models, then store and use them for predictions. The tool is supposed to be generically applicable so that it can be used for many different applications.

Sangkeun, MattLee↗
NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Biologically Relevant Space Radiation Data

RadLab, a new component of the NASA Open Science Data Repository (OSDR), is a platform built upon a database of radiation data relevant to space biology. RadLab provides visual and programmatic interfaces for interrogation of its database, as well as a submission process for inclusion of data from investigators. The RadLab application programming interface (API) implements a request syntax enabling users to retrieve data filtered by various combinations of parameters (detector type, location, direction, timespan, etc), which are delivered in machine-readable text formats, ready to be ingested by downstream analysis pipelines; while the graphical user interface (GUI) provides easy means to iteratively modify query parameters and incorporates a number of standard analyses and visualizations (time series plots, geospatial visualizations, detector comparison). Investigators from many countries, including US, Russia, Japan, Canada, the Czech Republic, Germany, Hungary, and Italy, have committed to provide data from their instruments located on the ISS; RadLab will also include data from other spacecraft in LEO (e.g., the Space Shuttle, the Mir space station), BLEO (e. g. BioSentinel, Mars Orbiter, among others), and on other celestial bodies (e. g. Chang’e 4, Curiosity). The first release of RadLab has been made available to the public. Once fully operational, RadLab will provide a comprehensive and ever-growing compendium of space radiation data, facilitating straightforward access to multiple types of readings and enabling space biology researchers to perform intercomparisons of detectors and to determine the radiation environment of research missions, both via programmatic retrieval of these data and via the graphical analysis toolkit; as well as a user-friendly submission portal for ingesting data from space agencies and research institutions. Radiation scientists will be able to use RadLab to gain a deeper understanding of the space radiation environment for future human space exploration. The RadLab Working Group has been formed to foster close collaborations among data contributors and users, to identify data sources, to put in place standards for data normalization, to guide the development of features of the analysis toolkit, to establish the use of RadLab in space radiation biology research, and eventually to provide a forum for discussing relevant research issues that can take advantage of RadLab's capabilities.

radiation↗
NASA NTRS2018

WebGeocalc and Cosmographia: Modern Tools to Access OPS SPICE Data

For more than two decades navigation and other ancillary data from most US and international planetary science missions have been packaged using "SPICE" (Spacecraft, Planet, Instrument, Camera-matrix, Events) system data files (a.k.a. SPICE kernels) and, in conjunction with SPICE Toolkit software used by scientists and engineers to compute observation geometry in various ground system tools ranging from mission planning and analysis applications to data production pipelines to science analysis tools. The traditional way for accessing SPICE data is by downloading necessary SPICE kernels to a user’s workstation, installing the SPICE Toolkit software available from NAIF, and writing an application calling APIs from the SPICE Toolkit library to compute numeric geometric parameters of interest. While this approach did and still does provide the greatest flexibility in implementing geometric computations of interest, it proved to be complicated for users with little programming abilities, required data to be always copied to the users’ workstations, and lacked any out-of-the-box visualization capabilities. To address these shortcomings NAIF developed the WebGeocalc (WGC) tool and extended the publicly available Cosmographia program to use SPICE. Employing these two new tools in mission operations enables easier access to SPICE computations and SPICE-based visualizations for a wider variety of mission personnel.

Semenov, Boris V.↗
DOE OSTI2021

pnnl/DPVPROT

Distributed photovoltaics protection software toolkit The code package includes several important functions for design and analysis of new grid protection schemes, for scenarios of distributed photovoltaic (DPV) generation approaching 100% of the peak feeder load, i.e., high-penetration DPV. Convert distribution feeder models, used by planning engineers, to electromagnetic transient (EMT) models that produce waveforms for advanced relay algorithms. Visualize the results of protection scheme operation. Tools to determine distance relay settings on distribution systems. Tools to predict the performance of time-domain, incremental distance relay algorithms

McDermott, Tom↗
NASA NTRS2014

The Use of NASA near Real-time and Archived Satellite Data to Support Disaster Assessment

With support from a NASA's Applied Sciences Program, The Short‐term Prediction Research and Transition (SPoRT) Center has explored a variety of techniques for utilizing archived and near real‐time NASA satellite data to support disaster assessment activities. MODIS data from the NASA Land Atmosphere Near Real‐time Capability for EOS currently provides true color and other imagery for assessment and potential applications including, but not limited to, flooding, fires, and tornadoes. In May 2013, the SPoRT Center developed unique power outage composites using the VIIRS Day/Night Band to represent the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF‐5 tornado that occurred on May 20. Pre‐event imagery provided by the NASA funded Web‐Enabled Landsat Data project offer a basis of comparison for monitoring post-disaster recovery efforts. Techniques have also been developed to generate products from higher resolution imagery from the recently available International Space Station SERVIR Environmental Research and Visualization System instrument. Of paramount importance is to deliver these products to end users expeditiously and in formats compatible with Decision Support Systems (DSS). Delivery techniques include a Tile Map Service (TMS) and a Web Mapping Service (WMS). These mechanisms allow easy integration of satellite products into DSS's, including the National Weather Service's Damage Assessment Toolkit for use by personnel conducting damage surveys. This poster will present an overview of the developed techniques and products and compare the strengths and weaknesses of the TMS and WMS.

McGrath, Kevin M.↗
NASA NTRS1991

Scalable Performance Environments for Parallel Systems

As parallel systems expand in size and complexity, the absence of performance tools for these parallel systems exacerbates the already difficult problems of application program and system software performance tuning. Moreover, given the pace of technological change, we can no longer afford to develop ad hoc, one-of-a-kind performance instrumentation software; we need scalable, portable performance analysis tools. We describe an environment prototype based on the lessons learned from two previous generations of performance data analysis software. Our environment prototype contains a set of performance data transformation modules that can be interconnected in user-specified ways. It is the responsibility of the environment infrastructure to hide details of module interconnection and data sharing. The environment is written in C++ with the graphical displays based on X windows and the Motif toolkit. It allows users to interconnect and configure modules graphically to form an acyclic, directed data analysis graph. Performance trace data are represented in a self-documenting stream format that includes internal definitions of data types, sizes, and names. The environment prototype supports the use of head-mounted displays and sonic data presentation in addition to the traditional use of visual techniques.

Reed, Daniel A.↗
NASA NTRS2015

Python-Based Scientific Analysis and Visualization of Precipitation Systems at NASA Marshall Space Flight Center

At NASA Marshall Space Flight Center (MSFC), Python is used several different ways to analyze and visualize precipitating weather systems. A number of different Python‐based software packages have been developed, which are available to the larger scientific community. The approach in all these packages is to utilize pre‐existing Python modules as well as to be object‐oriented and scalable. The first package that will be described and demonstrated is the Python Advanced Microwave Precipitation Radiometer (AMPR) Data Toolkit, or PyAMPR for short. PyAMPR reads geolocated brightness temperature data from any flight of the AMPR airborne instrument over its 25‐year history into a common data structure suitable for user‐defined analyses. It features rapid, simplified (i.e., one line of code) production of quick‐look imagery, including Google Earth overlays, swath plots of individual channels, and strip charts showing multiple channels at once. These plotting routines are also capable of significant customization for detailed, publication‐ready figures. Deconvolution of the polarization‐varying channels to static horizontally and vertically polarized scenes is also available. Examples will be given of PyAMPR's contribution toward real‐time AMPR data display during the Integrated Precipitation and Hydrology Experiment (IPHEx), which took place in the Carolinas during May‐June 2014. The second software package is the Marshall Multi‐Radar/Multi‐Sensor (MRMS) Mosaic Python Toolkit, or MMM‐Py for short. MMM‐Py was designed to read, analyze, and display three‐dimensional national mosaicked reflectivity data produced by the NOAA National Severe Storms Laboratory (NSSL). MMM‐Py can read MRMS mosaics from either their unique binary format or their converted NetCDF format. It can also read and properly interpret the current mosaic design (4 regional tiles) as well as mosaics produced prior to late July 2013 (8 tiles). MMM‐Py can easily stitch multiple tiles together to provide a larger regional or national picture of precipitating weather systems. Composites, horizontal and vertical crosssections, and combinations thereof are easily displayed using as little as one line of code. MMM‐Py can also write to the native MRMS binary format, and sub‐sectioning of tiles (or multiple stitched tiles) is anticipated to be in place by the time of this meeting. Thus, MMM‐Py also can be used to power the creation of custom mosaics for targeted regional studies. Overlays of other data (e.g., lightning observations) are easily accomplished. Demonstrations of MMM‐Py, including the creation of animations, will be shown. Finally, Marshall has done significant work to interface Python‐based analysis routines with the U.S. Department of Energy's Py‐ART software package for radar data ingest, processing, and analysis. One example of this is the Python Turbulence Detection Algorithm (PyTDA), an MSFC‐based implementation of the National Center for Atmospheric Research (NCAR) Turbulence Detection Algorithm (NTDA) for the purposes of convective‐scale analysis, situational awareness, and forensic meteorology. PyTDA exploits Py‐ART's radar data ingest routines and data model to rapidly produce aviation‐relevant turbulence estimates from Doppler radar data. Work toward processing speed optimization and better integration within the Py‐ART framework will be highlighted. Python‐based analysis within the Py‐ART framework is also being done for new research related to intercomparison of ground‐based radar data with satellite estimates of ocean winds, as well as research on the electrification of pyrocumulus clouds.

Lang, Timothy J.↗
NASA NTRS2009

Automated Verification of Design Patterns with LePUS3

Specification and [visual] modelling languages are expected to combine strong abstraction mechanisms with rigour, scalability, and parsimony. LePUS3 is a visual, object-oriented design description language axiomatized in a decidable subset of the first-order predicate logic. We demonstrate how LePUS3 is used to formally specify a structural design pattern and prove ( verify ) whether any JavaTM 1.4 program satisfies that specification. We also show how LePUS3 specifications (charts) are composed and how they are verified fully automatically in the Two-Tier Programming Toolkit.

Nicholson, Jonathan↗
DOE OSTI2021

NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

Automation and optimization of chemical systems require well-informed decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. Here we introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian optimization algorithms and supports both automated an d human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

The Aerosol Model Benchmarking Repository: A toolkit for model intercomparison

The Aerosol Model Benchmarking Repository and Standards (AMBRS) project was initiated to provide tools and to establish community standards for benchmarking aerosol models. This report describes a set of open-source tools for building, running, and analyzing aerosol box model simulations in a standardized framework. The framework consists of three core components: AMBuilder, a CMake-based build system that compiles supported models consistently; AMBRS, a Python module that defines unified numerical experiments and executes them with aligned inputs; and PyParticle, an aerosol analysis package that standardizes output, computes diagnostics, and visualizes simulation results. Together, these tools enable reproducible intercomparison of aerosol schemes and support process-level evaluation of how model simplifications affect predictions of size distributions, cloud condensation nuclei activity, and other relevant properties relevant for the Earth-Energy system. Beyond its role in benchmarking, AMBRS provides a platform for studying aerosol processes across scales and can be used to generate training data for AI/ML applications in support of a broader hierarchical aerosol modeling strategy.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Biologically Relevant Space Radiation Data

RadLab, a new component of the NASA Open Science Data Repository (OSDR), comprises a database of radiation measurements relevant to space biology, and visual and programmatic interfaces for interrogation and retrieval of these data. The attributes of data available through RadLab include spacecraft, types of radiation sensing instruments, locations within the spacecraft (e.g. modules of the ISS), associated celestial bodies, trajectories, and spacecraft coordinates. The application programming interface (API) implements a request syntax for retrieval of timestamped data filtered by various combinations of such attributes; the graphical user interface (GUI) extends this functionality with visualizations, such as spacecraft schematics, time series plots, geospatial visualizations, and provides easy means to iteratively refine search parameters, inspect the data on the fly, and download target subsets of these data. The release of RadLab currently available to the public contains datasets provided by US and international collaborators and focuses on data recorded on the ISS. Investigators from multiple countries, including the US, Canada, Germany, Bulgaria, Hungary, Italy, Japan, Russia and the Czech Republic, have committed to provide data from their instruments in and beyond low Earth orbit; RadLab will also soon expand to include past (e.g. Shuttle and Mir) and future (e.g. Artemis) data. RadLab will provide a comprehensive, dynamic compendium of space radiation data, enabling the scientific community to perform analyses of data from multiple detectors and to determine the radiation environment of research missions and experiments, both via programmatic retrieval of these data and through the graphical analysis toolkit. The RadLab Working Group has been formed to foster collaborations among data contributors and users, to identify data sources, to put in place standards for data harmonization, and to guide the development of the platform, with the goal to establish the use of RadLab in space radiation research and to advance our understanding of the space radiation environment in human habitats.

radiation↗
DOE OSTI2022

EXSCLAIM!

Due to recent improvements in image resolution and acquisition speed, materials microscopy is experiencing an explosion of published imaging data. The standard publication format, while sufficient for traditional data ingestion scenarios where a select number of images can be critically examined and curated manually, is not conducive tolarge-scale data aggregation or analysis, hindering data sharing and reuse. Most images in publications are presented as components of a larger figure with their explicit context buried in the main body or caption text, so even if aggregated, collections of images with weak or no digitized contextual labels have limited value. To solve the problem of curating labeled microscopy data from literature, we introduce the EXSCLAIM! Python toolkit for the automatic EXtraction, Separation, and Caption-based natural Language Annotation of IMages from scientific literature. The software is implemented through a three part pipeline: the JournalScraper, which searches the web and downloads figures and captions based on a user provided query, the CaptionDistributor, which separates caption text based on the subfigure each portion of the caption refers to, and the FigueSeparator, which separates figures into component subfigures and extracts other visual information. Also included is a Django user interface for exploring the resulting dataset.

CHAN, MARIA↗
DOE OSTI2022

Hardware-Accelerated Ray Tracing of CAD-Based Geometry for Monte Carlo Radiation Transport

Monte Carlo radiation transport (MCRT) methods have been used to simulate radiation environments for many decades by tracking individual particles through a model to accumulate statistical information. MCRT geometry is historically formed using the constructive solid geometry (CSG). Recently, significant work has been performed to support simulations using computer-aided design (CAD)-based tessellated surfaces to support highly complex geometries. Ray tracing acceleration data structures from the rendering and visualization community are applied to accelerate particle tracking in CAD-based models. Despite these efforts, CSG representations provide the superior performance in surface intersection operations during particle flight. Concurrently, pseudo Monte Carlo methods have become prevalent in rendering applications to support more realistic models for scattering media, motivating innovations that are advantageous for MCRT simulations. Finally, the authors’ work extends these innovations by employing Intel’s Embree ray tracing kernel within a geometry toolkit for Monte Carlo to improve the simulation performance using CAD-based models by factors of 1.5 to 2.

42 ENGINEERING↗