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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 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Xarray Climate Data Analysis Tools

xCDAT is an extension of xarray for climate data analysis on structured grids. It serves as a modern successor to the Community Data Analysis Tools (CDAT) library. Xarray is an "open source project and Python package that introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays, which allows for more intuitive, more concise, and less error-prone user experience. Xarray includes a large and growing library of domain-agnostic functions for advanced analytics and visualization with these data structures" (source: https://xarray.dev/). The goal of xCDAT is to provide generalizable features and utilities for simple and robust analysis of climate data. xCDAT's design philosophy is focused on reducing the overhead required to accomplish certain tasks in xarray. Some key xCDAT features are inspired by or ported from the core CDAT library, while others leverage powerful libraries in the xarray ecosystem (e.g., xESMF and cf_xarray) to deliver robust APIs.

Vo, Tom↗
DOE OSTI2022

Data-centric framework for crystal structure identification in atomistic simulations using machine learning

Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are required to aid interpretation. These have historically been challenging to construct, requiring significant intuition and effort. Here we propose an alternative framework for a fundamental structural characterization task: classifying atoms according to the crystal structure to which they belong. Our approach is data-centric and favors the employment of Machine Learning over heuristic rules of classification. A group of data-science tools and simple local descriptors of atomic structure are employed together with an efficient synthetic training set. We also introduce the first standard and publicly available benchmark data set for evaluation of algorithms for crystal-structure classification. Further, it is demonstrated that our data-centric framework outperforms all of the most popular heuristic methods—especially at high temperatures when lattices are the most distorted—while introducing a systematic route for generalization to new crystal structures. Moreover, through the use of outlier detection algorithms our approach is capable of discerning between amorphous atomic motifs (i.e., noncrystalline phases) and unknown crystal structures, making it uniquely suited for exploratory materials synthesis simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data for: "Inorganic synthesis-structure maps in zeolites with machine learning..."

Repository containing all the data necessary to reproduce the paper "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". This content includes: raw data for all results in the article; figures appearing in the main paper; and Jupyter notebooks and scripts containing the code for analyzing/reproducing the results.

Schwalbe Koda, Daniel↗
DOE OSTI2022

RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning

Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a ‘living data resource.’ Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2018

The Network for the Detection of Atmospheric Composition Change (NDACC): History, Status and Perspectives

The Network for the Detection of Atmospheric Composition Change (NDACC) is an international global network of more than 90 stations making high-quality measurements of atmospheric composition that began official operations in 1991 after 5 years of planning. Apart from sonde measurements, all measurements in the network are performed by ground-based remote-sensing techniques. Originally named the Network for the Detection of Stratospheric Change (NDSC), the name of the network was changed to NDACC in 2005 to better reflect the expanded scope of its measurements. The primary goal of NDACC is to establish long-term databases for detecting changes and trends in the chemical and physical state of the atmosphere (mesosphere, stratosphere, and troposphere) and to assess the coupling of such changes with climate and air quality. NDACC's origins, station locations, organizational structure, and data archiving are described. NDACC is structured around categories of ground-based observational techniques (sonde, lidar, microwave radiometers, Fourier-transform infrared, UV-visible DOAS (differential optical absorption spectroscopy)-type, and Dobson-Brewer spectrometers, as well as spectral UV radiometers), timely cross-cutting themes (ozone, water vapour, measurement strategies, cross-network data integration), satellite measurement systems, and theory and analyses. Participation in NDACC requires compliance with strict measurement and data protocols to ensure that the network data are of high and consistent quality. To widen its scope, NDACC has established formal collaborative agreements with eight other cooperating networks and Global Atmosphere Watch (GAW). A brief history is provided, major accomplishments of NDACC during its first 25 years of operation are reviewed, and a forward-looking perspective is presented.

lidar↗
NASA NTRS1974

Estimation of structural parameters from dynamic test data

Parameters for a lumped linear model approximating a distributed elastic structure are determined from dynamic test data comprised of several mode shapes and frequencies. Measurement errors, nonlinear response, and nonmeasurable quantities such as mode slope components are accommodated. Some mass and stiffness parameters may be known accurately, whereas the remainder are to be estimated. The method entails minimizing a quadratic function of the difference between corresponding modes and frequencies of the theoretical model and the test specimen. This technique was applied to some actual vibration test data, and the special techniques that are required to overcome convergence problems are described.

Hall, B. M.↗
DOE OSTI2026

Final technical report for DE-SC0022255: Discovering Physically Meaningful Structures from Climate Extreme Data

The past two decades have witnessed natural disasters and extreme weather events that affect millions of people. At the same time, the data volume from high-resolution climate models, satellite, in-situ and ground-based measurements have substantially increased to petabyte scales. These new and readily accessible datasets create the previously missing pipeline required for scientific machine learning (ML) and therefore new opportunities for improved understanding and prediction capability of climate extreme events. This project developed a deep latent variable model framework to discover physically meaningful hidden structures from high-dimensional, spatiotemporal climate extreme data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Experimental Unevaluated Nuclear Data List (XUNDL)

Contains nuclear structure and decay data from published articles. Data include nuclear level properties, such as energy, half-life, decay modes, spin and parity, as well as energy and intensity for radiation types such as gammas, alphas, betas and neutrons. For articles published in Physical Review C and the European Physical Journal, compilation takes place during the article submission process. Data from approximately 250 articles are added per year. All the compilation effort is supported by the US Nuclear Data Program and coordinated by the National Nuclear Data Center at Brookhaven National Laboratory, which is also responsible for its web dissemination.

Basic Nuclear Research, nuclear structure and deca↗
DOE OSTI2015

Optimizing Irregular Applications for Energy and Performance on the Tilera Many-core Architecture

Optimizing applications simultaneously for energy and performance is a complex problem. High performance, parallel, irregular applications are notoriously hard to optimize due to their data-dependent memory accesses, lack of structured locality and complex data structures and code patterns. Irregular kernels are growing in importance in applications such as machine learning, graph analytics and combinatorial scientific computing. Performance- and energy-efficient implementation of these kernels on modern, energy efficient, multicore and many-core platforms is therefore an important and challenging problem. We present results from optimizing two irregular applications { the Louvain method for community detection (Grappolo), and high-performance conjugate gradient (HPCCG) { on the Tilera many-core system. We have significantly extended MIT's OpenTuner auto-tuning framework to conduct a detailed study of platform-independent and platform-specific optimizations to improve performance as well as reduce total energy consumption. We explore the optimization design space along three dimensions: memory layout schemes, compiler-based code transformations, and optimization of parallel loop schedules. Using auto-tuning, we demonstrate whole node energy savings of up to 41% relative to a baseline instantiation, and up to 31% relative to manually optimized variants.

irregular applications, energy opttimization, many↗
DOE OSTI2017

Exploring performance and energy tradeoffs for irregular applications: A case study on the Tilera many-core architecture

High performance, parallel applications with irregular data accesses are becoming a critical workload class for modern systems. In particular, the execution of such workloads on emerging many-core systems is expected to be a significant component of applications in data mining, machine learning, scientific computing and graph analytics. However, power and energy constraints limit the capabilities of individual cores, memory hierarchy and on-chip interconnect of such systems, thus leading to architectural and software trade-os that must be understood in the context of the intended application’s behavior. Irregular applications are notoriously hard to optimize given their data-dependent access patterns, lack of structured locality and complex data structures and code patterns. We have ported two irregular applications, graph community detection using the Louvain method (Grappolo) and high-performance conjugate gradient (HPCCG), to the Tilera many-core system and have conducted a detailed study of platform-independent and platform-specific optimizations that improve their performance as well as reduce their overall energy consumption. To conduct this study, we employ an auto-tuning based approach that explores the optimization design space along three dimensions - memory layout schemes, GCC compiler flag choices and OpenMP loop scheduling options. We leverage MIT’s OpenTuner auto-tuning framework to explore and recommend energy optimal choices for different combinations of parameters. We then conduct an in-depth architectural characterization to understand the memory behavior of the selected workloads. Finally, we perform a correlation study to demonstrate the interplay between the hardware behavior and application characteristics. Using auto-tuning, we demonstrate whole-node energy savings and performance improvements of up to 49:6% and 60% relative to a baseline instantiation, and up to 31% and 45:4% relative to manually optimized variants.

auto tuning↗
DOE OSTI2016

Magnetic structure of Co(Cr{sub 0.925}Fe{sub 0.075}){sub 2}O{sub 4}

We report results of neutron diffraction (ND) measurements on Co(Cr{sub 0.925}Fe{sub 0.075}){sub 2}O{sub 4} compound and determination of its magnetic structure. ND data at 90 K shows ferrimagnetic structure which is consistent with the bulk magnetization transition temperature, T{sub C} (~ 120 K). Appearance of additional peaks at 20 K coincides with the view that a magnetostructural transition occurs at T{sub S} (~ 26 K) in bulk magnetization of the sample.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

bokeh streaming [SWR 19-08]

Simple examples of bokeh streaming data. The plots are intended to be generic and able to infer some of the plot data from the structure of the data sent. The receiver accepts zlib compressed pickled python dictionaries.

Simpson, Jeffery↗
DOE OSTI2022

Data Catalog Project - A Browsable, Searchable, Metadata System

Modern experiments are typically conducted by large, extended, where researchers rely on other team members to produce much of the data they use. The experiments record very large numbers of measurements which can be difficult for users to find, access and understand. We are developing a system for users to annotate their data products with structured metadata, providing data consumers with a discoverable, browsable data index. Machine understandable metadata captures the underlying semantics of the recorded data, which can then be consumed by both programs, and interactively by users. Collaborators can use these metadata to select and understand recorded measurements.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS1974

Mechanical properties of eutectic bonded boron aluminum

Results are presented of extensive testing recently completed on boron-aluminum composites to determine mechanical property data for structural design and analysis. Average property data and associated test methods are given for unidirectional and cross plied laminates at room temperature and 600 F. Results from crippling tests of hat-shaped stringers and mechanical fastener joint tests of laminates containing titanium interleaves are also presented. Compared to aluminum which has approximately same density, tests show that boron-aluminum loaded in compression is 4 1/2 times stronger and 3 1/2 times stiffer. When loaded in tension it is 2 times stronger and 3 times stiffer.

Bohlmann, R. E.↗
NASA NTRS1976

Formulation of detailed consumables management models for the development (preoperational) period of advanced space transportation system. Volume 2: Consumables data base workbook

The consumables characteristic data associated with the performance of the mission activities required by the mission planning processor are defined to calculate the consumables requirements. The activity data is defined in terms of discrete time periods having a distinct rate for each consumable required to support the performance of a given operation. The data is structured in a series of consumable data worksheets for each activity that includes a profile of its operations and the rate of each consumable required to support the given activity. The data worksheets provide for the uniform specification of consumables data, allows for the ready identification of the consumables affected by a given activity, and facilitates the updating process. An activity and the data that must be included in the data worksheets are defined and an example of its use and application the consumables data requirements for the performance of the EVA are presented.

Zamora, M. A.↗
NASA NTRS1977

Consumables data base workbook: Formulation of consumables management models

Activity consumables data specifications and data applications are presented. The data are structured in a series of "Consumable Data Worksheets" for each activity that includes a profile of its operations and the rate of each consumable required to support the given activity. The data worksheets provide for the uniform specification of consumables data, allows for the ready identification of the consumables affected by a given activity, and facilitates the updating process. An activity is defined and the data that must be included in the data worksheets are specified. An example of its use and application is given, i.e. consumables data requirements for the performance of the EVA. The consumables data for the activities currently identified for the shuttle spacecraft are included. The consumables data sources are identified and information to facilitate the maintenance process is detailed.

Zamora, M. A.↗