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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Second Report of the Nuclear Data Subcommittee of the Nuclear Science Advisory Committee

The central importance of the nuclear data curated by the US Nuclear Data Program (USNDP) for clean energy generation, national security, nonproliferation, medical applications, and space exploration as well as basic science was described in a prior report issued by the DOE/NSF Nuclear Science Advisory Committee subcommittee on Nuclear Data (NSAC-ND) in September 2022. In this report, we present a set of fourteen (14) recommendations that will enhance and advance DOE-NP's stewardship of nuclear data. The first three recommendations focus on the existing core USNDP capabilities, namely: 1) Support the nuclear structure evaluation workforce to improve the currency, consistency, and accessibility of the Evaluated Nuclear Structure Data File (ENSDF); 2) Enhance nuclear reaction evaluation within the USNDP in support of the Evaluated Nuclear Data File (ENDF) through expansion of the workforce and integration of high-performance computing, automation, and machine learning and; 3) Continue atomic mass evaluation in support AME and NUBASE databases. This is followed by eight (8) recommendations representing new cross-cutting initiatives involving both measurement and evaluation to address outstanding nuclear data needs. These new initiatives require a highly trained, diverse workforce that includes personnel with expertise from both inside and outside the nuclear physics community from which evaluators have traditionally been recruited. As such, many of these initiatives are accomplished via a Topical Nuclear Data Collaborations (TNDC). A TNDC is made up of domestic and international stakeholders, subject matter and nuclear data experts, and nuclear data evaluators and features a workforce development plan to ensure that nuclear data evaluators maintain currency in the relevant applications and are seen as equity partners in the endeavor. These include: 1) Establish a coordinated effort to improve evaluation and modeling in nuclear astrophysics for stellar dynamics, multi-messenger astronomy and nucleosynthesis; 2) Initiate a TNDC to develop and maintain nuclear structure evaluation beyond discrete states, including nuclear level densities, photon strength functions and photonuclear data for improved reaction modeling, and exploring nuclear structure at finite temperature; 3) Create a TNDC to perform correlated fission data evaluation, including cross sections, fragment yields, v(A), v(E n ) for nuclear energy, national security, nonproliferation and basic science; 4) From a panel of subject matter experts to establish and annually update a roster of key decay data to nurture its accelerated dissemination including both measurement and evaluation for targeted high-value nuclides for national security, nonproliferation and medical applications; 5) Comprehensive, consistent neutron-induced structure and reaction data for nuclear energy, national security, nonproliferation and planetary nuclear spectroscopy; 6) Charged-particle stopping powers for detector design, space effects and ion beam therapy; 7) High-energy reactions for space exploration and medical nuclide production, and; 8) The creation of an infrastructure for open data and data preservation for use by the entire nuclear physics community. All told, these initiatives require approximately $6.5M increase in NP support of the USNDP in fiscal year 2023 dollars and would require at least 3-5 years to carry out due to the length of time needed to recruit and train new nuclear data researchers. This relatively modest investment would help ensure that the fruits of the nuclear data research carried out by DOE-NP and its collaborators would be brought to bear to address some of the most important needs of our nation and the world. To ensure effective execution of this plan, we present an overview of recruitment, training, and retention goals for the USNDP, the centerpiece of which is a mutually agreed upon code of conduct. Finally, we identify the facility and instrumentation needed to perform the recommended experimental activities. This includes a short review of target fabrication capabilities, reactors, neutron beam, light- and heavy-stable ion, gamma-ray, high-energy and radioactive ion beam facilities. Lastly, a more complete appendix of experimental facilities previously compiled is included with new input provided for 6 facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

LDA + $U_{sc}$ calculations of phase relations in FeO

Using the LDA + $U_{sc}$ method, we present calculation phase relations of iron monoxides involving five polytypes in multiple spin-state configurations. In this work, the Hubbard parameter $\textit{U}$ is determined self-consistently simultaneously with the occupation matrix and structures at arbitrary pressures. The Hubbard parameter strongly depends on pressure, structure, and spin state. Comparison with experimental structural data indicates the LDA + $U_{sc}$ can predict structure, compression curves, phase relations, and transition pressures very well for the insulating $\textit{B}$1 and $\textit{iB}$8 states. However, it requires additional calculations using the Mermin functional that includes the electronic entropic contribution to the free energy to obtain an $\textit{nB}$8 metallic state and a consistent $\textit{iB}$8 to $\textit{nB}$8 insulator to metal transition pressure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Distributed Hierarchical Contour Trees

Contour trees are a significant tool for data analysis as they capture both local and global variation. However, their utility has been limited by scalability, in particular for distributed computation and storage. We report a distributed data structure for storing the contour tree of a data set distributed on a cluster, based on a fan-in hierarchy, and an algorithm for computing it based on the boundary tree that represents only the superarcs of a contour tree that involve contours that cross boundaries between blocks. This allows us to limit the communication cost for contour tree computation to the complexity of the block boundaries rather than of the entire data set.

Carr, Hamish A↗
DOE OSTI2017

Efficient Delaunay Tessellation through K-D Tree Decomposition

Delaunay tessellations are fundamental data structures in computational geometry. They are important in data analysis, where they can represent the geometry of a point set or approximate its density. The algorithms for computing these tessellations at scale perform poorly when the input data is unbalanced. We investigate the use of k-d trees to evenly distribute points among processes and compare two strategies for picking split points between domain regions. Because resulting point distributions no longer satisfy the assumptions of existing parallel Delaunay algorithms, we develop a new parallel algorithm that adapts to its input and prove its correctness. We evaluate the new algorithm using two late-stage cosmology datasets. The new running times are up to 50 times faster using k-d tree compared with regular grid decomposition. Moreover, in the unbalanced data sets, decomposing the domain into a k-d tree is up to five times faster than decomposing it into a regular grid.

Morozov, Dmitriy↗
DOE OSTI2026

buhito

buhito is a Python library for graph analysis and machine learning. Graphs can represent networks with objects as nodes and their relationships as edges. buhito focuses on graphlet methods that study graphs through enumerating their component subgraphs to enable interpretable and fast models of complex systems. The package provides tools for different algorithmic designs for computing, analyzing, and applying graphlets to research problems such as machine learning, data compression, and anomaly detection in graph-structured data. A central feature is performing decomposition data analysis on graphs for machine learning models. Implemented in Python and built upon open-source scientific libraries such as NetworkX, NumPy, and SciPy, buhito provides high-performance methods for researchers exploring the mathematical and computational foundations of graphlet analysis applicable to systems of different sizes.

Pimonova, Yulia↗
DOE OSTI2024

Machine Learning-Based Identification of the Interface Regions for Coupling Local and Nonlocal Models

Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models and the accuracy of nonlocal models. However, the coupling process can be challenging, requiring expertise to identify the interface between local and nonlocal regions. Here, this study introduces a machine learning-based approach to automatically detect the regions in which the local and nonlocal models should be used. The method uses loading functions evaluated at grid points to decide the model selection at those points. Training of the networks is based on datasets provided by classes of loading functions for which reference coupling configurations are computed using accurate coupled solutions, where accuracy is measured in terms of the relative error between the solution to the coupling approach and the solution to the nonlocal model. We study two approaches that vary in data structure. The first, the full-domain input data approach, uses the entire load vector and outputs a complete label vector, performing a global classification. The second, a window-based approach, processes loads into windows and addresses the problem as a node-wise classification where each window's central point is classified individually. The classification problems are solved via deep learning algorithms based on convolutional neural networks. The performance of these approaches is studied on one-dimensional numerical examples using F1-scores and accuracy metrics. Notably, the windowing approach achieves an accuracy of 0.96 and an F1-score of 0.97, highlighting its potential to automate coupling processes effectively and enhance computational efficiency in material science applications.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

SeaWiFS technical report series. Volume 15: The simulated SesWiFS data set, version 2

This document describes the second version of the simulated SeaWiFS data set. A realistic simulated data set is essential for mission readiness preparations and can potentially assist in all phases of ground support for a future mission. The second version improves on the first version primarily through additional realism and complexity. This version incorporates a representation of virtually every aspect of the flight mission. Thus, it provides a high-fidelity data set for testing several aspects of the ground system, including data acquisition, data processing, data transfers, calibration and validation, quality control, and mission operations. The data set is constructed for a seven-day period, 25-31 March 1994. Specific features of the data set include Global Area coverage (GAC), recorded Local Area Coverage (LAC), and realtime High Resolution Picture Transmission (HRPT) data for the seven-day period. A realistic orbit, which is propagated using a Brouwer-Lyddane model with drag, is used to simulate orbit positions. The simulated data corresponds to the command schedule based on the orbit for this seven-day period. It includes total (at-satellite) radiances not only for ocean, but for land, clouds, and ice. The simulation also utilizes a high-resolution land-sea mask. It includes the April 1993 SeaWiFS spectral responses and sensor saturation responses. The simulation is formatted according to July 1993 onboard data structures, which include corresponding telemetry (instrument and spacecraft) data. The methods are described and some examples of the output are given. The instrument response functions made available in April 1993 have been used to produce the Version 2 simulated data. These response functions will change as part of the sensor improvements initiated in July-August 1993.

Hooker, Stanford B.↗
NASA NTRS2015

Developing a Business Intelligence Process for a Training Module in SharePoint 2010

Prior to this project, training information for the employees of the National Center for Critical Processing and Storage (NCCIPS) was stored in an array of unrelated spreadsheets and SharePoint lists that had to be manually updated. By developing a content management system through a web application platform named SharePoint, this training system is now highly automated and provides a much less intensive method of storing training data and scheduling training courses. This system was developed by using SharePoint Designer and laying out the data structure for the interaction between different lists of data about the employees. The automation of data population inside of the lists was accomplished by implementing SharePoint workflows which essentially lay out the logic for how data is connected and calculated between certain lists. The resulting training system is constructed from a combination of five lists of data with a single list acting as the user-friendly interface. This interface is populated with the courses required for each employee and includes past and future information about course requirements. The employees of NCCIPS now have the ability to view, log, and schedule their training information and courses with much more ease. This system will relieve a significant amount of manual input and serve as a powerful informational resource for the employees of NCCIPS in the future.

Schmidtchen, Bryce↗
DOE OSTI2019

Striped Data Analysis Framework

Traditionally, High Energy data analysis is based on the model where data are stored in files and analyzed by running multiple analysis processes, each reading one or more of the data files. This process involves repeated data reduction step, which produces smaller files, which is time consuming and leads to data duplication. We propose an alternative approach to data storage and analysis, based on the Big Data technologies. The idea is to store each element of data once and only once in a distributed scalable database and analyze data by reading only "interesting" pieces of data from the database. To make this approach possible, we developed columnar Striped Data Representation Format as the basis of the framework. Traditional columnar approach allows for efficient analysis of complex data structures. While keeping all the benefits of columnar data representation, striped mechanism goes further by enabling efficient parallelization of computations and flexible dist ribution of data analysis. The framework includes scalable and elastic data storage, compute and user analysis backend components. The framework uses off-the shelf web services and data caching technologies as the compute/data co-location mechanism. Flexible architecture allows the framework run in the cloud using container technologies. The framework offers Python/Jupyter as the user analysis backend platform, but can also run in command line or batch mode. In the article, we will present the results of the FNAL-LDRD-2016-032 FNAL LDRD project, the design, implementation, features and performance characteristics of the Striped Data Analysis Framework.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Recommended Nuclear Structure and Decay Data for A = 200 Isobars

Evaluated nuclear structure and decay data for all nuclei with mass number A = 200 ( 200 Os, 200 Ir, 200 Pt, 200 Au, 200 Hg, 200 Tl, 200 Pb, 200 Bi, 200 Po, 200 At, 200 Rn, and 200 Fr), are presented. All available experimental data are compiled and evaluated, and best values for level and gamma-ray energies, quantum numbers, lifetimes, gamma-ray intensities and transition probabilities, as well as other nuclear properties, are recommended. Inconsistencies and discrepancies that exist in the literature are discussed. A number of computer codes (https://www-nds.iaea.org/public/ensdf pgm/) developed by members of the NSDD network were used during the evaluation process. In conclusion, this work supersedes the earlier evaluation by F.G. Kondev and S. Lalkovski (2007Ko42), published in Nuclear Data Sheets 108, 1471 (2007).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Neural-network accelerated coupled core-pedestal simulations with self-consistent transport of impurities and compatible with ITER IMAS

pedestal structure, current profile, and plasma equilibrium physics has been developed and tested against a DIII-D discharge. Here, key features of the achieved core-pedestal coupled workflow are its ability to account for the transport of impurities in the plasma self-consistently, as well as its use of machine learning accelerated models for the pedestal structure and for the turbulent transport physics. Notably, the coupled workflow is implemented within the OMFIT framework, and makes use of the ITER integrated modeling and analysis suite (IMAS) data structure for exchanging data among the physics codes that are involved in the simulations. Such technical advance has been facilitated by the development of a new numerical library named OMAS.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1978

Spacelab uplink/downlink data flow and formats

The results of an analysis of the Spacelab (SL) data uplink/downlink structure and those data system elements associated with the support of this data flow are presented. Specific objectives of this report are to present the results of the following analyses: (1) operations of the SL high rate multiplexer, including format structure, data rates, format combinations, format switching, etc.; (2) operations of SL data recorders to include the definition of modes, data rates and forms; (3) operations of the high rate demultiplexer as described above; (4) potential experiment data formats defining formatting parameters to be considered in decommutation analysis; (5) SL computer input/output (I/O) decommutation channels, including the definition of structure, quantity and use of this I/O data; (6) detailed requirements of the data quality monitoring philosophy for this function.

Kandefer, F.↗
DOE OSTI2018

Cryo-EM structure of substrate-bound human telomerase holoenzyme

Telomerase adds telomeric repeats to chromosome ends to balance incomplete replication. Telomerase regulation is implicated in cancer, aging and other human diseases, but progress towards telomerase clinical manipulation is hampered by the lack of structural data. Here we present the cryo-electron microscopy structure of substrate-bound human telomerase holoenzyme at subnanometer resolution, describing two flexibly RNA-tethered lobes: the catalytic core with telomerase reverse transcriptase (TERT) and conserved motifs of telomerase RNA (hTR), and an H/ACA ribonucleoprotein (RNP). In the catalytic core, RNA encircles TERT, adopting a well-ordered tertiary structure with surprisingly limited protein-RNA interactions. The H/ACA RNP lobe comprises two sets of heterotetrameric H/ACA proteins and one Cajal body protein, TCAB1, representing a pioneering structure of a large eukaryotic family of ribosome and spliceosome biogenesis factors. Our findings provide a structural framework for understanding human telomerase disease mutations and represent an important step towards telomerase-related clinical therapeutics

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

ATDM LANL FleCSI: Topology and Execution Framework

FleCSI is a compile-time configurable C++ framework designed to support multi-physics application development. As such, FleCSI attempts to provide a very general set of infrastructure design patterns that can be specialized and extended to suit the needs of a broad variety of solver and data requirements. This means that FleCSI is potentially useful to many different ECP projects. Current support includes multidimensional mesh topology, mesh geometry, and mesh adjacency information, n-dimensional hashed-tree data structures, graph partitioning interfaces, and dependency closures (to identify data dependencies between distributed-memory address spaces). FleCSI introduces a functional programming model with control, execution, and data abstractions that are consistent with state-of-the-art task-based runtimes such as Legion and Charm++. The model also provides support for fine-grained, data-parallel execution with backend support for runtimes such as OpenMP and C++17. The FleCSI abstraction layer provides the developer with insulation from the underlying runtimes, while allowing support for multiple runtime systems, including conventional models like asynchronous MPI. The intent is to give developers a concrete set of user-friendly programming tools that can be used now, while allowing flexibility in choosing runtime implementations and optimizations that can be applied to architectures and runtimes that arise in the future. This project is essential to the ECP Ristra Next-Generation Code project, part of ASC ATDM, because it provides a hierarchically parallel programming model that is consistent with the design of modern system architectures, but which allows for the straightforward expression of algorithmic parallelism in a portably performant manner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Asynchronous Graphs (Final Report)

The most performant massive-scale distributed graph analytic engines were essentially unusable for anything but the analytic they were tuned to solve. The project team had previously developed HavoqGT and used this project to demonstrate it as a flexible graph framework, aiming to extend algorithmic capabilities, improve underlying data structures, and develop novel communication paradigms to enable new data analysis missions. Highlights of the technology developed include (a) a new distributed communication paradigm dubbed You’ve Got Mail (YGM), (b) a performant exact and fuzzy HPC graph pattern matching approach, and (c) a distributed biased random walk simulation capability. The project was successful on several measurable fronts, including advancing computer science (reflected by publishing 14 peer-reviewed research papers yielding 4 publication awards and over 16 conference presentations), contributing tangibly to LLNL recruiting rare talent, and obtaining follow-on funding. These technologies were transferred to several FY19-20 programmatic projects and will continue to be leveraged by new research projects for several years.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

BASIN-3D Data Integration for Selected ARM Data Field Campaign Report

The purpose of this data services request was to demonstrate integration of the Atmospheric Radiation Measurement (ARM) User Facility’s “met” datastreams with time series data from other earth science data sources using the BASIN-3D data synthesis software tool. BASIN-3D is an open-source Python library that enables researchers to integrate data across configured public and private data sources. It provides a common query language for researchers to request measurement locations and time series data based on specified locations, variables, time period, statistics, aggregation, and data quality. BASIN-3D acquires the data that match the query from each configured data source and translates the results into harmonized vocabularies, thus reducing researchers' data-wrangling effort. In addition, because the queries are executed on demand, researchers can easily regenerate their synthesized data sets as new data and/or data updates become available, eliminating one-off data products. BASIN-3D can output data using a variety of different data structures for end-user applications including Python pandas data frames and hdf5 output formats.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Evaluation of a data dictionary system

The usefulness was investigated of a data dictionary/directory system for achieving optimum benefits from existing and planned investments in computer data files in the Data Systems Development Branch and the Institutional Data Systems Division. Potential applications of the data catalogue system are discussed along with an evaluation of the system. Other topics discussed include data description, data structure, programming aids, programming languages, program networks, and test data.

Driggers, W. G.↗