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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Data Point Averaging for Computational Fluid Dynamics Data

A system and method for generating fluid flow parameter data for use in aerodynamic heating analysis. Computational fluid dynamics data is generated for a number of points in an area on a surface to be analyzed. Sub-areas corresponding to areas of the surface for which an aerodynamic heating analysis is to be performed are identified. A computer system automatically determines a sub-set of the number of points corresponding to each of the number of sub-areas and determines a value for each of the number of sub-areas using the data for the sub-set of points corresponding to each of the number of sub-areas. The value is determined as an average of the data for the sub-set of points corresponding to each of the number of sub-areas. The resulting parameter values then may be used to perform an aerodynamic heating analysis.

Norman, David, Jr.↗
NASA NTRS2016

Data Point Averaging for Computational Fluid Dynamics Data

A system and method for generating fluid flow parameter data for use in aerodynamic heating analysis. Computational fluid dynamics data is generated for a number of points in an area on a surface to be analyzed. Sub-areas corresponding to areas of the surface for which an aerodynamic heating analysis is to be performed are identified. A computer system automatically determines a sub-set of the number of points corresponding to each of the number of sub-areas and determines a value for each of the number of sub-areas using the data for the sub-set of points corresponding to each of the number of sub-areas. The value is determined as an average of the data for the sub-set of points corresponding to each of the number of sub-areas. The resulting parameter values then may be used to perform an aerodynamic heating analysis.

Norman, Jr., David↗
NASA NTRS1977

Contouring randomly spaced data

Computer program using triangulation contouring technique contours data points too numerous to fit into rectangular grid. Using random access procedures, program can handle up to 56,000 data points and provides up to 20 contour intervals for multiple number of parameters.

Kibler, J. F.↗
NASA NTRS1991

Rectilinear partitioning of irregular data parallel computations

New mapping algorithms for domain oriented data-parallel computations, where the workload is distributed irregularly throughout the domain, but exhibits localized communication patterns are described. Researchers consider the problem of partitioning the domain for parallel processing in such a way that the workload on the most heavily loaded processor is minimized, subject to the constraint that the partition be perfectly rectilinear. Rectilinear partitions are useful on architectures that have a fast local mesh network. Discussed here is an improved algorithm for finding the optimal partitioning in one dimension, new algorithms for partitioning in two dimensions, and optimal partitioning in three dimensions. The application of these algorithms to real problems are discussed.

Nicol, David M.↗
NASA NTRS1989

Computational results for the effects of external disturbances on transition location of bodies of revolution from subsonic to supersonic speeds and comparisons with experimental data

Computational experiments have been performed for a few configurations in order to investigate the effects of external flow disturbances on the extent of laminar flow and wake drag. Theoretical results have been compared with experimental data for the AEDC cone, for Mach numbers from subsonic to supersonic, and for both free flight and wind tunnel environments. The comparisons have been found to be very satisfactory, thus establishing the utility of the present method for the design and development of laminar flow configurations and for the assessment of wind tunnel data. In addition, results of calculations concerning the effects of unit Reynolds numbers on transition are presented. In addition to the AEDC cone, computations have been performed for an ogive body of revolution at zero angle of attack and supersonic Mach numbers. Results are presented for transition Reynolds number and wake drag for external disturbances corresponding to free air and the test section of the AEDC-VKF tunnel. These results have been found to compare quite well with wind tunnel data for cases when surface suction is applied as well as when suction is absent.

Goradia, S. H.↗
DOE OSTI2021

Online data analysis and reduction: An important co-design motif for extreme-scale computers

A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.

Data Analysis↗
DOE OSTI2021

Computational Data-Driven Materials Discovery

Machine learning from large materials datasets enables accelerated materials discovery. Currently, the most accessible way to generate uniform, well curated, voluminous datasets is by the application of high-throughput first principles computations. In this work, we present the guiding principles of using computational data and machine learning to drive new materials discovery.

36 MATERIALS SCIENCE↗
DOE OSTI2024

DICER: Data Intensive Computing Environment and Runtime for Evaluating Unprecedented Scale of Geospatial-Temporal Human Mobility Data

With the significant increase in sources and volume of human mobility data through commercial data vendors as well as microsimulation of cities, the scale of geospatial-temporal data to analyze and assess for mobility characterization has grown to the level of Big Data. There are mobility related commercial organizations deploying scalable computing, but often the system architecture, workflow, and intermediate processing components are not fully disclosed in relevant scope. Current research literature has a notable lack of studies demonstrating architectures and workflows for human mobility analytics that are implemented on a TeraByte scale of geospatial-temporal data. In this context, this paper presents a hyperscale-level system solution named DICER (Data Intensive Computing Environment and Runtime) for processing and analytics of geospatial-temporal data at big data scale. Although the cluster computing architecture of DICER with Apache Spark job running on Kubernetes cluster is not new, there are innovations in the workflow, hierarchical processing logic, and a wide range of intermediate preprocessing and mobility metrics calculation. We have performed case studies to validate the effectiveness of DICER system solution by performing detailed analytics and assessment of human mobility microsimulation output at three different scopes and scale, including a usecase with 16.97 TeraByte and 259.2 Billion rows of data. In addition, we have presented another case study of utilizing DICER to perform the same mobility processing and comparative analytics on large-scale commercially available geospatial-temporal data. All these case studies validate the efficiency and usefulness of DICER in computing population mobility characteristics from geospatial-temporal trajectory data at an unprecedented scale (not only just data volume, but also combination of: number of user entities, temporal frequency, spatial resolution, data duration).

De, Debraj↗