Search NASA⌕ Search

SEARCH · Search NASA

Results for “Large data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Self-Adjusting Hash Tables for Embedded Flight Applications

A common practice in computer science to associate a value with a key is to use a class of algorithms called a hash-table. These algorithms enable rapid storage and retrieval of values based upon a key. This approach assumes that many keys will need to be stored immediately. A new set of hash-table algorithms optimally uses system resources to ideally represent keys and values in memory such that the information can be stored and retrieved with a minimal amount of time and space. These hash-tables support the efficient addition of new entries. Also, for large data sets, the look-up time for large data-set searches is independent of the number of items stored, i.e., O(1), provided that the chance of collision is low.

James, Mark↗
NASA NTRS2016

Integrating Large Scale Data Sets to Develop Predictive Hypotheses of Low-Dose Radiation-Induced Health Effects

Over one hundred years of radiation biology research has revealed much about the DNA damages induced by the deposition of energy from exposure to ionizing radiation and the subsequent cellular responses. However, there are still significant gaps in our understanding of how these might lead to detrimental health effects, particularly at low doses (100 mGy (milligray)). Recent advances in high throughput omics technologies enable interrogation of induced radiation effects at the genomic, proteomic and metabolomic levels. These include changes in gene expression, protein modifications, e.g., phosphorylation, acetylation, and methylation, and metabolic changes. We will discuss the integration of data obtained from multiple omics platforms to understand radiation dose, and dose rate effects in a complex human tissue model as a function of time. We will use as an example our results on the low dose responses in a 3D human skin model.

ionizing radiation↗
DOE OSTI2023

Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data: Preprint

To meet the demand for accuracy and real-time capability of PV system degradation evaluation, massive volume data is needed to run high-fidelity and high-efficiency simulations and perform advanced data analysis. However, PV farm operators have a series of difficulties with PV inverter data, such as data collection from multiple channels, massive data storage, data management and massive data analysis. To address these challenges, we developed an integrated data management platform capable of data acquisition, processing, storage, query, and performing big data analysis utilizing AI algorithms. The platform can also achieve data correctness verification and provide an effective distributed data management solution to retrieve massive data and establish a connection to distributed computational frameworks.

data management↗
NASA NTRS1975

Data management for Large Space Telescope

The data management system for the Large Space Telescope (LST) must be capable of meeting requirements of 160 million bits per 95 min orbit with a bit error rate of less than 0.00001 for data. The system will be supported by the Tracking and Data Relay Satellite System of the Space Tracking and Data Network. The on-board system includes a general purpose computer that controls the vehicle as a stable observation platform and the array of instruments used for data collection. The ground-based system comprises a Mission Operations Center (MOC), a Science Institute where instrument data is processed, and a communications service for the space and point-to-point data flow. The allocation of hardware and software between on-board and ground-based components to achieve design objectives of maximum flexibility at minimum cost is discussed.

Hope, G. R., Jr.↗
NASA NTRS2002

The Social Life of a Data Base

This paper presents the complex social life of a large data base. The topics include: 1) Social Construction of Mechanisms of Memory; 2) Data Bases: The Invisible Memory Mechanism; 3) The Human in the Machine; 4) Data of the Study: A Large-Scale Problem Reporting Data Base; 5) The PRACA Study; 6) Description of PRACA; 7) PRACA and Paper; 8) Multiple Uses of PRACA; 9) The Work of PRACA; 10) Multiple Forms of Invisibility; 11) Such Systems are Everywhere; and 12) Two Morals to the Story. This paper is in viewgraph form.

Linde, Charlotte↗
NASA NTRS1990

Using expert systems to implement a semantic data model of a large mass storage system

The successful development of large volume data storage systems will depend not only on the ability of the designers to store data, but on the ability to manage such data once it is in the system. The hypothesis is that mass storage data management can only be implemented successfully based on highly intelligent meta data management services. There now exists a proposed mass store system standard proposed by the IEEE that addresses many of the issues related to the storage of large volumes of data, however, the model does not consider a major technical issue, namely the high level management of stored data. However, if the model were expanded to include the semantics and pragmatics of the data domain using a Semantic Data Model (SDM) concept, the result would be data that is expressive of the Intelligent Information Fusion (IIF) concept and also organized and classified in context to its use and purpose. The results are presented of a demonstration prototype SDM implemented using the expert system development tool NEXPERT OBJECT. In the prototype, a simple instance of a SDM was created to support a hypothetical application for the Earth Observing System, Data Information System (EOSDIS). The massive amounts of data that EOSDIS will manage requires the definition and design of a powerful information management system in order to support even the most basic needs of the project. The application domain is characterized by a semantic like network that represents the data content and the relationships between the data based on user views and the more generalized domain architectural view of the information world. The data in the domain are represented by objects that define classes, types and instances of the data. In addition, data properties are selectively inherited between parent and daughter relationships in the domain. Based on the SDM a simple information system design is developed from the low level data storage media, through record management and meta data management to the user interface.

Roelofs, Larry H.↗
NASA NTRSDate not supplied

Additional Metadata Guidelines to Improve the Structure and Usability of HDF and NetCDF Files

The Hierarchical Data Format (HDF) and Network Common Data Form (NetCDF) are data file formats created to aid users in the creation or use of scientific data. These file formats are useful for handling large data volumes and hosting extensive metadata as global attributes or variables and are popular with the modeling community. HDF and NetCDF files are largely used with remote sensing data and have been used to support measurements from numerous campaigns, from satellite to aircraft or ground and mobile based measurements. The files from airborne field studies, however, vary greatly in terms of the file structure and the amount and content of metadata. Information relevant to the file that can be useful to the user such as the data producer, location where data was taken, variable descriptions, or information about the instrument might not be included in the file. This metadata might be present in another file in the dataset containing the same data using the International Consortium for Atmospheric Research on Transport and Transformation (ICARTT) format. Recently, the Aerosols, Clouds, and their Interactions for Earth System Models (MACIE) group started a grassroots effort to develop a set of requirements for the HDF and NetCDF files for field studies, aiming to make the data products more interoperable and usable. Particularly, these requirements seek to make the files more compliant to Climate and Forecast (CF) metadata conventions and to standardize the file structure and the global and variable attributes. These requirements would help to ensure that HDF and NetCDF files contain adequate metadata to better support their use for research, e.g., the modeling community, and to enhance the usability and interoperability of data for research communities at large. To be presented are the details of the MACIE requirements as well as examples of the implementation of these requirements for merge files and lidar observation data files.

Sean Leavor↗
DOE OSTI2020

PARAFAC_T1

This is a method of performing trilinear analysis on large data sets using a modification of the PARAFAC-ALS algorithm. It iteratively decomposes the data matrix into a core matrix and three loading matrices based on the Tucker1 model. The algorithm is particularly useful for data sets that are too large to upload into a computer?s main memory. While the performance advantage in utilizing our algorithm is dependent on the number of data elements and dimensions of the data array, we have seen a significant performance improvement over operating PARAFAC-ALS on the full data set. SAND2020-12649 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Van Benthem, Mark↗
NASA NTRS1972

The needs of the 80's - Data processing.

The potential observational needs requiring data processing are assessed for a projection of the large data processing systems needed to meet the requirements of meteorology, environmentology, earth resources inventories, and hydrology in the next decade. It is anticipated that a large array of space and ground sensing devices and basic communications capabilities for collecting and disseminating large volumes of data will be available by 1980.

Ludwig, G. H.↗
DOE OSTI2020

Nuclear data uncertainty propagation and modeling uncertainty impact evaluation in neutronics core simulation

Uncertainty analysis is a critical requirement in reactor simulation as it is used to quantify the reliability of best-estimate calculation. A comprehensive uncertainty analysis should characterize all sources of uncertainties in a computationally-feasible and scientifically-defendable manner. Here we employ a well-established reduced order modeling (ROM) based uncertainty quantification methodology to propagate uncertainties throughout neutronic calculations. ROM relies on recent advances in randomized data mining techniques applied to large data streams. In our proposed implementation, the nuclear data uncertainties are first propagated from multi-group level through lattice physics calculation to generate few-group parameter uncertainties, described using a vector of mean values and a covariance matrix. Employing an ROM-based compression of the covariance matrix, the few-group uncertainties are then propagated through downstream core simulation in a computationally efficient manner. This straightforward approach, albeit efficient as compared to brute force forward and/or adjoint-based methods, often employs a number of assumptions that have been unquestioned in the literature of neutronic uncertainty analysis. This manuscript argues that these assumptions could introduce another source of uncertainty referred to as modeling uncertainties, whose magnitude needs to be quantified in tandem with nuclear data uncertainties. Thus, our primary goal is to explore the interactions between these two uncertainty sources in order to assess whether modeling uncertainties have an impact on parameter uncertainties. To explore this endeavor, the impact of a number of modeling assumptions on core attributes uncertainties is quantified. The study employs a CANDU reactor model, with Serpent and NEWT as lattice physics solvers and NESTLE-C as core simulator. The modeling assumptions investigated include those related with the uncertainty propagation method employed, e.g., deterministic vs. stochastic, the few-group energy structure employed to represent the cross-sections, the resonance treatment in lattice physics calculation, the reference values for the cross-section, and the number of samples employed to render ROM compression. Results indicate that some of the modeling assumptions could have a non-negligible impact on the core responses propagated uncertainties, highlighting the need for a more comprehensive approach to combine parameter and modeling uncertainties.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

A data integration framework of additive manufacturing based on FAIR principles

Abstract Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data enables a standardized format allowing for opportunities to generate automated extraction methods and scalability. We establish a framework that captures and integrates data from a L-PBF study such as radiography and high-speed camera video, linking these data sets cohesively allowing for future exploration. Graphical abstract

36 MATERIALS SCIENCE↗
NASA NTRS1978

Development of fire test methods for airplane interior materials

Fire tests were conducted in a 737 airplane fuselage at NASA-JSC to characterize jet fuel fires in open steel pans (simulating post-crash fire sources and a ruptured airplane fuselage) and to characterize fires in some common combustibles (simulating in-flight fire sources). Design post-crash and in-flight fire source selections were based on these data. Large panels of airplane interior materials were exposed to closely-controlled large scale heating simulations of the two design fire sources in a Boeing fire test facility utilizing a surplused 707 fuselage section. Small samples of the same airplane materials were tested by several laboratory fire test methods. Large scale and laboratory scale data were examined for correlative factors. Published data for dangerous hazard levels in a fire environment were used as the basis for developing a method to select the most desirable material where trade-offs in heat, smoke and gaseous toxicant evolution must be considered.

Tustin, E. A.↗
NASA NTRS1998

A Review of the New AVIRIS Data Processing System

The processing of AVIRIS data - from the Metrum Very Large Data Store (VLDS) flight tape to delivered data products - has traditionally been performed in essentially the same way, from the beginning of the AVIRIS project up to and including the 1996 flight season. Starting with the 1997 flight season, a drastically different paradigm has been used for the processing of AVIRIS data. This change was made possible by the recent development of and related availability of affordable data storage devices.

Aronsson, Mikael↗
NASA NTRS2003

Proposed Space Flight Experiment Hardware

The primary thrust for this plan is to develop design tools and fundamental understanding that are timely and consistent with the goal of the various exploration initiatives. The plan will utilize ISS facilities, such as the Fluids Integrated Rack (FIR) and the Microgravity Science Glovebox (MSG). A preliminary flow schematic of Two-Phase Flow Facility (T(phi)FFy) which would utilize FIR is shown in Figure 3. MSG can be utilized to use the Boiling eXperiment Facility (BXF) and Contact Line Dynamics Experiment (CLiDE) Facility. The T(phi)FFy system would have multiple test sections whereby different configurations of heat exchangers could be used to study boiling and condensation phenomena. The test sections would be instrumented for pressure drop, void fraction, heat fluxes, temperatures, high-speed imaging and other diagnostics. Besides a high-speed data acquisition system with a large data storage capability, telemetry could be used to update control and test parameters and download limited amounts of data. In addition, there would be multiple accumulators that could be used to investigate system stability and fluid management issues. The system could accommodate adiabatic tests through either the space station nitrogen supply or have an experiment-specific compressor to pressurize a sufficient amount of air or other non-condensable gas for reuse as the supply bottle is depleted.

Source record↗
NASA NTRS2004

Data Management as a Cluster Middleware Centerpiece

Through earth and space modeling and the ongoing launches of satellites to gather data, NASA has become one of the largest producers of data in the world. These large data sets necessitated the creation of a Data Management System (DMS) to assist both the users and the administrators of the data. Halcyon Systems Inc. was contracted by the NASA Center for Computational Sciences (NCCS) to produce a Data Management System. The prototype of the DMS was produced by Halcyon Systems Inc. (Halcyon) for the Global Modeling and Assimilation Office (GMAO). The system, which was implemented and deployed within a relatively short period of time, has proven to be highly reliable and deployable. Following the prototype deployment, Halcyon was contacted by the NCCS to produce a production DMS version for their user community. The system is composed of several existing open source or government-sponsored components such as the San Diego Supercomputer Center s (SDSC) Storage Resource Broker (SRB), the Distributed Oceanographic Data System (DODS), and other components. Since Data Management is one of the foremost problems in cluster computing, the final package not only extends its capabilities as a Data Management System, but also to a cluster management system. This Cluster/Data Management System (CDMS) can be envisioned as the integration of existing packages.

Zero, Jose↗