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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Transferring data objects: A focused Ada investigation

The use of the Ada language does not guarantee that data objects will be in the same form or have the same value after they have been stored or transferred to another system. There are too many possible variables in such things as the formats used and other protocol conditions. Differences may occur at many different levels of support. These include program level, object level, application level, and system level. A standard language is only one aspect of making a complex system completely homogeneous. Many components must be standardized and the various standards must be integrated. The principal issues in providing for interaction between systems are of exchanging files and data objects between systems which may not be compatible in terms of their host computer, operating system or other factors. A typical resolution of the problem of invalidating data involves at least a common external form, for data objects and for representing the relationships and attributes of data collections. Some of the issues dealing with the transfer of data are listed and consideration is given on how these issues may be handled in the Ada language.

Legrand, Sue↗
NASA NTRS1980

Longitudinal and transverse magnetic field program procedure and detailed specification for Sigma 5

A computer program and procedure for plotting the contour of the data transferred from the Marshall Space Flight Center solar magnetography is presented. The plotted data then can be easily compared with solar data from other sources, such as the Solar Maximum Mission. From the data file for circular polarization the longitudinal program plots the contours for filtered longitudinal plot and intensity plot by choosing the positive and negative contour levels, intensity levels, and also X,Y plotting ranges which need to be used. In a similar manner the transverse program generates the transverse contour plot, azimuth plot, and intensity plot from the linear polarization data files.

Wang, C. K.↗
NASA NTRS1994

FAST User Guide

The Flow Analysis Software Toolkit, FAST, is a software environment for visualizing data. FAST is a collection of separate programs (modules) that run simultaneously and allow the user to examine the results of numerical and experimental simulations. The user can load data files, perform calculations on the data, visualize the results of these calculations, construct scenes of 3D graphical objects, and plot, animate and record the scenes. Computational Fluid Dynamics (CFD) visualization is the primary intended use of FAST, but FAST can also assist in the analysis of other types of data. FAST combines the capabilities of such programs as PLOT3D, RIP, SURF, and GAS into one environment with modules that share data. Sharing data between modules eliminates the drudgery of transferring data between programs. All the modules in the FAST environment have a consistent, highly interactive graphical user interface. Most commands are entered by pointing and'clicking. The modular construction of FAST makes it flexible and extensible. The environment can be custom configured and new modules can be developed and added as needed. The following modules have been developed for FAST: VIEWER, FILE IO, CALCULATOR, SURFER, TOPOLOGY, PLOTTER, TITLER, TRACER, ARCGRAPH, GQ, SURFERU, SHOTET, and ISOLEVU. A utility is also included to make the inclusion of user defined modules in the FAST environment easy. The VIEWER module is the central control for the FAST environment. From VIEWER, the user can-change object attributes, interactively position objects in three-dimensional space, define and save scenes, create animations, spawn new FAST modules, add additional view windows, and save and execute command scripts. The FAST User Guide uses text and FAST MAPS (graphical representations of the entire user interface) to guide the user through the use of FAST. Chapters include: Maps, Overview, Tips, Getting Started Tutorial, a separate chapter for each module, file formats, and system administration.

Walatka, Pamela P.↗
DOE OSTI2021

CHARMM-GUI Nanomaterial Modeler for Modeling and Simulation of Nanomaterial Systems

Molecular modeling and simulation are invaluable tools for nanoscience that predict mechanical, physicochemical, and thermodynamic properties of nanomaterials and provide molecular-level insight into underlying mechanisms. However, building nanomaterial-containing systems remains challenging due to the lack of reliable and integrated cyberinfrastructures. Here we present Nanomaterial Modeler in CHARMM-GUI, a web-based cyberinfrastructure that provides an automated process to generate various nanomaterial models, associated topologies, and configuration files to perform state-of- the-art molecular dynamics simulations using most simulation packages. The nanomaterial models are based on the interface force field, one of the most reliable force fields (FFs). The transferability of nanomaterial models among the simulation programs was assessed by single-point energy calculations, which yielded 0.01% relative absolute energy differences for various surface models and equilibrium nanoparticle shapes. Three widely used Lennard-Jones (LJ) cutoff methods are employed to evaluate the compatibility of nanomaterial models with respect to conventional biomolecular FFs: simple truncation at r = 12 Å (12 cutoff), force-based switching over 10 to 12 Å (10-12 fsw), and LJ particle mesh Ewald with no cutoff (LJPME). The FF parameters with these LJ cutoff methods are extensively validated by reproducing structural, interfacial, and mechanical properties. As a result, we find that the computed density and surface energies are in good agreement with reported experimental results, although the simulation results increase in the following order: 10-12 fsw <12 cutoff < LJPME. Nanomaterials in which LJ interactions are a major component show relatively higher deviations (up to 4% in density and 8% in surface energy differences) compared with the experiment. Nanomaterial Modeler's capability is also demonstrated by generating complex systems of nanomaterial-biomolecule and nanomaterial-polymer interfaces with a combination of existing CHARMM-GUI modules. We hope that Nanomaterial Modeler can be used to carry out innovative nanomaterial modeling and simulations to acquire insight into the structure, dynamics, and underlying mechanisms of complex nanomaterial-containing systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Open-Source Science-led Development of the Atmosphere Observing System (AOS) Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David Giles↗
NASA NTRSDate not supplied

Open-Source Science-led Development of the AOS Mission Science Data System (SDS)

The Earth System Observatory (ESO) Atmosphere Observing System (AOS) mission will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The AOS Science Data System (SDS) will be a system of systems developed within the Cloud to manage the research and operational processing of AOS mission orbital and suborbital sensors and curate these data for reprocessing (e.g., in near real-time or by collection) and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage. Further, AOS SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The AOS mission follows NASA’s lead in making a commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the AOS SDS system components will be developed with open-source concepts including components of SDS itself as well as AOS mission algorithms. Further, the AOS SDS assumes the role to lead and facilitate OSS activities for the AOS mission. This presentation describes the framework of the AOS SDS and its integral part in facilitating OSS within the AOS mission.

David M. Giles↗
DOE OSTI2023

Development of a hybrid neural network and transfer learning model for optimized ICP-MS/MS operation

Correct function and calibration of instrumentation is a crucial assumption for any scientific experiment. One such instrument, tandem inductively coupled plasma mass spectrometer (ICP-MS/MS), has in-depth calibration settings that range across 30+ different parameters, making it difficult to determine optimal conditions without expertise and some degree of trial and error. Often, these settings are hand-tuned, a time-intensive process prone to local maxima and human error. While some automation is available, the automation also may favor local optimizations over a global optimum. In addition to these difficulties, day to day instrument variability can further complicate the calibration process. We propose a solution to this problem as a machine learning (ML) algorithm that learns how each parameter helps determine the calibration sensitivity across several elements, and re-weights parameters over time as instrument variability changes (e.g., a global neural network (NN) with a time-dependent transfer learning (TL) component). This model would be able to generate a surface of predicted calibration sensitivities and their respective parameters, and a simple multivariate algorithm would be able to pull out the optimum results with the settings associated with them. Here-in, we describe our initial findings in working towards this goal, including data extraction from historical files, exploratory data analysis, and some initial model building to better describe the data and the feasibility of our goal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1993

Storage media pipelining: Making good use of fine-grained media

This paper proposes a new high-performance paradigm for accessing removable media such as tapes and especially magneto-optical disks. In high-performance computing the striping of data across multiple devices is a common means of improving data transfer rates. Striping has been used very successfully for fixed magnetic disks improving overall system reliability as well as throughput. It has also been proposed as a solution for providing improved bandwidth for tape and magneto-optical subsystems. However, striping of removable media has shortcomings, particularly in the areas of latency to data and restricted system configurations, and is suitable primarily for very large I/Os. We propose that for fine-grained media, an alternative access method, media pipelining, may be used to provide high bandwidth for large requests while retaining the flexibility to support concurrent small requests and different system configurations. Its principal drawback is high buffering requirements in the host computer or file server. This paper discusses the possible organization of such a system including the hardware conditions under which it may be effective, and the flexibility of configuration. Its expected performance is discussed under varying workloads including large single I/O's and numerous smaller ones. Finally, a specific system incorporating a high-transfer-rate magneto-optical disk drive and autochanger is discussed.

Vanmeter, Rodney↗
DOE OSTI2024

U.S. Freight Transload Facilities Dataset

The U.S. Freight Transload Facilities Dataset provides location information (latitude, longitude, zip, city, county, state)for more than 9,000 facilities across 50 U.S. States where freight may be transferred between waterways, railways, and roadways. The dataset lists the known modes and available direction(s) for freight transfers at each facility as of 2024. The U.S. Freight Transload Facilities dataset was built by mining and fusing several public sources, such as the USACE Master Docks Plus, the USDOT National Transportation Atlas Database (NTAD), files from the Intermodal Association of North America (IANA), and the industry publication Bulk Transloader. The dataset constitutes a key piece of a multimodal freight transportation network and routing algorithm developed by USACE-ERDC. The dataset is shared as a .csv file. The dataset is published for research purposes and should not be considered exhaustive or authoritative.

Peterson, Steven [ORNL] (ORCID:0000000287672998)↗
DOE OSTI2022

Guidelines for Publicly Archiving Terrestrial Model Data to Enhance Usability, Intercomparison, and Synthesis

Scientific communities are increasingly publishing data to evaluate, accredit, and build on published research. However, guidelines for curating data for publication are sparse for model-related research, limiting the usability of archived simulation data. In particular, there are no established guidelines for archiving data related to terrestrial models that simulate land processes and their coupled interactions with climate. Terrestrial modelers have a unique set of challenges when publishing data due to the diversity of scientific domains, research questions, and the types and scales of simulations. Researchers in the U.S. Department of Energy’s (DOE) projects use a variety of multiscale models to advance robust predictions of terrestrial and subsurface ecosystem processes. Here, we synthesize archiving needs for data associated with different DOE models, and provide guidelines for publishing terrestrial model data components following FAIR (Findable, Accessible, Interoperable, Reusable) principles. The guidelines recommend archiving model inputs and testing data used in final simulation runs along with associated codes, workflow scripts, and metadata in public repositories. Researchers should consider archiving model outputs if they are within the storage limits of the repository. We also provide considerations for how to bundle files into different data publications with citable digital object identifiers. Finally, we identify repository features and tools that would enable storage and reuse of model data. Given the diversity of DOE terrestrial models, these guidelines are transferable to other model types and will enable efficient reuse of simulation data for purposes such as model intercomparisons, initialization, benchmarking, synthesis, and comparisons with field observations.

58 GEOSCIENCES↗
DOE OSTI2021

When to use rsync

We have endeavored to show, using a series of data transfer results obtained from two testbeds, when to use the popular data copying tool rsync and related tools. Tests have been conducted in local area network (LAN) and wide area network (WAN) environments. We conclude that for files in a certain size range and network latency &lE; 10 ms round trip time (RTT), rsync is still useful for data moving tasks in the category 4 of the U.S. DOE Technical Report “Data Movement Categories”. For more demanding data movement requirements, tools of different classes are suggested. Sample histograms from two DOE user facilities are provided to further support our conclusions.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1989

Optical archiving for scientific data

Write-once-read-many (WORM) optical disk technology is now practical for use as (1) a near online storage media, (2) long-term storage in a passive repository, and (3) under some circumstances, transporting from and to data generators and data users at remote sites. Optical technology provides several significant advantages over magnetic tapes as a data storage media because it is more compact, provides random access, has a longer lifetime, at least 30 years, and is less sensitive to environmental factors such as temperature and humidity. Two major archival projects using optical technology underway are discussed: the Coastal Zone Color Scanner data and the Dynamics Explorer data. Both projects have used the software for optical archive and retrieval (SOAR) to transfer data in a volume format that can be read transparently by VAX/VMS. Through the use of this software technique, (1) data generators and data users do not have to rewrite extant software and (2) the VAX/VMS Files-11 volume format is in wide use, so it can serve as an interim standard and will be accessible in the long-term.

Lowrey, Barbara E.↗
NASA NTRSDate not supplied

Open-Source Science-Driven Development of the Science Data System (SDS) for Earth System Observatory (ESO) Atmospheric Missions

The NASA Earth System Observatory (ESO) atmospheric missions will provide space-based and suborbital observations of collocated cloud, dynamic, precipitation and aerosol processing leading to improved weather, air quality, and climate predictions. The Science Data System (SDS) will deploy the adaptive processing system (APS) developed within the Cloud to manage the research and operational processing of ESO atmospheric mission orbital and suborbital sensors and curate these data for near real-time and collection reprocessing and transfer them to a NASA Distributed Active Archive Center (DAAC) for long-term storage and distribution. Further, the SDS will follow guidelines provided by NASA Earth Science Data Systems (ESDS) program including standard conventions for data file formats, naming, and metadata to improve data interoperability, interpretability, usability, discovery, provenance, and spatiotemporal representativeness. The SDS follows NASA’s commitment to Open-Source Science (OSS) including the sharing of data, software, and knowledge in an open and timely manner. Each of the SDS system components will be developed with open-source concepts including components of APS itself as well as ESO atmospheric mission algorithms. This presentation describes the framework of the SDS and its integral part in facilitating OSS within the ESO atmospheric missions.

David M. Giles↗
DOE OSTI2024

Data and scripts associated with a manuscript on residence time distribution simulation in two 10-kilometer long river sections

This data package is associated with the publication “On the Transferability of Residence Time Distributions in Two 10-km Long River Sections with Similar Hydromorphic Units” submitted to the Journal of Hydrology (Bao et al. 2024).Quantifying hydrologic exchange fluxes (HEFs) at the stream-groundwater interface, along with their residence time distributions (RTDs) in the subsurface, is crucial for managing water quality and ecosystem health in dynamic river corridors. However, directly simulating high-spatial resolution HEFs and RTDs can be a time-consuming process, particularly for watershed-scale modeling. Efficient surrogate models that link RTDs to hydromorphic units (HUs) may serve as alternatives for simulating RTDs in large-scale models. One common concern with these surrogate models, however, is the transferability of the relationship between the RTDs and HUs from one river corridor to another. To address this, we evaluated the HEFs and the resulting RTD-HU relationships for two 10-kilometer-long river corridors along the Columbia River, using a one-way coupled three-dimensional transient surface-subsurface water transport modeling framework that we previously developed. Applying this framework to the two river corridors with similar HUs allows for quantitative comparisons of HEFs and RTDs using both statistical tests and machine learning classification models. This data package includes the model inputs files and the simulation results data. This data package contains 10 folders. The modeling simulation results data are in the folders 100H_pt_data and 300area_pt_data, for the study domain Hanford 100H and 300 area respectively. The remaining eight folders contain the scripts and data to generate the manuscript figures. The file-level metadata file (Bao_2024_Residence_Time_Distribution _flmd.csv) includes a list of all files contained in this data package and descriptions for each. The data dictionary file (Bao_2024_Residence_Time_Distribution _dd.csv) includes column header definitions and units of all tabular files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

pySMARTS: SMARTS Python Wrapper (Simple Model of the Atmospheric Radiative Transfer of Sunshine)

The pySMARTS module contains functions for calling SMARTS: Simple Model of the Atmospheric Radiative Transfer of Sunshine, from NREL, developed by Dr. Christian Gueymard. SMARTS software can be obtained from: https://www.nrel.gov/grid/solar-resource/smarts.html Users will be responsible to obtain a copy of SMARTS from NREL, honor it's license, and download the SMART files into their PVLib folder. This wrapper is shared under a BSD-3-Clause License, and was originally coded in Matlab by Juan Russo (2001), updated and ported to python by Silvana Ayala (2019-2020).

Ayala Pelaez, Silvana↗
NASA NTRS2001

Binaural Simulation Experiments in the NASA Langley Structural Acoustics Loads and Transmission Facility

A location and positioning system was developed and implemented in the anechoic chamber of the Structural Acoustics Loads and Transmission (SALT) facility to accurately determine the coordinates of points in three-dimensional space. Transfer functions were measured between a shaker source at two different panel locations and the vibrational response distributed over the panel surface using a scanning laser vibrometer. The binaural simulation test matrix included test runs for several locations of the measuring microphones, various attitudes of the mannequin, two locations of the shaker excitation and three different shaker inputs including pulse, broadband random, and pseudo-random. Transfer functions, auto spectra, and coherence functions were acquired for the pseudo-random excitation. Time histories were acquired for the pulse and broadband random input to the shaker. The tests were repeated with a reflective surface installed. Binary data files were converted to universal format and archived on compact disk.

Grosveld, Ferdinand W.↗
NASA NTRS1991

Transferable Output ASCII Data (TOAD) gateway: Version 1.0 user's guide

The Transferable Output ASCII Data (TOAD) Gateway, release 1.0 is described. This is a software tool for converting tabular data from one format into another via the TOAD format. This initial release of the Gateway allows free data interchange among the following file formats: TOAD; Standard Interface File (SIF); Program to Optimize Simulated Trajectories (POST) input; Comma Separated Value (TSV); and a general free-form file format. As required, additional formats can be accommodated quickly and easily.

Bingel, Bradford D.↗
NASA NTRS2006

Steps Towards the Standardization of a More Efficient Uplink Protocol and Code

In June 2004, NASA announced a new vision for the further exploration of the Moon and Mars. The vision includes a long-term human and robotic program to explore the solar system, starting with a return to the Moon that will ultimately enable future exploration of Mars and other destinations. Inherent in this endeavor is the need to transfer the communication technology developed for lunar missions to deep space whenever possible. Furthermore, greater data throughput on the uplink will be required for future nominal operations for the Exploration missions given their highly interactive nature and the need to send files in both directions. Therefore, it is prudent for standards bodies i.e., CCSDS to develop the underlying communication recommendations necessary to deliver greater data throughput to meet future agencies' needs.

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