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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Status on the Recovery of Voyager Plasma Electron Observations at Jupiter

We give status report on the recovery of Voyager 1 and 2 plasma electron observations during their Jupiter flybys. The plan is to complete this analysis including new results never published before. The original summary data tapes and later datafiles were evidently permanently lost along with our past analysis results of the plasma electron observations except for that shown in previous publications. These summary data tapes included the plasma ion and electron raw datafiles, status words needed to convert data numbers to currents, SEDR data with trajectory and attitude information and magnetic field measurements. In addition to recovering the plasma electron plasma observations (electron intensities and fluid parameters of the thermal and suprathermal electron populations, respectively), the SEDR trajectory information during the out bound passes were lost but fortunately in the past the SEDR files were converted to Spice Kernals, so this unexpected problem was solved. In addition, we had to rewrite our read routines it looked like the previous read routines so the rest of the Fortran code could be used; the Fortran- code had to be modified to work with the latest compilers and computer platforms. We will report on the- status of our work- originally funded under the Planetary Data Archiving, Restoration, and Tools (PDART)program but now being funded by the Internal Scientist Funding Model (ISFM) Exosphere Ionosphere Magnetospheres Modeling (EIMM)program at NASA Goddard Space Flight Center.

E C Sittler Jr.↗
DOE OSTI2023

Perspectives for self-driving labs in synthetic biology

Self-driving labs (SDLs) combine fully automated experiments and data collection with artificial intelligence (AI) and control algorithms that decide not only the set of parameters for the next experiment, but also potentially which scientific hypotheses to test. Taken to their ultimate expression, SDLs could usher a new paradigm of scientific research, where the world is probed, interpreted, and explained by machines for human benefit. Whereas there are functioning SDLs in the fields of chemistry and materials science, we contend that synthetic biology provides a unique opportunity since the genome provides a single, easily accessible, target for affecting the incredibly wide repertoire of biological cell behavior. Since they can provide large amounts of high-quality data, SDLs can be a platform for AI to develop approaches to systematically convert data into scientific knowledge systems. These knowledge systems can be used both to understand the biological world and to design bioengineered systems to fit a desired specification (inverse design). However, the level of investment required for the creation of biological SDLs is only warranted if directed towards solving difficult and enabling biological questions. Here, we discuss challenges and opportunities in creating SDLs for synthetic biology.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

Peripheral Equipment Interchanges Bytes Of Data

Method for conversion of data formats between incompatible computers reduces conversion time by 80 percent. Transposes high and low bytes of word so data from computer A match storage format of computer B. Two interface circuit boards convert data. Such boards ordinarily used for communication between computers. In format-conversion application, cable connecting boards modified so high-8-bit and low-8-bit data lines interchanged with result data words in proper format for computer B.

Aguilar, Robert B.↗
NASA NTRS1998

Nimbus/TOMS Science Data Operations Support

Projected goals include the following: (1) Participate in and provide analysis of laboratory and in-flight calibration of LTV sensors used for space observations of backscattered LTV radiation; (2) Provide support to the TOMS Science Operations Center, including generating instrument command lists and analysis of TOMS health and safety data; (3) Develop and maintain software and algorithms designed to capture and process raw spacecraft and instrument data, convert the instrument output into measured radiance and irradiances, and produce scientifically valid products; (4) Process the TOMS data into Level 1, Level 2, and Level 3 data products; (5) Provide analysis of the science data products in support of NASA GSFC Code 916's research.

Source record↗
NASA NTRS1998

Nimbus/TOMS Science Data Operations Support

1. Participate in and provide analysis of laboratory and in-flight calibration of UV sensors used for space observations of backscattered UV radiation. 2. Provide support to the TOMS Science Operations Center, including generating instrument command lists and analysis of TOMS health and safety data. 3. Develop and maintain software and algorithms designed to capture and process raw spacecraft and instrument data, convert the instrument output into measured radiance and irradiances, and produce scientifically valid products. 4. Process the TOMS data into Level 1, Level 2, and Level 3 data products. 5. Provide analysis of the science data products in support of NASA GSFC Code 916's research.

Childs, Jeff↗
NASA NTRS1980

Data acquisition and analysis in the DOE/NASA Wind Energy Program

Four categories of data systems, each responding to a distinct information need are presented. The categories are: control, technology, engineering and performance. The focus is on the technology data system which consists of the following elements: sensors which measure critical parameters such as wind speed and direction, output power, blade loads and strains, and tower vibrations; remote multiplexing units (RMU) mounted on each wind turbine which frequency modulate, multiplex and transmit sensor outputs; the instrumentation available to record, process and display these signals; and centralized computer analysis of data. The RMU characteristics and multiplexing techniques are presented. Data processing is illustrated by following a typical signal through instruments such as the analog tape recorder, analog to digital converter, data compressor, digital tape recorder, video (CRT) display, and strip chart recorder.

Neustadter, H. E.↗
NASA NTRS1984

Development of a thermal and structural model for a NASTRAN finite-element analysis of a hypersonic wing test structure

The development of a thermal and structural model for a hypersonic wing test structure using the NASTRAN finite-element method as its primary analytical tool is described. A detailed analysis was defined to obtain the temperature and thermal stress distribution in the whole wing as well as the five upper and lower root panels. During the development of the models, it was found that the thermal application of NASTRAN and the VIEW program, used for the generation of the radiation exchange coefficients, were definicent. Although for most of these deficiencies solutions could be found, the existence of one particular deficiency in the current thermal model prevented the final computation of the temperature distributions. A SPAR analysis of a single bay of the wing, using data converted from the original NASTRAN model, indicates that local temperature-time distributions can be obtained with good agreement with the test data. The conversion of the NASTRAN thermal model into a SPAR model is recommended to meet the immediate goal of obtaining an accurate thermal stress distribution.

Lameris, J.↗
DOE OSTI2018

ALS/Milo: BL402 RSXD data analysis

Suite of tools designed to simply data collection, extraction, reduction, and plotting for resonant soft x-ray scattering experiments performed at ALS beamline 4.0.2; speeds up the process of converting data into meaningful information for users to publish experimental results. * Python software routines for processing CCD images, including reciprocal space maps, diffraction peak alignment, resonant spectral profiles, line-cut comparisons, coordinate transforms, etc. * Scan file generator, optimized for typical uses cases, reduces burden on users by handling complex operations behind the scenes of a simplified user interface * Deployed at beamline endstation computers; packaged and deployable as standardized PyPI package * Under version control (git) and hosted on LBL-sponsored repository (bitbucket)

Shafer, Padraic↗
DOE OSTI2023

QA/QC-ed Groundwater Level Time Series in PLM-1 and PLM-6 Monitoring Wells, East River, Colorado (2016-2022)

This data set contains QA/QC-ed (Quality Assurance and Quality Control) water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location at the East River Watershed, Colorado, USA. These wells are used to monitor subsurface water and carbon inventories and fluxes, and to determine the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry (see related references) can be used to estimate rates of solute export from the hillslope to the floodplain and river. QA/QC analysis of measured groundwater levels in monitoring wells PLM-1 and PLM-6 included identification and flagging of duplicated values of timestamps, gap filling of missing timestamps and water levels, removal of abnormal/bad and outliers of measured water levels. The QA/QC analysis also tested the application of different QA/QC methods and the development of regular (5-minute, 1-hour, and 1-day) time series datasets, which can serve as a benchmark for testing other QA/QC techniques, and will be applicable for ecohydrological modeling. The package includes a Readme file, one R code file used to perform QA/QC, a series of 8 data csv files (six QA/QC-ed regular time series datasets of varying intervals (5-min, 1-hr, 1-day) and two files with QA/QC flagging of original data), and three files for the reporting format adoption of this dataset (InstallationMethods, file level metadata (flmd), and data dictionary (dd) files).QA/QC-ed data herein were derived from the original/raw data publication available at Williams et al., 2020 (DOI: 10.15485/1818367). For more information about running R code file (10.15485_1866836_QAQC_PLM1_PLM6.R) to reproduce QA/QC output files, see README (QAQC_PLM_readme.docx). This dataset replaces the previously published raw data time series, and is the final groundwater data product for the PLM wells in the East River. Complete metadata information on the PLM1 and PLM6 wells are available in a related dataset on ESS-DIVE: Varadharajan C, et al (2022). https://doi.org/10.15485/1660962. These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales. 2022/09/09 Update: Converted data files using ESS-DIVE’s Hydrological Monitoring Reporting Format. With the adoption of this reporting format, the addition of three new files (v1_20220909_flmd.csv, V1_20220909_dd.csv, and InstallationMethods.csv) were added. The file-level metadata file (v1_20220909_flmd.csv) contains information specific to the files contained within the dataset. The data dictionary file (v1_20220909_dd.csv) contains definitions of column headers and other terms across the dataset. The installation methods file (InstallationMethods.csv) contains a description of methods associated with installation and deployment at PLM1 and PLM6 wells. Additionally, eight data files were re-formatted to follow the reporting format guidance (er_plm1_waterlevel_2016-2020.csv, er_plm1_waterlevel_1-hour_2016-2020.csv, er_plm1_waterlevel_daily_2016-2020.csv, QA_PLM1_Flagging.csv, er_plm6_waterlevel_2016-2020.csv, er_plm6_waterlevel_1-hour_2016-2020.csv, er_plm6_waterlevel_daily_2016-2020.csv, QA_PLM6_Flagging.csv). The major changes to the data files include the addition of header_rows above the data containing metadata about the particular well, units, and sensor description. 2023/01/18 Update: Dataset updated to include additional QA/QC-ed water level data up until 2022-10-12 for ER-PLM1 and 2022-10-13 for ER-PLM6. Reporting format specific files (v2_20230118_flmd.csv, v2_20230118_dd.csv, v2_20230118_InstallationMethods.csv) were updated to reflect the additional data. R code file (QAQC_PLM1_PLM6.R) was added to replace the previously uploaded HTML files to enable execution of the associated code. R code file (QAQC_PLM1_PLM6.R) and ReadMe file (QAQC_PLM_readme.docx) were revised to clarify where original data was retrieved from and to remove local file paths.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

SPARSKIT: A basic tool kit for sparse matrix computations

Presented here are the main features of a tool package for manipulating and working with sparse matrices. One of the goals of the package is to provide basic tools to facilitate the exchange of software and data between researchers in sparse matrix computations. The starting point is the Harwell/Boeing collection of matrices for which the authors provide a number of tools. Among other things, the package provides programs for converting data structures, printing simple statistics on a matrix, plotting a matrix profile, and performing linear algebra operations with sparse matrices.

Saad, Youcef↗
NASA NTRS1987

A distributed data acquisition system for aeronautics test facilities

The NASA Lewis Research Center is in the process of installing a new data acquisition and display system. This new system will provide small and medium sized aeronautics test facilities with a state-of-the-art real-time data acquisition and display system. The new data system will provide for the acquisition of signals from a variety of instrumentation sources. They include analog measurements of temperatures, pressures, and other steady state voltage inputs; frequency inputs to measure speed and flow; discrete I/O for significant events, and modular instrument systems such as multiplexed pressure modules or electronic instrumentation with a IEEE 488 interface. The data system is designed to acquire data, convert it to engineering units, compute test dependent performance calculations, limit check selected channels or calculations, and display the information in alphanumeric or graphical form with a cycle time of one second for the alphanumeric data. This paper describes the system configuration, its salient features, and the expected impact on testing.

Fronek, Dennis L.↗
NASA NTRS1987

A distributed data acquisition system for aeronautics test facilities

The NASA Lewis Research Center is in the process of installing a new data acquisition and display system. This new system will provide small and medium sized aeronautics test facilities with a state-of-the-art real-time data acquisition and display system. The new data system will provide for the acquisition of signals from a variety of instrumentation sources. They include analog measurements of temperatures, pressures, and other steady state voltage inputs; frequency inputs to measure speed and flow; discrete I/O for significant events, and modular instrument systems such as multiplexed pressure modules or electronic instrumentation with a IEEE 488 interface. The data system is designed to acquire data, convert it to engineering units, compute test dependent performance calculations, limit check selected channels or calculations, and display the information in alphanumeric or graphical form with a cycle time of one second for the alphanumeric data. This paper describes the system configuration, its salient features, and the expected impact on testing.

Fronek, Dennis L.↗
DOE OSTI2014

Enhancing User Customization through Novel Software Architecture for Utility Scale Solar Siting Software

There is a need for a spatial decision support application that allows users to create customized metrics for comparing proposed locations of a new solar installation. This document discusses how PVMapper was designed to overcome the customization problem through the development of loosely coupled spatial and decision components in a JavaScript plugin architecture. This allows the user to easily add functionality and data to the system. The paper also explains how PVMapper provides the user with a dynamic and customizable decision tool that enables them to visually modify the formulas that are used in the decision algorithms that convert data to comparable metrics. The technologies that make up the presentation and calculation software stack are outlined. This document also explains the architecture that allows the tool to grow through custom plugins created by the software users. Some discussion is given on the difficulties encountered while designing the system.

14 SOLAR ENERGY↗
DOE OSTI2023

Performance and Commissioning of the BigBite Timing Hodoscope for Nucleon Form Factor Measurements at Jefferson Lab

The BigBite Timing Hodoscope detector is the primary subject of this thesis. The Super BigBite Spectrometer is a Jefferson Lab Hall A Collaboration project that has and will continue to measure nucleon electromagnetic form factors. This spectrometer includes the Timing Hodoscope which provides high resolution particle timing data for scattered electrons in the electron arm of BigBite. The Timing Hodoscope utilizes 90, 25 × 25 × 600 mm3 scintillator bars stacked on top of each other to form a single detector plane, and these bars are connected to 180 photo-multiplier tubes via light guides. Particles collide with the scintillating material creating a shower of optical photons and these particle events in the bars are collected to generate signals that are readout by the data acquisition (DAQ) electronics. NINO ASIC amplifier-discriminator cards output signals from the photo-multiplier tubes into analogue and logic signals, which are sent to analogue-to-digital (ADC) and time-to-digital (TDC) converter data acquisition readout modules. This data is then used for analysis of the detector. The focus of this thesis is the construction, commissioning, calibration, and performance of the BigBite Timing Hodoscope before and during the first of five nucleon electromagnetic form factor experiments at Jefferson Lab Hall A. Before the neutron magnetic form factor, G n M, experiment, cosmic ray data was collected during commissioning to confirm proper operation of the Timing Hodoscope electronics by observing the ADC and TDC data. Commissioning studies for charge normalization, gain matching, and other ADC and TDC detector data variables were performed before moving the detector into Hall A. Following installation in Hall A, several calibration studies were implemented to fine-tune the detector in preparation for use in the experiment. The calibration studies included analysis of timing cuts, TDC alignment, the time-walk effect, time difference offsets, and scintillator velocity corrections. Once the Timing Hodoscope was well-calibrated, data-taking during the experiment commenced and the beam-on-target data was used to characterize the Timing Hodoscope performance during the G n M experiment run-time. The performance analysis included studies observing energy deposit, cluster size, rates, accidentals, pile-up, tracking efficiency, position resolution, and time resolution. After application of physics cuts to ensure a data set comprised of particle tracks corresponding to elastic electrons, which is the main data of interest for measurement of G n M, the Timing Hodoscope is shown on average across all kinematic settings to have a >98% tracking efficiency, a position resolution of 4-6 cm in the non-dispersive plane and 1.5-2 cm in the dispersive plane, and a time resolution of 500-750 ps. These performance results are compared to a GEANT4 based performance simulation of the BigBite Timing Hodoscope for reference, showing to what degree the measured performance values match those taken from the simulation.

Marinaro, Ralph↗
NASA NTRS1981

Satellite oceanography - The instruments

It is pointed out that no instrument is sensitive to only one oceanographic variable; rather, each responds to a combination of atmospheric and oceanic phenomena. This complicates data interpretation and usually requires that a number of observations, each sensitive to somewhat different phenomena, be combined to provide unambiguous information. The distinction between active and passive instruments is described. A block diagram illustrating the steps necessary to convert data from satellite instruments into oceanographic information is included, as is a diagram illustrating the operation of a radio-frequency radiometer. Attention is also given to the satellites that carry the various oceanographic instruments.

Stewart, R. H.↗
DOE OSTI2023

Emulation automation and model checking

A method of automating emulations is provided. The method comprising collecting publicly available network data over a predefined time interval, wherein the collected network data might comprise structured and unstructured data. Any unstructured data is converted into structured data. The original and converted structured data is stored in a database and compared to known network vulnerabilities. An emulated network is created according to the collected network data and the comparison of the structured data with known vulnerabilities. Virtual machines are created to run on the emulated network. Director programs and guest actor programs are run on the virtual machines, wherein the actor programs imitate real user behavior on the emulated network. The director programs deliver task commands to the guest actor programs to imitate real user behavior. The imitated behavior is presented to a user via an interface.

Urias, Vincent↗
NASA NTRS1993

TOAD Editor

Transferable Output ASCII Data (TOAD) computer program (LAR-13755), implements format designed to facilitate transfer of data across communication networks and dissimilar host computer systems. Any data file conforming to TOAD format standard called TOAD file. TOAD Editor is interactive software tool for manipulating contents of TOAD files. Commonly used to extract filtered subsets of data for visualization of results of computation. Also offers such user-oriented features as on-line help, clear English error messages, startup file, macroinstructions defined by user, command history, user variables, UNDO features, and full complement of mathematical statistical, and conversion functions. Companion program, TOAD Gateway (LAR-14484), converts data files from variety of other file formats to that of TOAD. TOAD Editor written in FORTRAN 77.

Bingle, Bradford D.↗
DOE OSTI2021

Adapter Python IO (Adapter) v1.0

The Adapter Python IO software, in short Adapter or the Adapter software, encapsulates certain Python IO capabilities used for loading in and writing out data when performing analytical Python code runs. More specificcally, it provides a Python API to load data tables from various formats such as XLSX (MS Excel), CSV, and database, into Python code as Pandas DataFrames, as well as to write out tables into a database or CSV files. The Adapter software standardizes a way to point the code to one or multiple input files of one or multiple formats. Therefore, its main feature is the ability to convert data tables identified in one main and, optionally, one or more additional input files, into database tables and Pandas DataFrames for downstream usage in any compatible software. In addition to the loading capability, an instance of the Adapter IO object has the capability to write data out. If the write capability is invoked, all loaded tables are written as either a single database or a set of CSV files, or both, to a location specified in the dedicated input table.

Grahovac, Milica↗