Search NASA⌕ Search

SEARCH · Search NASA

Results for “time 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 1,783 records · Page 99
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Case History : Using time-lapse vertical seismic profiling data to constrain velocity-saturation relations: the Frio brine pilot CO 2 injection: Time-lapse VSP to constrain VSRs

CO 2 sequestration projects benefit from quantitative assessment of saturation distribution and plume extent for field development and leakage prevention. In this work, we carry out quantitative analysis of time-lapse seismic by using rock physics and seismic modelling tools. We investigate the suitability of Gassmann's equation for a CO 2 sequestration project with 1600 tons of CO 2 injected into high-porosity, brine-saturated sandstone. We analyze the observed time delays and amplitude changes in a time-lapse vertical seismic profile dataset. Both reflected and transmitted waves are analyzed qualitatively and quantitatively. To interpret the changes obtained from the vertical seismic profile, we perform a 2.5D elastic, finite-difference modelling study. The results show a P-wave velocity reduction of 750 m/s in the proximity of the injection well evident by the first arrivals (travel-time delays and amplitude change) and reflected wave amplitude changes. These results do not match with our rock physics model using Gassmann's equation predictions even when taking uncertainty in CO 2 saturation and grain properties into account. We find that time-lapse vertical seismic profile data integrated with other information (e.g., core and well log) can be used to constrain the velocity–saturation relation and verify the applicability of theoretical models such as Gassmann's equation with considerable certainty. The study shows that possible nonelastic factors are in play after CO 2 injection (e.g., CO 2 –brine–rock interaction and pressure effect) as Gassmann's equation underestimated the velocity reduction in comparison with field data for all three sets of time-lapse vertical seismic profile attributes. Here, our work shows the importance of data integration to validate the applicability of theoretical models such as Gassmann's equation for quantitative analysis of time-lapse seismic data.

58 GEOSCIENCES↗
NASA NTRS1973

Electro-optical processing of phased array data

An on-line spatial light modulator for application as the input transducer for a real-time optical data processing system is described. The use of such a device in the analysis and processing of radar data in real time is reported. An interface from the optical processor to a control digital computer was designed, constructed, and tested. The input transducer, optical system, and computer interface have been operated in real time with real time radar data with the input data returns recorded on the input crystal, processed by the optical system, and the output plane pattern digitized, thresholded, and outputted to a display and storage in the computer memory. The correlation of theoretical and experimental results is discussed.

Casasent, D.↗
NASA NTRS2011

Extensions to the Dynamic Aerospace Vehicle Exchange Markup Language

The Dynamic Aerospace Vehicle Exchange Markup Language (DAVE-ML) is a syntactical language for exchanging flight vehicle dynamic model data. It provides a framework for encoding entire flight vehicle dynamic model data packages for exchange and/or long-term archiving. Version 2.0.1 of DAVE-ML provides much of the functionality envisioned for exchanging aerospace vehicle data; however, it is limited in only supporting scalar time-independent data. Additional functionality is required to support vector and matrix data, abstracting sub-system models, detailing dynamics system models (both discrete and continuous), and defining a dynamic data format (such as time sequenced data) for validation of dynamics system models and vehicle simulation packages. Extensions to DAVE-ML have been proposed to manage data as vectors and n-dimensional matrices, and record dynamic data in a compatible form. These capabilities will improve the clarity of data being exchanged, simplify the naming of parameters, and permit static and dynamic data to be stored using a common syntax within a single file; thereby enhancing the framework provided by DAVE-ML for exchanging entire flight vehicle dynamic simulation models.

Brian, Geoffrey J.↗
NASA NTRS2013

Time-resolved Neutron-gamma-ray Data Acquisition for in Situ Subsurface Planetary Geochemistry

The current gamma-ray/neutron instrumentation development effort at NASA Goddard Space Flight Center aims to extend the use of active pulsed neutron interrogation techniques to probe the subsurface elemental composition of planetary bodies in situ. Previous NASA planetary science missions, that used neutron and/or gamma-ray spectroscopy instruments, have relied on neutrons produced from galactic cosmic rays. One of the distinguishing features of this effort is the inclusion of a high intensity 14.1 MeV pulsed neutron generator synchronized with a custom data acquisition system to time each event relative to the pulse. With usually only one opportunity to collect data, it is difficult to set a priori time-gating windows to obtain the best possible results. Acquiring time-tagged, event-by-event data from nuclear induced reactions provides raw data sets containing channel/energy, and event time for each gamma ray or neutron detected. The resulting data set can be plotted as a function of time or energy using optimized analysis windows after the data are acquired. Time windows can now be chosen to produce energy spectra that yield the most statistically significant and accurate elemental composition results that can be derived from the complete data set. The advantages of post-processing gamma-ray time-tagged event-by-event data in experimental tests using our prototype instrument will be demonstrated.

Pulsed neutrongenerator↗
NASA NTRS2017

Expanding NASA's Land, Atmosphere Near Real-Time Capability for EOS (LANCE)

NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) is a virtual system that provides near real-time EOS data and imagery to meet the needs of scientists and application users interested in monitoring a wide variety of natural and man-made phenomena in near real-time. Over the last year: near real-time data and imagery from MOPITT, MISR, OMPS and VIIRS (Land and Atmosphere), the Fire Information for Resource Management System (FIRMS) has been updated and LANCE has begun the process of integrating the Global NRT flood, and Black Marble products. In addition, following the AMSU-A2 instrument anomaly in September 2016, AIRS-only products have replaced the NRT level 2 AIRS+AMSU products. This presentation provides a brief overview of LANCE, describes the new products that are recently available and contains a preview of what to expect in LANCE over the coming year.

NASA Lance↗
NASA NTRS1981

A design approach to real-time formatting of high speed multispectral image data

A design approach to formatting multispectral image data in real time at very high data rates is presented for future onboard processing applications. The approach employs a microprocessor-based alternating buffer memory configuration whose formatting function is completely programmable. Data are read from an output buffer in the desired format by applying the proper sequence of addresses to the buffer via a lookup table memory. Sensor data can be processed using this approach at rates limited by the buffer memory access time and the buffer switching process delay time. This design offers flexible high speed data processing and benefits from continuing increases in the performance of digital memories.

Meredith, B. D.↗
DOE OSTI2023

Use of Convolutional Neural Network Image Classification and High-Speed Ion Probe Data Toward Real-Time Detonation Characterization in a Water-Cooled Rotating Detonation Engine

As rotating detonation engines (RDEs) progress in maturity, the importance of monitoring advancements toward development of active control becomes more critical. Experimental RDE data processing at time scales which satisfy real-time diagnostics will likely require the use of machine learning. This study aims to develop and deploy a novel real-time monitoring technique capable of determining detonation wave number, direction, frequency, and individual wave speeds throughout experimental RDE operational windows. To do so, the diagnostic integrates image classification by a convolutional neural network (CNN) and ionization current signal analysis. Wave mode identification through single-image CNN classification bypasses the need to evaluate sequential images and offers instantaneous identification of the wave mode present in the RDE annulus. Here, real-time processing speeds are achieved due to low data volumes required by the methodology, namely one short-exposure image and a short window of sensor data to generate each diagnostic output. The diagnostic acquires live data using a modified experimental setup alongside Pylon and PyDAQmx libraries within a python data acquisition environment. Lab-deployed diagnostic results are presented across varying wave modes, operating conditions, and data quality, currently executed at 3–4 Hz with a variety of iteration speed optimization options to be considered as future work. These speeds exceed that of conventional techniques and offer a proven structure for real-time RDE monitoring. The demonstrated ability to analyze detonation wave presence and behavior during RDE operation will certainly play a vital role in the development of RDE active control, necessary for RDE technology maturation toward industrial integration.

42 ENGINEERING↗
NASA NTRS2018

Gravitational Wave Sources as Timing References for LISA Data

In the megahertz gravitational-wave band, galactic ultra-compact binaries (UCBs) are continuous sources emitting at near-constant frequency. The signals from many of these galactic binaries will be sufficiently strong to be detectable by the Laser Interferometer Space Antenna (LISA) after approximately Omicron (1 week) of observing. In addition to their astrophysical value, these UCBs can be used to monitor the data quality of the observatory. This paper demonstrates the capabilities of galactic UCBs to be used as calibration sources for LISA by demanding signal coherence between adjacent week-long data segments separated by a gap in time of a priori unknown duration. A parameter for the gap duration is added to the UCB waveform model and used in a Markov-chain Monte Carlo algorithm simultaneously fitting for the astrophysical source parameters. Results from measurements of several UCBs are combined to produce a joint posterior on the gap duration. The measurement accuracy's dependence on how much is known about the UCBs through prior observing, and seasonal variations due to the LISA orbital motion, is quantified. The duration of data gaps in a two-week segment of data can be constrained to within approximately 0.2 s using Omicron (10) UCBs after one month of observing. The timing accuracy from UCBs improves to approximately or less than o.1 s after 1 year of mission operations. These results are robust to within a factor of approximately 2 when taking into account seasonal variations.

Gravitation Wave Sources↗
NASA NTRS1995

Sea surface topography fields of the tropical Pacific from data assimilation

Time series of maps of monthly tropical Pacific dynamic topography anomalies from 1979 through 1985 were constructed by means of assimilation of the tide gauge and expendable bathythermograph (XBT) data into a linear model driven by observed winds. Estimates of error statistics were calculated and compared to actual differences between hindcasts and observations. Four experiements were performed as follows: one with no assimilation, one with assimiation of sea level anomaly data from eight selected island tide gauge stations, one with assimilation of dynamic height anomalies derived from XBT data, and one with both XBT and tide gauge data assimilated. Data from seven additional tide gauge stations were withheld from the assimilation process and used for verification in all four experiements. Statistical objective maps based on data alone were also constructed for comparison purposes. The dynamic response of the model without assimilation was, in general, weaker than the observed response. Assimilation resulted in enhanced signal amplitude in all three assimilation experiments. With few exceptions the error estimated generated by the Kalman filter appeared quite reasonable. Since the error processes cannot be assumed to be white or stationary, we could find no straightforward way to test the formal statistical hypothesis that the time series of differences between the filter ouput and the actual observations were drawn from a population with statistics given by the Kalman filter estimaes. We found that the best filter we could devise was still short of the goal of producing a white innovation sequence. Extensive changes in the assumed error statistics make only marginal differences. The same is true for long time and space scale behavior of different models with richer physics and finer resolution. Better data assimilation results will probably require relaxation of the assumptions of stationarity and serial independence of the errors. Formation of such detailed noise models will require longer time series, with the attendant problems of matching very different data sets.

Miller, Robert N.↗
DOE OSTI2022

CoordNet: Data Generation and Visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural Network

Although deep learning has demonstrated its capability in solving diverse scientific visualization problems, it still lacks generalization power across different tasks. To address this challenge, we propose CoordNet, a single coordinate-based framework that tackles various tasks relevant to time-varying volumetric data visualization without modifying the network architecture. The core idea of our approach is to decompose diverse task inputs and outputs into a unified representation (i.e., coordinates and values) and learn a function from coordinates to their corresponding values. We achieve this goal using a residual block-based implicit neural representation architecture with periodic activation functions. We evaluate CoordNet on data generation (i.e., temporal super-resolution and spatial super-resolution) and visualization generation (i.e., view synthesis and ambient occlusion prediction) tasks using time-varying volumetric data sets of various characteristics. Here, the experimental results indicate that CoordNet achieves better quantitative and qualitative results than the state-of-the-art approaches across all the evaluated tasks. Source code and pre-trained models are available at https://github.com/stevenhan1991/CoordNet.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Time-series dissolved oxygen, other bigeochemically-relevant analytes, and pressure gradients associated with the manuscript “Dissolved oxygen sensor in an automated hyporheic sampling system reveals biogeochemical dynamics”

This dataset contains time-series data from a vertical profile within the bed and banks of the Columbia river near Richland, WA. Water was sampled through 3 small tubes embedded in the sediment at 50,100, and 200 cm below the sediment-water interface. The goal of this study was to observe the correlations between hydraulic drivers and biogeochemical responses. The results of this study are published in the manuscript “Dissolved oxygen sensor in an automated hyporheic sampling system reveals biogeochemical dynamics”. The file types included in the data package are all time-series spreadsheet data, including hydraulic head gradients, physical parameters (temperature, pressure, SpC (specific conductivity)), and biogeochemical parameters (dissolved oxygen, pH, NO3 (nitrate) and ORP (oxidation-reduction potential)).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Adaptation of Control Center Software to Commerical Real-Time Display Applications

NASA-Marshall Space Flight Center (MSFC) is currently developing an enhanced Huntsville Operation Support Center (HOSC) system designed to support multiple spacecraft missions. The Enhanced HOSC is based upon a distributed computing architecture using graphic workstation hardware and industry standard software including POSIX, X Windows, Motif, TCP/IP, and ANSI C. Southwest Research Institute (SwRI) is currently developing a prototype of the Display Services application for this system. Display Services provides the capability to generate and operate real-time data-driven graphic displays. This prototype is a highly functional application designed to allow system end users to easily generate complex data-driven displays. The prototype is easy to use, flexible, highly functional, and portable. Although this prototype is being developed for NASA-MSFC, the general-purpose real-time display capability can be reused in similar mission and process control environments. This includes any environment depending heavily upon real-time data acquisition and display. Reuse of the prototype will be a straight-forward transition because the prototype is portable, is designed to add new display types easily, has a user interface which is separated from the application code, and is very independent of the specifics of NASA-MSFC's system. Reuse of this prototype in other environments is a excellent alternative to creation of a new custom application, or for environments with a large number of users, to purchasing a COTS package.

Collier, Mark D.↗
DOE OSTI2019

Physics-Guided Deep Learning for Time-Series State Estimation Against False Data Injection Attacks

The modern power grid is a cyber-physical system. While the grid is becoming more intelligent with emerging sensing and communication techniques, new vulnerabilities are introduced and cyber security becomes a major concern. One type of cyber-attacks - False Data Injection Attacks (FDIAs) - exploits the limitations in traditional power system state estimation, and modifies system states without being detected. In this paper, we propose a physics-guided deep learning (PGDL) approach to defend against FDIAs. The PGDL takes real-time measurements as inputs to neural networks, outputs the estimated states, and reconstructs measurements considering power system physics. A deep recurrent neural network - Long Short-Term Memory (LSTM) - is employed to learn the temporal correlations among states. This hybrid learning model leads to a time-series state estimation method to defend against FDIAs. The simulation results using IEEE 14-bus test system demonstrate the accuracy and robustness of the proposed time-series state estimation under FDIAs.

Cyber security, state estimation, physics-guided d↗
DOE OSTI2016

Retention modeling for ultra-thin density of Cu-based conductive bridge random access memory (CBRAM)

We investigate the effect of Cu concentration On-state resistance retention characteristics of W/Cu/Ti/HfO{sub 2}/Pt memory cell. The development of RRAM device for application depends on the understanding of the failure mechanism and the key parameters for device optimization. In this study, we develop analytical expression for cations (Cu{sup +}) diffusion model using Gaussian distribution for detailed analysis of data retention time at high temperature. It is found that the improvement of data retention time depends not only on the conductive filament (CF) size but also on Cu atoms concentration density in the CF. Based on the simulation result, better data retention time is observed for electron wave function associated with Cu{sup +} overlap and an extended state formation. This can be verified by analytical calculation of Cu atom defects inside the filament, based on Cu{sup +} diffusion model. The importance of Cu diffusion for the device reliability and the corresponding local temperature of the filament were analyzed by COMSOL Multiphysics simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2014

The Use of NASA near Real-time and Archived Satellite Data to Support Disaster Assessment

With support from a NASA's Applied Sciences Program, The Short‐term Prediction Research and Transition (SPoRT) Center has explored a variety of techniques for utilizing archived and near real‐time NASA satellite data to support disaster assessment activities. MODIS data from the NASA Land Atmosphere Near Real‐time Capability for EOS currently provides true color and other imagery for assessment and potential applications including, but not limited to, flooding, fires, and tornadoes. In May 2013, the SPoRT Center developed unique power outage composites using the VIIRS Day/Night Band to represent the first clear sky view of damage inflicted upon Moore and Oklahoma City, Oklahoma following the devastating EF‐5 tornado that occurred on May 20. Pre‐event imagery provided by the NASA funded Web‐Enabled Landsat Data project offer a basis of comparison for monitoring post-disaster recovery efforts. Techniques have also been developed to generate products from higher resolution imagery from the recently available International Space Station SERVIR Environmental Research and Visualization System instrument. Of paramount importance is to deliver these products to end users expeditiously and in formats compatible with Decision Support Systems (DSS). Delivery techniques include a Tile Map Service (TMS) and a Web Mapping Service (WMS). These mechanisms allow easy integration of satellite products into DSS's, including the National Weather Service's Damage Assessment Toolkit for use by personnel conducting damage surveys. This poster will present an overview of the developed techniques and products and compare the strengths and weaknesses of the TMS and WMS.

McGrath, Kevin M.↗
DOE OSTI2022

FAIRification, Quality Assessment, and Missingness Pattern Discovery for Spatiotemporal Photovoltaic Data

The ongoing growth of the photovoltaic market has pushed the demand for power forecasting and performance evaluation for a huge population of PV power plants. Through access to a large number of time series data sets from different power plants, we have found common issues that impede the modeling process. Namely, the time series data are hard to transfer between groups due to differences in variable nomenclature, and the quality of the data sets can vary. We address the issue of variable nomenclature by FAIRifying spatiotemporal PV time series data. Through the creation of a solar power plant ontology, we propose standards for the naming and structure of metadata used to describe the data from these power plants. Using the structure from this ontology, we have developed both R and Python packages for the automation of the FAIRification process. We have also developed an R package that automates the analysis of the quality of a data set through the designation of letter grades. With access to large time series data sets across many power plants, we can utilize spatiotemporal coherence between the sites in order to improve the quality of our data. To solve the issue of data missingness, we propose the use of Spatiotemporal-GNN autoencoders to detect and impute missing values from a data set by utilizing data from power plants nearby.

14 SOLAR ENERGY↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Progress Report on the Airborne Composition Standard Variable Name and Time Series Working Groups of the 2017 ESDSWG

The role of NASA's Earth Science Data Systems Working Groups (ESDSWG) is to make recommendations relevant to NASA's Earth science data systems from users' experiences and community insight. Each group works independently, focusing on a unique topic. Progress of two of the 2017 Working Groups will be presented. In a single airborne field campaign, there can be several different instruments and techniques that measure the same parameter on one or more aircraft platforms. Many of these same parameters are measured during different airborne campaigns using similar or different instruments and techniques. The Airborne Composition Standard Variable Name Working Group is working to create a list of variable standard names that can be used across all airborne field campaigns in order to assist in the transition to the ICARTT Version 2.0 file format. The overall goal is to enhance the usability of ICARTT files and the search ability of airborne field campaign data. The Time Series Working Group (TSWG) is a continuation of the 2015 and 2016 Time Series Working Groups. In 2015, we started TSWG with the intention of exploring the new OGC (Open Geospatial Consortium) WaterML 2 standards as a means for encoding point-based time series data from NASA satellites. In this working group, we realized that WaterML 2 might not be the best solution for this type of data, for a number of reasons. Our discussion with experts from other agencies, who have worked on similar issues, identified several challenges that we would need to address. As a result, we made the recommendation to study the new TimeseriesML 1.0 standard of OGC as a potential NASA time series standard. The 2016 TSWG examined closely the TimeseriesML 1.0 and, in coordination with the OGC TimeseriesML Standards Working Group, identified certain gaps in TimeseriesML 1.0 that would need to be addressed for the standard to be applicable to NASA time series data. An engineering report was drafted based on the OGC Engineering Report template, describing recommended changes to TimeseriesML 1.0, in the form of use cases. In 2017, we are conducting interoperability experiments to implement the use cases and demonstrate the feasibility and suitability of these modifications for NASA and related user communities. The results will be incorporated into the existing draft engineering report.

OGC (Open Geospatial Consortium) WaterML 2 standar↗