Search NASA⌕ Search

SEARCH · Search NASA

Results for “Log 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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

2017 National Household Travel Survey - Arizona Add-On

# 2017 National Household Travel Survey – Arizona Add-On The Arizona add-on survey supplements the 2017 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. ## Data Collection Agency The Federal Highway Administration conducted the NHTS and corresponding add-on surveys. ## Methodology This survey documents the demographic, attitudinal, and travel behavior for all members of 2,987 households, as collected from April 2016 to April 2017. Daily travel details provide insight into work and school commutes, non-emergency medical trips, shopping trips, and even how travel differs in the summer and on weekends as compared to a typical weekday when school is in session. When statistically weighted to adjust for survey biases, the data demographically represents all Americans and is appropriate for analysis at the national and census region levels. ## Survey Records Survey records include a total of 6,081 participants. ## Transportation Data The NHTS Arizona add-on data package contains a demographic and socioeconomic composition of 6,081 people from 2,987 households in Arizona, as well as detailed information on the travel behavior of each household for a designated 24-hour period. The survey logged more than 258,000 vehicle miles of travel by participants during 19,779 trips. For details on available data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/data-elements-2017.xlsx?sfvrsn=ad09b18c_3). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2014

Stellar Laboratories II. New Zn Iv and Zn v Oscillator Strengths and Their Validation in the Hot White Dwarfs G191-B2B and RE0503-289

Context. For the spectral analysis of high-resolution and high-signal-to-noise (SN) spectra of hot stars, state-of-the-art non-local thermodynamic equilibrium (NLTE) model atmospheres are mandatory. These are strongly dependent on the reliability of the atomic data that is used for their calculation. In a recent analysis of the ultraviolet (UV) spectrum of the DA-type white dwarf G191B2B,21 Zn iv lines were newly identified. Because of the lack of Zn iv data, transition probabilities of the isoelectronic Ge vi were adapted for a first, coarse determination of the photospheric Zn abundance.Aims. Reliable Zn iv and Zn v oscillator strengths are used to improve the Zn abundance determination and to identify more Zn lines in the spectra of G191B2B and the DO-type white dwarf RE 0503289. Methods. We performed new calculations of Zn iv and Zn v oscillator strengths to consider their radiative and collisional bound-bound transitions in detail in our NLTE stellar-atmosphere models for the analysis of the Zn iv v spectrum exhibited in high-resolution and high-SN UV observations of G191B2B and RE 0503289. Results. In the UV spectrum of G191B2B, we identify 31 Zn iv and 16 Zn v lines. Most of these are identified for the first time in any star. We can reproduce well almost all of them at log Zn 5.52 0.2 (mass fraction, about 1.7 times solar). In particular, the Zn iv Zn v ionization equilibrium, which is a very sensitive Teff indicator, is well reproduced with the previously determined Teff 60 000 2000 K and log g 7.60 0.05. In the spectrum of RE 0503289, we identified 128 Zn v lines for the first time and determined log Zn 3.57 0.2 (155 times solar). Conclusions. Reliable measurements and calculations of atomic data are a pre-requisite for stellar-atmosphere modeling. Observed Zn iv and Zn v line profiles in two white dwarf (G191B2B and RE 0503289) ultraviolet spectra were well reproduced with our newly calculated oscillator strengths. This allowed us to determine the photospheric Zn abundance of these two stars precisely.

strengths↗
DOE OSTI2024

Event Detection and Classification Using Machine Learning Applied to PMU Data for the Western US Power System

Smart grid technology enhances our comprehension and reliability of the power grid, leveraging Phasor Measurement Unit (PMU) data—time-synchronized, high-frequency measurements gathered across the US power grid. This paper employs machine learning techniques to effectively analyze the vast PMU data in Wide Area Monitoring Systems (WAMS) for power grid event detection and classification. Analyzing several months of real-world PMU data, the paper focuses on machine learning for fast, precise event detection and classification, corroborated by utility event logs. Practical challenges like feature extraction, dimensionality reduction, and model selection are addressed. A novel feature yielding improved results is discovered, and a supplementary algorithm for detecting small power grid faults is developed. The final algorithm is validated using a month-long real PMU data set, demonstrating its capability in accurately identifying power grid events in near real-time.

machine learning, event detection, PMU↗
NASA NTRSDate not supplied

Repurposing Drilling Control Diagnostics for Subsurface Edge Detection and Boundary Advisement During Planetary Drilling

Informed decision-making during lunar drilling and sampling missions will require data monitoring tools and specialized ground data systems. Accurate and updated situational awareness, with ongoing data monitoring, is critical for timely responses by to incoming science data. Traverse plans and scheduled activities may need to be flexibly changed in order to react to unexpected data or situations. Unlike (for example) Mars missions, the relative lightspeed closeness of the Moon allows for near-real-time ground processing of incoming mission and instrument data. An Apollo-class lunar regolith drill will in a sense “travel” a meter or two vertically at a given subsurface characterization site. As the drill penetrates into lunar regolith, it is likely to encounter a range of material densities, orientations, fracture toughness, and (perhaps) ice percentages. Lunar drill telemetry can provide science teams with a valuable first look into the subsurface structure, the regolith bulk properties, and constituents at each drilled site. Real-time AI-based recognition and reaction to downhole situations has been developed for automated deeper drilling on Mars and beyond. We can leverage the same knowledge bases and pattern-matching as areal-time interpreter of the subsurface, a situational awareness tool during drilling operations. We recently (Sept. 2019) demonstrated this AI drilling monitoring and analysis capability, in control of in-situ drilling and sampling operations, mounted on a KREX-2 rover in Chile’s Atacama Desert. Terrestrial automated drilling log analyses in oil exploration have used similar machine learning techniques in classifying and identifying features in drilling logs –but these typically are designed assuming a drilling fluid influencing downhole measurements and data (permeability, resistivity). Drilling models and existing AI software designed to detect and respond to drilling faults and hard materials can be repurposed, for near-real-time (ground-based) interpretation of drilling telemetry –a potentially valuable advisory tool for strata boundaries and changes in drilling parameters. On the Moon, this approach could be used to study the structure and to some extent the composition of lunar regolith vs. borehole depth, based on recognizable variations in fracture hardness, drilling energy and penetration rates while actively drilling. Since the early 2000s, a series of increasingly-capable real-time drilling telemetry interpretation and characterization software tools have been developed. These subsurface models and software tools have monitored the real-time drilling data received, and automatically identified changes in drill behavior (e.g., encountering a harder target layer, bit inclusions, drill choking due to infall downhole, and others) correlating these with subsurface structures and features. We discuss the mappings between drill borehole parameters, faults or events detected, and modeled changes in rock layer boundaries, in examples drawn from field testing at analog sites in an Arctic impact crater, Rio Tinto, and Chile’s Atacama Desert. These demonstrate how subsurface structural boundaries led to fault detections and responses by the software.

drilling advisor↗
NASA NTRS2011

Soil Moisture Limitations on Monitoring Boreal Forest Regrowth Using Spaceborne L-Band SAR Data

A study was carried out to investigate the utility of L-band SAR data for estimating aboveground biomass in sites with low levels of vegetation regrowth. Data to estimate biomass were collected from 59 sites located in fire-disturbed black spruce forests in interior Alaska. PALSAR L-band data (HH and HV polarizations) collected on two dates in the summer/fall of 2007 and one date in the summer of 2009 were used. Significant linear correlations were found between the log of aboveground biomass (range of 0.02 to 22.2 t ha-1) and (L-HH) and (L-HV) for the data collected on each of the three dates, with the highest correlation found using the LHV data collected when soil moisture was highest. Soil moisture, however, did change the correlations between L-band and aboveground biomass, and the analyses suggest that the influence of soil moisture is biomass dependent. The results indicate that to use L-band SAR data for mapping aboveground biomass and monitoring forest regrowth will require development of approaches to account for the influence that variations in soil moisture have on L-band microwave backscatter, which can be particularly strong when low levels of aboveground biomass occur

Kasischke, Eric S.↗
DOE OSTI2024

Machine Learning-Driven Quantification of CO2 Plume Dynamics at Illinois Basin Decatur Project Sites Using Microseismic Data

This study utilizes machine learning to quantify CO2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO2 injection metrics, this work aims to predict the temporal evolution of subsurface CO2 saturation plumes. The findings illustrate that machine learning can predict plume dynamics, revealing vertical clustering of microseismic events over distinct time periods within certain proximities to the injection well, consistent with an invasion percolation model. The buoyant CO2 plume partially trapped within sandstone intervals periodically breaches localized barriers or baffles, which act as leaky seals and impede vertical migration until buoyancy overcomes gravity and capillary forces, leading to breakthroughs along vertical zones of weakness. Between different unsupervised clustering techniques, K-Means and DBSCAN were applied and analyzed in detail, where K-means outperformed DBSCAN in this specific study by indicating the combination of the highest Silhouette Score and the lowest Davies–Bouldin Index. The predictive capability of machine learning models in quantifying CO2 saturation plume extension is significant for real-time monitoring and management of CO2 sequestration sites. The models exhibit high accuracy, validated against physical models and injection data from the IBDP, reinforcing the viability of CO2 geological sequestration as a climate change mitigation strategy and enhancing advanced tools for safe management of these operations.

Iyegbekedo, Ikponmwosa↗
NASA NTRS1974

Effects of turbulence in the atmosphere of Venus on Pioneer Venus radio, phase 2

Two problems related to the effects of turbulence in the atmosphere of Venus on the Pioneer entry probe radio link were studied. In the first problem, the cross correlation between the log amplitude and phase fluctuations of the Pioneer Venus communications link is examined. Data show that for fluctuation frequencies above approximately 1 Hz there is little or no correlation. For frequencies below this region the correlation is weak and the square root of the coherence has a peak value close to 0.65. The second problem consists of interferring turbulence characteristics of the Venus atmosphere from the Mariner 5 phase fluctuations. Data show that with the data processing techniques developed and currently available, the phase error due to oscillator drift, assumed trajectory delay, and spline curve fit exceed the turbulence induced fluctuations. Results show that it is not possible to interfere with the turbulence characteristics from Mariner 5 phase fluctuations.

Woo, R. T.↗
NASA NTRS1980

Mode I fracture behavior of nonlinear materials

A finite element analysis is presented for the Mode I fracture behavior of cracked plates (stationary crack) made of different nonlinear materials (elastoplastic or elastic locking). The assumed stress-strain behavior of the nonlinear materials was piecewise linear. For each given 'stationary' crack size, the corresponding critical remote tensile stress was calculated based on the maximum crack-tip stress failure criterion. It was found that in the log-log plots of the critical remote stress versus critical crack length, the fracture data of the piecewise linear materials obey a 'stepwise-linear-inverse-square-root' fracture law. It is also shown how the fracture data of the piecewise linear materials can be fitted by proper piecewise graphical shifting of the classical 'inverse-square-root' fracture curve for the linearly elastic materials.

Ko, W. L.↗
NASA NTRS1981

Supersonic Stall Flutter of High Speed Fans

An analytical model is developed for predicting the onset of supersonic stall bending flutter in axial flow compressors. The analysis is based on a modified two dimensional, compressible, unsteady actuator disk theory. It is applied to a rotor blade row by considering a cascade of airfoils whose geometry and dynamic response coincide with those of a rotor blade element at 85 percent of the span height (measured from the hub). The rotor blades are assumed to be unshrouded (i.e., free standing) and to vibrate in their first flexural mode. The effects of shock waves and flow separation are included in the model through quasi-steady, empirical, rotor total-pressure-loss and deviation-angle correlations. The actuator disk model predicts the unsteady aerodynamic force acting on the cascade blading as a function of the steady flow field entering the cascade and the geometry and dynamic response of the cascade. Calculations show that the present model predicts the existence of a bending flutter mode at supersonic inlet Mach numbers. This flutter mode is suppressed by increasing the reduced frequency of the system or by reducing the steady state aerodynamic loading on the cascade. The validity of the model for predicting flutter is demonstrated by correlating the measured flutter boundary of a high speed fan stage with its predicted boundary. This correlation uses a level of damping for the blade row (i.e., the log decrement of the rotor system) that is estimated from the experimental flutter data. The predicted flutter boundary is shown to be in good agreement with the measured boundary.

Adamczyk, J. J.↗
NASA NTRS1982

The IUE spectrum of BD + 39 deg 4926

IUE spectra of the A- or F-type supergiant BD + 39 deg 4926 have been combined with existing optical data. Comparison with model atmospheres yields an effective temperature of 7750 K, gravity log g of 0.5, and a reddening corresponding to E(B-V) of 0.15. The Mg II wavelength 2800 feature was used to estimate the abundance of Mg(+), and the value of log (epsilon (Mg(+)/epsilon (Ca(+)) was found to be about 2.7.

Saha, A.↗
NASA NTRS1994

Passive microwave observations of the Wedell Sea during austral winter and early spring

The results of multispectral passive microwave observations (6.7 to 90-GHz) are presented from the cruises of the FS Polarstern in the Weddell Sea from July to December 1986. This paper includes primarily the analysis of radiometric observations taken at ice station sites. Averaged emissivity spectra for first-year (FY) ice were relatively constant throughout the experiment and were not statistically different from FY ice signatures in the Arctic. Detailed ice characterization was carried out at each site to compare the microwave signatures of the ice with the physical properties. Absorption optical depths of FY ice were found to be sufficiently high that only the structure in the upper portions of the ice contributed significantly to interstation emissivity variations. The emissivities at 90-GHz, e(90), had the greatest variance. Both e(90) at vertical polarization and GR(sub e)(90, 18.7)(defined as (e(sub V)(90)-e(sub V)(18.7))/e(sub V)(90 + e(sub V)(18.7)) depended on the scattering optical depth which is a function of the snow grain diameter and layer thickness. The variance showed a latitude dependence and is probably due to an increase in the strength of snow metamorphism nearer the northern edge of the ice pack. The contribution of variations of near-surface brine volume to the emissivity was not significant over the range of values encountered at the station sites. Emissivity spectra are presented for a range of thin ice types. Unsupervised principal component analysis produced three significant eigenvectors and showed a separation among four different surface types: open water, thin ice, FY ice, and FY ice with a thick snow cover. A comparison with SMMR satellite data showed that average ice concentrations derived from the ship's ice watch log were consistent with the satellite concentrations. The surface based emissivities for FY ice were also compared with emissivities calculated from scanning multichannel microwave radiometer (SMMR) satellite radiances. Best agreement was found at 6.7 and 10-GHz, while at 18 and 37-GHz, SMMR emissivities were slightly lower than surface based results. For the three lower frequencies agreement was found within a confidence limit of 95% and for 37-GHz within about 90%.

Grenfell, T. C.↗
NASA NTRS1995

Is coverage a factor in non-Gaussianity of IMF parameters?

Recently, Feynman and Ruzmaikin (1994) showed that IMF parameters for the 1973 to 1990 period are not log-normally distributed as previously suggested by Burlaga and King (1979) for the data obtained over a shorter time period (1963-75). They studied the first four moments, namely: mean, variance, skewness, and kurtosis. For a Gaussian distribution, moments higher than the variance should vanish. In particular, Feynman and Ruzmaikin obtained very high values of kurtosis during some periods of their analysis. We note that the coverage for IMF parameters is very uneven for the period analyzed by them, ranging from less than 40% to greater than 80%. So a question arises as to whether the amount of coverage is a factor in their analysis. We decided to test this for the B(sub z) component of IMF, since it is an effective geoactive parameter for short term disturbances. Like them, we used 1-hour averaged data available on the Omnitape. We studied the scatter plots of the annual mean values of B(sub z)(nT) and its kurtosis versus the percent coverage for the year. We obtain a correlation coefficient of 0.48 and 0.42 respectively for the 1973-90 period. The probability for a chance occurrence of these correlation coefficients for 18 pair of points is less than 8%. As a rough measure of skewness, we determined the percent asymmetry between the areas of the histograms representing the distributions of the positive and the negative values of B(sub z) and studied its correlation with the coverage for the year. This analysis yields a correlation coefficient of 0.41 When we extended the analysis for the whole period for which IMF data are available (1963-93) the corresponding correlation coefficients are 0.59, 0.14, and 0.42. Our findings will be presented and discussed

Ahluwalia, H. S.↗
NASA NTRS1998

Worldwide Research, Worldwide Participation: Web-Based Test Logger

Thanks to the World Wide Web, a new paradigm has been born. ESCORT (steady state data system) facilities can now be configured to use a Web-based test logger, enabling worldwide participation in tests. NASA Lewis Research Center's new Web-based test logger for ESCORT automatically writes selected test and facility parameters to a browser and allows researchers to insert comments. All data can be viewed in real time via Internet connections, so anyone with a Web browser and the correct URL (universal resource locator, or Web address) can interactively participate. As the test proceeds and ESCORT data are taken, Web browsers connected to the logger are updated automatically. The use of this logger has demonstrated several benefits. First, researchers are free from manual data entry and are able to focus more on the tests. Second, research logs can be printed in report format immediately after (or during) a test. And finally, all test information is readily available to an international public.

Clark, David A.↗
NASA NTRS2002

Deep Space Network equipment performance, reliability, and operations management information system

The Deep Space Mission System (DSMS) Operations Program Office and the DeepSpace Network (DSN) facilities utilize the Discrepancy Reporting Management System (DRMS) to collect, process, communicate and manage data discrepancies, equipment resets, physical equipment status, and to maintain an internal Station Log. A collaborative effort development between JPL and the Canberra Deep Space Communication Complex delivered a system to support DSN Operations.

discrepancy reporting DSN operations web-based too↗
NASA NTRS2019

Long-Term Impacts of Selective Logging on Amazon Forest Dynamics from Multi-Temporal Airborne LiDAR

Forest degradation is common in tropical landscapes, but estimates of the extent and duration of degradation impacts are highly uncertain. In particular, selective logging is a form of forest degradation that alters canopy structure and function, with persistent ecological impacts following forest harvest. In this study, we employed airborne laser scanning in 2012 and 2014 to estimate three-dimensional changes in the forest canopy and understory structure and aboveground biomass following reduced-impact selective logging in a site in Eastern Amazon. Also, we developed a binary classification model to distinguish intact versus logged forests. We found that canopy gap frequency was significantly higher in logged versus intact forests even after 8 years (the time span of our study). In contrast, the understory of logged areas could not be distinguished from the understory of intact forests after 6–7 years of logging activities. Measuring new gap formation between LiDAR acquisitions in 2012 and 2014, we showed rates 2 to 7 times higher in logged areas compared to intact forests. New gaps were spatially clumped with 76 to 89% of new gaps within 5 m of prior logging damage. The biomass dynamics in areas logged between the two LiDAR acquisitions was clearly detected with an average estimated loss of -4.14 +/- 0.76 MgC/hay. In areas recovering from logging prior to the first acquisition, we estimated biomass gains close to zero. Together, our findings unravel the magnitude and duration of delayed impacts of selective logging in forest structural attributes, confirm the high potential of airborne LiDAR multitemporal data to characterize forest degradation in the tropics, and present a novel approach to forest classification using LiDAR data.

Pinage, Ekena Rangel↗
DOE OSTI2024

Machine Learning Applications in Analyzing the Role of Shale Barriers and Baffles for CO2 Storage

This study uses machine learning to analyze microseismic data from the Illinois Basin Decatur Project (IBDP) and quantify CO₂ plume extents. By leveraging well logs, microseismic records, and CO₂ injection metrics, the research predicts subsurface CO₂ plume dynamics. Findings show vertical clustering of microseismic events near the injection well, with CO₂ periodically breaching barriers due to buoyancy. K-Means clustering performed best, achieving the highest Silhouette Score and lowest Davies-Bouldin Index. This capability is crucial for real-time monitoring and management of CO₂ sequestration sites, validated against physical models and IBDP data, reinforcing CO₂ geological sequestration's viability and enhancing management tools.

Carr, Timothy↗
NASA NTRS2018

Quantifying Long-Term Changes in Carbon Stocks and Forest Structure from Amazon Forest Degradation

Despite sustained declines in Amazon deforestation, forest degradation from logging and firecontinues to threaten carbon stocks, habitat, and biodiversity in frontier forests along the Amazon arcof deforestation. Limited data on the magnitude of carbon losses and rates of carbon recoveryfollowing forest degradation have hindered carbon accounting efforts and contributed to incompletenational reporting to reduce emissions from deforestation and forest degradation (REDD+). Wecombined annual time series of Landsat imagery and high-density airborne lidar data to characterizethe variability, magnitude, and persistence of Amazon forest degradation impacts on abovegroundcarbon density (ACD) and canopy structure. On average, degraded forests contained 45.1% of thecarbon stocks in intact forests, and differences persisted even after 15 years of regrowth. Incomparison to logging, understory fires resulted in the largest and longest-lasting differences in ACD.Heterogeneity in burned forest structure varied by fire severity and frequency. Forests with a historyof one, two, and three or more fires retained only 54.4%, 25.2%, and 7.6% of intact ACD,respectively, when measured after a year of regrowth. Unlike the additive impact of successive fires,selective logging before burning did not explain additional variability in modeled ACD loss andrecovery of burned forests. Airborne lidar also provides quantitative measures of habitat structure thatcan aid the estimation of co-benefits of avoided degradation. Notably, forest carbon stocks recoveredfaster than attributes of canopy structure that are critical for biodiversity in tropical forests, includingthe abundance of tall trees. We provide the first comprehensive look-up table of emissions factors forspecific degradation pathways at standard reporting intervals in the Amazon. Estimated carbon lossand recovery trajectories provide an important foundation for assessing the long-term contributionsfrom forest degradation to regional carbon cycling and advance our understanding of the currentstate of frontier forests.

Rappaport, Danielle I.↗
DOE OSTI2024

Oscilloscope Data Push Program

This paper details the development of a Python program designed to automate the data acquisition and conversion for an oscilloscope for the purposes of a one-off/temporary data acquisition system for users that readily need data, and do not have the option of obtaining a Data Acquisition (DAQ) solution. Creating DAQ systems for analyzing a system requires expensive electronics and a dedicated team of engineers for support. Traditionally, manual data collection and processing are time consuming and prone to error. By automating these processes, the cost, efficiency and accuracy of data handling are improved upon. This project involves the creation of a program that interacts with the oscilloscope. During this interaction, there are various functions being performed such as the acquisition of waveform data via floating points, generating plots with the acquired wave points, and storing of floating points in a CSV file format for future reference and plotting purposes. While the initial aim of the project included continuous logging to a cloud database, this was deferred due to time constraints. The results portrayed an almost-instant rate of data collection with a buffer time, showcasing the potential for further integration and real-time data processing.

Osei-Tutu, Jason↗