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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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Atmospheric data from Explorer XVII mass spectrometer, giving data analysis converting ion currents into number densities of ambient constituents
This paper represents an integration of several Landsat TM image processing techniques with other data to discriminate the lithologies and associated areas of hydrothermal alteration in the vicinity of the Paradise Peak gold mine in west central Nevada. A microprocessor-based image processing system and an IDIMS system were used to analyze data from a 512 X 512 window of a Landsat-5 TM scene collected on June 30, 1984. Image processing techniques included simple band composites, band ratio composites, principal components composites, and baseline-based composites. These techniques were chosen based on their ability to discriminate the spectral characteristics of the products of hydrothermal alteration as well as of the associated regional lithologies. The simple band composite, ratio composite, two principal components composites, and the baseline-based composites separately can define the principal areas of alteration. Combined, they provide a very powerful exploration tool.
The command and data-handling subsystem of the Atmosphere Explorer satellite provides the necessary controls for the instrumentation and telemetry, and also controls the satellite attitude and trajectory. The subsystem executes all command information within the spacecraft, either in real time (as received over the S-band command transmission link) or remote from the command site (as required by the orbit operations schedule). Power consumption in the spacecraft is optimized by suitable application and removal of power to various instruments; additional functions include control of magnetic torquers and of the orbit-adjust propulsion subsystem. Telemetry data from instruments and the spacecraft equipment are formatted into a single serial bit stream. Attention is given to command types, command formats, decoder operation, and command processing functions.
We present the results of a search for extremely metal-poor (EMP), carbon-enhanced metal-poor (CEMP), and cataclysmic variable (CV) stars using a new exploration tool based on linked scatter plots (LSPs). Our approach is especially designed to work with very large spectrum data sets such as the SDSS, LAMOST, RAVE, and Gaia data sets, and it can be applied to stellar, galaxy, and quasar spectra. As a demonstration, we conduct our search using the SDSS DR10 data set. We first created a 3326-dimensional phase space containing nearly 2 billion measures of the strengths of over 1600 spectral features in 569,738 SDSS stars. These measures capture essentially all the stellar atomic and molecular species visible at the resolution of SDSS spectra. We show how LSPs can be used to quickly isolate and examine interesting portions of this phase space. To illustrate, we use LSPs coupled with cuts in selected portions of phase space to extract EMP stars, CEMP stars, and CV stars. We present identifications for 59 previously unrecognized candidate EMP stars and 11 previously unrecognized candidate CEMP stars. We also call attention to 2 candidate He ii emission CV stars found by the LSP approach that have not yet been discussed in the literature.
A study aimed at describing and illustrating visualization tools used in Computational Fluid Dynamics (CFD) and indicating how these tools are likely to change by showing a projected resolution of the human computer interface is presented. The following are outlined using a graphically based test format: the revolution of human computer environments for CFD research; comparison of current environments; current environments with the ideal; predictions for the future CFD environments; what can be done to accelerate the improvements. The following comments are given: when acquiring visualization tools, potential rapid changes must be considered; environmental changes over the next ten years due to human computer interface cannot be fathomed; data flow packages such as AVS, apE, Explorer and Data Explorer are easy to learn and use for small problems, excellent for prototyping, but not so efficient for large problems; the approximation techniques used in visualization software must be appropriate for the data; it has become more cost effective to move jobs that fit on workstations and run only memory intensive jobs on the supercomputer; use of three dimensional skills will be maximized when the three dimensional environment is built in from the start.
Wavelike fluctuations in neutral composition data obtained with the neutral atmospheric composition system quadrupole mass spectrometer carried aboard the Dynamics Explorer (DE 2) satellite are at a maximum in the vicinity of the magnetic poles. Typical rms amplitudes near the poles for N2, O, He, and Ar fluctuations in the 400- to 4000-km-wavelength band are found to be 11, 6, 6, and 20 percent, respectively. Amplitudes near the equator are roughly a third of the polar amplitudes, and activity in the 50- to 400-km-wavlength band is roughly 20 percent of the longer-wavelength activity.
Energy infrastructure can both induce and suffer risks from wildfires ranging from direct damage to energy assets such as substations and power lines to Public Safety Power Shutoffs. Recent wildfire events underscore the need for data-driven approaches that help states and utilities proactively plan for wildfire risk. Existing national tools such as Federal Emergency Management Agency (FEMA)’s National Risk Index (NRI) are valuable for community hazard planning. However, they are less suited for energy infrastructure, as they emphasize population and building exposure rather than system vulnerabilities. In this paper, we explore relationships between energy and wildfire data and present a Wildfire-Energy State Prioritization Index (WESPI). Our methodology combines data from the US Forest Service’s Fire Simulation (FSIM) dataset with energy resilience metrics, historical fire incidents, and geospatial data on transmission lines and fire stations. Correlation analyses suggest that FSIM burn probability is more strongly associated with power outage metrics (ρ = 0.32) than NRI wildfire frequency, and counties with a greater density of fire stations experience more frequent, but less intense wildfires. We further leverage data for burn probability, transmission line density, and fire station density to develop a Wildfire-Energy State Prioritization Index (WESPI) to highlight counties where wildfire hazard, infrastructure exposure, and limited suppression capacity converge. The index provides a consistent, scalable framework for state energy offices and utilities to screen counties for vegetation management, optimization of outage management system deployment, and to inform wildfire mitigation plans.
Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.
Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.
The coming decade of Mars exploration involves a variety of unique telecommunications challenges. Increasing spatial and spectral resolution of in situ science instruments drive the need for increased bandwidth. At the same time, many innovative and low-cost in situ mission concepts are enabled by energy-efficient relay communications. In response to these needs, the Mars Exploration Program has established a plan for an evolving orbital infrastructure that can provide enhancing and enabling telecommunications services to future Mars missions. We will present the evolving capabilities of this network over the coming decade in terms of specific quantitative metrics such as data volume per sol and required lander energy per Gb of returned data for representative classes of Mars exploration spacecraft.
Increasingly larger scale simulations are generating an unprecedented amount of output data, causing researchers to explore new `data staging' methods that buffer, use, and/or reduce such data online rather than simply pushing it to disk. Leveraging the capabilities of data staging, this study explores the potential for data reduction via online data compression, first using general compression techniques and then proposing use-specific methods that permit users to define simple data queries that cause only the data identified by those queries to be emitted. Using online methods for code generation and deployment, with such dynamic data queries, end users can precisely identify the quality of information (QoI) of their output data, by explicitly determining what data may be lost vs. retained, in contrast to general-purpose lossy compression methods that do not provide such levels of control. The paper also describes the key elements of a quality-aware data management system (QADMS) for high-end machines enabled by this approach. Initial experimental results demonstrate that QADMS can effectively reduce data movement cost and improve the QoS while meeting the QoI constraint stated by users.
Increasingly larger scale simulations are generating an unprecedented amount of output data, causing researchers to explore new data staging methods that buffer, use, and/or reduce such data online rather than simply pushing it to disk. Leveraging the capabilities of data staging, this study explores the potential for data reduction via online data compression, first using general compression techniques and then proposing usespecific methods that permit users to define simple data queries that cause only the data identified by those queries to be emitted. Using online methods for code generation and deployment, with such dynamic data queries, end users can precisely identify the quality of information (QoI) of their output data, by explicitly determining what data may be lost vs. retained, in contrast to general-purpose lossy compression methods that do not provide such levels of control. The paper also describes the key elements of a quality-aware data management system (QADMS) for highend machines enabled by this approach. Initial experimental results demonstrate that QADMS can effectively reduce data movement cost and improve the QoS while meeting the QoI constraint stated by users.
Explorer vi and pioneer v data reduction and analysis
Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.
Data are presented from one orbit of Explorer 33 which entered the magnetosphere near the stagnation point. Magnetic activity as measured at the earth was relatively quiet during the orbit, although minor substorm activity began 2 hours prior to the boundary crossing. Well before entry, the magnetosheath observations show flow patterns consistent with hydrodynamic models. Near the stagnation point close to the boundary the observations show rapid variations between virtually isotropic flux (stagnated flow) and flows tangent to the boundary in alternately opposite directions. Movement of the stagnation point of the order of 1 earth radius is inferred from these observations. Multiple boundary crossings are observed in the magnetic field covering a thickness of about 500 km. Prior to the magnetic crossings, the higher-energy proton fluxes begin to disappear. The bulk of the protron flux in the lower energy ranges and also the electron flux decrease rapidly at the last magnetic crossing. No boundary is apparent in the highest energy range measured by the detector.
The data base of IUE spectra available for dissemination to the scientific community presently contains approximately 22,000 separate images. Due to storage space and computer processing time limitations, it was decided to store the data on high density (6250 bpi) specially formatted tapes and to retrieve and reconvert them to the originally supplied format upon request. The archival/retrieval system for the data and an automatic data entry system for creation and maintenance of an indexing data base are described. Suggestion are made for the use of standard formats for future image data storage, retrieval and dissemination.
This is the data backend for the MOF Explorer application on the Materials Project and is built around the QMOF Database.
As part of the Cyclotron Road program, Zanskar Geothermal & Minerals, Inc. investigated the application of micro-earthquake and ambient noise seismology methods to imaging and characterizing the structural characteristics and hydrothermal flux of subsurface faults. Significant advances in what could be resolved were enabled by two major developments in seismology: 1) the availability of large-n arrays of low-cost seismometers, and 2) the availability of increased computational power and semi-automated data reduction algorithms. In tandem, these advances may improve the signal-to-noise ratio and spatial precision of the data collected and enable higher-resolution characterization of subsurface fracture systems and their spatio-temporal evolution. These tools supported efforts to reduce dry-hole risk and to improve wellfield productivity for geothermal resource development. In particular, two applications of these advances were evaluated: 1) fracture-seismic imaging, which was used to detect ambient emissions from fluid-filled fractures, and 2) reservoir tomography, which used information about travel paths, source locations, and source parameters of micro-earthquakes to identify areas of enhanced permeability. Integration of these methods provided guidance for siting wells and served as prior constraints for reservoir models, informing forecasts of power potential and production and injection strategies aimed at minimizing temperature decline and improving overall resource productivity.