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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Data Analysis and Theoretical Studies of the Upper Mesosphere and Lower Thermosphere

Three separate tasks were proposed under this award. The first involved extending our continuing study of electrodynamical feedback between the thermosphere/ionosphere and the magnetosphere. The second was a model-experiment comparison study of global dynamics and the third was a 'spectral energetics' analysis of tidal dissipation and energy exchange mechanisms. The Earth's mesosphere and lower-thermosphere/ionosphere (MLTI), between approximately 60 and 180 km altitude, is the most poorly understood region of the Earth's atmosphere, primarily because of its relative inaccessibility. This lack of knowledge has been widely recognized and has provided important scientific rationale for the upcoming NASA TIMED mission. While the data gathered during the TIMED era will revolutionize our understanding of the MLTI region, much work can be done prior to the mission, both to develop data-analysis and modeling techniques and to study the more limited relevant experimental data from previous missions. The grant reported on here continues and extends an existing successful program of scientific research into the energetics, dynamics and electrodynamics of the MLTI, using available theoretical and data analysis tools.

Burns, Alan↗
NASA NTRS1986

An overview of data acquisition, signal coding and data analysis techniques for MST radars

An overview is given of the data acquisition, signal processing, and data analysis techniques that are currently in use with high power MST/ST (mesosphere stratosphere troposphere/stratosphere troposphere) radars. This review supplements the works of Rastogi (1983) and Farley (1984) presented at previous MAP workshops. A general description is given of data acquisition and signal processing operations and they are characterized on the basis of their disparate time scales. Then signal coding, a brief description of frequently used codes, and their limitations are discussed, and finally, several aspects of statistical data processing such as signal statistics, power spectrum and autocovariance analysis, outlier removal techniques are discussed.

Rastogi, P. K.↗
NASA NTRS1993

Pioneer Venus data analysis for the retarding potential analyzer

This report describes the data analysis and archiving activities, analysis results, and instrument performance of the orbiter retarding potential analyzer (ORPA) flown on the Pioneer Venus Orbiter spacecraft during the period, Aug. 1, 1988 to Sept. 30, 1993. During this period, the periapsis altitude of the Orbiter spacecraft descended slowly from 1900 km altitude, at which altitude the spacecraft was outside the Venus ionosphere, to approximately 130 km altitude in Oct. 1992 at which time communication with the spacecraft ceased as a result of entry of the spacecraft into the Venus atmosphere. The quantity of ORPA data returned during this reporting period was greatly reduced over that recovered in the previous years of the mission because of the reduced power capability of the spacecraft, loss of half of the onboard data storage, and partial failure of the ORPA. Despite the reduction in available data, especially ionospheric data, important scientific discoveries resulted from this extended period of the Pioneer Venus mission. The most significant discovery was that of a strong solar cycle change in the size of the dayside ionosphere and the resulting shutoff of flow of dayside ions into the nightside hemisphere. The large, topside O+ F2 ionospheric layer observed during the first three years of the Pioneer Venus mission, a period of solar cycle maximum activity, is absent during the solar cycle minimum activity period.

Knudsen, William C.↗
DOE OSTI2020

Small Angle Scattering Data Analysis Assisted by Machine Learning Methods

Small angle scattering (SAS) is a widely used technique for characterizing structures of wide ranges of materials. For such wide ranges of applications of SAS, there exist a large number of ways to model the scattering data. While such analysis models are often available from various suites of SAS data analysis software packages, selecting the right model to start with poses a big challenge for beginners to SAS data analysis. Here, we present machine learning (ML) methods that can assist users by suggesting scattering models for data analysis. A series of one-dimensional scattering curves have been generated by using different models to train the algorithms. The performance of the ML method is studied for various types of ML algorithms, resolution of the dataset, and the number of the dataset. The degree of similarities among selected scattering models is presented in terms of the confusion matrix. The scattering model suggestions with prediction scores provide a list of scattering models that are likely to succeed. Therefore, if implemented with extensive libraries of scattering models, this method can speed up the data analysis workflow by reducing search spaces for appropriate scattering models.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1978

Analysis and Test of Deep Flaws in Thin Sheets of Aluminum and Titanium. Volume 1: Program Summary and Data Analysis

Six thicknesses of 2219-T87 aluminum base metal surface flaw and center crack specimens ranging from 9.53 to 0.635 mm (0.375 to 0.025 inch) were tested at temperatures ranging from 295K to 20K. Additionally, 6A1-4V STA titanium base metal specimens were tested in three thicknesses 3.18, 2.03, and 1.02 mm (0.125, 0.080 and 0.040 inch) at room temperature. All tests were conducted on uniaxial specimens. Results were analyzed and compared with previously developed data to establish a criterion for proof testing thin walled pressure vessels. The data analysis and exact flaw size dimensions are presented.

Finger, R. W.↗
NASA NTRS1994

Software for Data Analysis with Graphical Models

Probabilistic graphical models are being used widely in artificial intelligence and statistics, for instance, in diagnosis and expert systems, as a framework for representing and reasoning with probabilities and independencies. They come with corresponding algorithms for performing statistical inference. This offers a unifying framework for prototyping and/or generating data analysis algorithms from graphical specifications. This paper illustrates the framework with an example and then presents some basic techniques for the task: problem decomposition and the calculation of exact Bayes factors. Other tools already developed, such as automatic differentiation, Gibbs sampling, and use of the EM algorithm, make this a broad basis for the generation of data analysis software.

Buntine, Wray L.↗
NASA NTRS1977

Sampled data analysis of a computer-controlled manipulator

A comprehensive sampled data analysis of a computer-controlled manipulator is presented in terms of root loci for gain selection and transient responses to step input functions. All parameter values and their derivations where applicable were tabulated. The analysis, while quite specific, uses normalized gain parameters, which allows the results to be applied to any similar system regardless of individual hardware parameter values.

Markiewicz, B. R.↗
NASA NTRS1999

[Global Atmospheric Chemistry/Transport Modeling and Data-Analysis]

This grant supported a global atmospheric chemistry/transport modeling and data- analysis project devoted to: (a) development, testing, and refining of inverse methods for determining regional and global transient source and sink strengths for trace gases; (b) utilization of these inverse methods which use either the Model for Atmospheric Chemistry and Transport (MATCH) which is based on analyzed observed winds or back- trajectories calculated from these same winds for determining regional and global source and sink strengths for long-lived trace gases important in ozone depletion and the greenhouse effect; (c) determination of global (and perhaps regional) average hydroxyl radical concentrations using inverse methods with multiple "titrating" gases; and (d) computation of the lifetimes and spatially resolved destruction rates of trace gases using 3D models. Important ultimate goals included determination of regional source strengths of important biogenic/anthropogenic trace gases and also of halocarbons restricted by the Montreal Protocol and its follow-on agreements, and hydrohalocarbons now used as alternatives to the above restricted halocarbons.

Prinn, Ronald G.↗
NASA NTRS1993

Data Analysis for GOPEX Laser Communications Experiment

This paper describes the data analysis based on the image frames received at the Solid State Imaging camera of the Galileo Optical-communications from an Earth-based Transmitter demonstration conducted between December 9 and December 16 of 1992.

SSI Galileo GOPEX laser communication↗
NASA NTRS1990

Proceedings of the LDEF Materials Data Analysis Workshop

The 5-year, 10-month flight of the Long Duration Exposure Facility (LDEF) greatly enhanced the potential value of most LDEF materials, compared to the original 1-year flight plan. NASA recognized this potential by forming the LDEF Space Environmental Effects on Materials Special Investigation Group in early 1989 to address the expanded opportunities available in the LDEF structure and on experimental trays, so that the value of all LDEF materials to current and future space missions would be assessed and documented. The LDEF Materials Data Analysis Workshop served as one step toward the realization of that responsibility and ran concurrently with activities surrounding the successful return of the spacecraft to the NASA Kennedy Space Center. A compilation of visual aids utilized by speakers at the workshop is presented. Session 1 summarized current information on analysis responsibilities and plans and was aimed at updating the workshop attendees: the LDEF Advisory Committee, Principle Investigators, Special Investigation Group Members, and others involved in LDEF analyses or management. Sessions 2 and 3 addressed materials data analysis methodology, specimen preparation, shipment and archival, and initial plans for the LDEF Materials Data Base. A complementary objective of the workshop was to stimulate interest and awareness of opportunities to vastly expand the overall data base by considering the entire spacecraft as a materials experiment.

Stein, Bland A.↗
DOE OSTI2020

High Resolution Data Analysis: Plans and Prospects [Book Chapter]

Herein, a report on the progress on the high resolution data analysis of the ADMX experimental results is presented. In this paper, tools are developed and tested on a blind injection mimicking a Maxwellian like signal in the frequency domain. This blind injection will be used as a test bed which can be later implemented on all the high resolution data. The high resolution data is stored in the Fermilab server. In this analysis a PostgreSQL query was made to ensure the blind injection is in the middle of the frequency spectrum and 19 such files were found. The time series data is read using a c++ program. An apodization function is applied on the time series data and zero filled to reduce the frequency spacing in order to achieve a better interpolation. A FFTW header is used to compute the Fourier transform of the time series data. A Savitzky–Golay filter is applied on the unnormalized power which then can be used to remove the spectral shape. Each frequency spectrum has a bandwidth of 50 kHz.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1993

Research requirements for a real-time flight measurements and data analysis system for subsonic transport high-lift research

A multiphased research program to obtain detailed flow characteristics on a multielement high-lift flap system is being conducted on the Transport Systems Research Vehicle (B737-100 aircraft) at NASA Langley Research Center. Upcoming flight tests have required the development of a highly capable and flexible flight measurement and data analysis instrumentation system. This instrumentation system will be more comprehensive than any of the systems used on previous high-lift flight experiment at NASA Langley. The system will provide the researcher near-real-time information for decision making needed to modify a flight test in order to further examine unexpected flow conditions. This paper presents the research requirements and instrumentation design concept for an upcoming flight experiment for the subsonic transport high-lift research program. The flight experiment objectives, the measurement requirements, the data acquisition system, and the onboard data analysis and display capabilities are described.

Whitehead, Julia H.↗
DOE OSTI2021

Columnar data analysis with ATLAS analysis formats

Future analysis of ATLAS data will involve new small-sized analysis formats to cope with the increased storage needs. The smallest of these, named DAOD_PHYSLITE, has calibrations already applied to allow fast downstream analysis and avoid the need for further analysis-specific intermediate formats. This allows for application of the "columnar analysis" paradigm where operations are applied on a per-array instead of a per-event basis. We will present methods to read the data into memory, using Uproot, and also discuss I/O aspects of columnar data and alternatives to the ROOT data format. Furthermore, we will show a representation of the event data model using the Awkward Array package and present proof of concept for a simple analysis application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Deep learning as a tool for neural data analysis: Speech classification and cross-frequency coupling in human sensorimotor cortex

A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements. This is often accomplished by learning a simple, linear transformations between neural features and features of the sensory stimuli or motor task. While successful in some early sensory processing areas, linear mappings are unlikely to be ideal tools for elucidating nonlinear, hierarchical representations of higher-order brain areas during complex tasks, such as the production of speech by humans. Here, we apply deep networks to predict produced speech syllables from a dataset of high gamma cortical surface electric potentials recorded from human sensorimotor cortex. We find that deep networks had higher decoding prediction accuracy compared to baseline models. Having established that deep networks extract more task relevant information from neural data sets relative to linear models (i.e., higher predictive accuracy), we next sought to demonstrate their utility as a data analysis tool for neuroscience. We first show that deep network's confusions revealed hierarchical latent structure in the neural data, which recapitulated the underlying articulatory nature of speech motor control. We next broadened the frequency features beyond high-gamma and identified a novel high-gamma-to-beta coupling during speech production. Finally, we used deep networks to compare task-relevant information in different neural frequency bands, and found that the high-gamma band contains the vast majority of information relevant for the speech prediction task, with little-to-no additional contribution from lower-frequency amplitudes. Together, these results demonstrate the utility of deep networks as a data analysis tool for basic and applied neuroscience.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1997

Pioneer 10 and 11 Data Analysis

This report finishes the work of NASA Grant NAS2-153, which supported data analysis for the UCSD instruments on Pioneers 10 and 11. The data analyzed under this grant span 22 years of interplanetary measurements in the inner and outer heliosphere. The UCSD instruments made their mark in cosmic ray research based upon their high energy thresholds, directional responses, and reliable data streams. one of their primary scientific objectives concerns the size, configuration, and time behavior of the heliosphere. The size scale is inferred from the radial intensity gradient, which is measured between the two spacecraft and extrapolated to interstellar intensity levels at the cosmic ray modulation boundary. This boundary still eludes us, and its position, motion, and the best method of extrapolation are ongoing problems. Current projections place the boundary beyond 100 AU, which may be beyond the termination shock, and raises the question of possible modulation in the heliosheath. Probably our only hope of seeing this region in the immediate future rides on the possibility that the boundary will move inward. Our instruments have recorded many Forbush, or transient, decreases in the outer heliosphere. These observations led us to a model that attributes many of the decreases to solar wind stream-stream interactions, and relates the cosmic ray variations to the locally observed magnetic field magnitude. As the cosmic ray variations in this model result only from topological changes in the modulation integral, the model is a tool for studying the possibility that the 11 year cosmic ray modulation cycle can be accounted for by a superposition of Forbush decreases. The cosmic ray angular distribution function is measurable, given a good telemetry rate, by the UCSD Cerenkov detector which counts particles of energy greater than 500 MeV/n. We obtained statistically significant samples from 1 to 9 AU, at 13 AU, and at 34 AU. The anisotropy tends to be a few tenths of a per cent at all radial distances. A quasiperiodic variation in the east-west anisotropy with period of about 50 days remains unexplained.

Fillius, Walker↗
DOE OSTI2022

Efficient Implementation of Artificial Neural Networks for Sensor Data Analysis Based on a Genetic Algorithm

The reliability of many industrial processes depends on the sensor system. However, these sensors can be affected by noise, perturbations and failures. Hence, sensor monitoring and diagnosis are fundamental to guarantee the quality of an industrial process. Nowadays, artificial neural networks (ANN) are widely used in sensor signal processing and diagnosis. However, those ANNs usually require many artificial neurons, being difficult to implement in software and hardware due to their high computational costs. This paper presents an optimized implementation of artificial neurons in ANNs for sensor data analysis using a Genetic Algorithm (GA). The objective of GA is to find an adequate segmentation to reduce the activation function approximation error. One of the advantages of the proposed approach is that the cost function used in GA considers the effect of factors such as the ANN architecture or the number of bits used in arithmetic operations. The proposed ANN implementation technique aims to get the best possible approximation for a specific ANN architecture, making easier its implementation in software and hardware. Simulation and experimental results using FPGA (Field Programmable Gate Array) prove the advantages of the proposed approach for implementing sensor data analysis systems based on ANNs.

D estefani, André↗