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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Cassini radio and plasma wave investigation: Data compression and scientific applications

The Radio and Plasma Wave Science (RPWS) experiment being built for the Cassini spacecraft will study a wide range of plasma and radio wave phenomena in the magnetosphere of Saturn and will also make valuable measurements during the cruise phase and at other encounters. A feature of data from wave receivers is the capability of producing vastly more data than the spacecraft telemetry link is capable of transmitting back to the Earth. Thus, techniques of on-board data compression and data reduction are important. The RPWS instrument has one processor dedicated to data compression tasks.

Woolliscroft, L. J. C.↗
NASA NTRS1999

Investigation of Point Doppler Velocimetry (PDV) for Transition Detection in Boundary Layers

A two component Point Doppler Velocimetry (PDV) system has been developed and tested. Improvements were made to an earlier PDV system, in terms of experimental techniques, as well as the data acquisition and reduction software. Measurements of the streamwise and spanwise mean and fluctuating velocities for flows from a rectangular channel and over an NACA 0012 airfoil were made, and the data were compared against hot wire data. The closest to the airfoil surface that PDV measurements could be made was on the order of 0.005 m(0.2", z/c = 0.0169). When the PDV and hot wire data were compared, the time traces for each appeared similar. The mean velocities agreed to within plus or minus 2 m/sec, while the RMS velocities agreed to plus or minus 0.4 m/sec. While the PDV time autocorrelations agreed with those of the hot wire data, the PDV power spectral densities were noisier above 750 Hz. A major source of error in these experiments was determined to be the drifting of the iodine cell stem temperatures. While the stem temperatures were controlled to within plus or minus 0.1 C, this could lead to a frequency shift of as much as 6 MHz, which translates into an error of 1.6 m/sec for the back scatter channel, and up to 6.9 m/sec for the forward scatter channel. These error estimates are consistent with the observed error magnitudes.

Kuhlman, John M.↗
NASA NTRS2000

WFPC2 Stellar Photometry with HSTphot

HSTphot, a photometry package designed to handle the undersampled PSFs found in WFPC2 images, is introduced and described, as well as some of the considerations that have to be made in order to obtain accurate PSF-fitting stellar photometry with WFPC2 data. Tests of HSTphot's internal reliability are made using multiple observations of the same field, and tests of external reliability are made by comparing with DoPHOT reductions of the same data. Subject headz'ngs: techniques: photometric

Dolphin, Andrew E.↗
DOE OSTI2021

An integrated manifold learning approach for high-dimensional data feature extractions and its applications to online process monitoring of additive manufacturing

As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis. With the rapid development of sensor technology, a large amount of high-dimensional data such as image streams can be easily available. Thus, a promising application of manifold learning is in the field of sensor signal analysis, particular for the applications of online process monitoring and control using high-dimensional data. The objective of this study is to develop a manifold learning-based feature extraction method for process monitoring of Additive Manufacturing (AM) using online sensor data. Due to the non-parametric nature of most existing manifold learning methods, their performance in terms of computational efficiency, as well as noise resistance has yet to be improved. To address this issue, this study proposes an integrated manifold learning approach termed multi-kernel metric learning embedded isometric feature mapping (MKML-ISOMAP) for dimension reduction and feature extraction of online high-dimensional sensor data such as images. Based on the extracted features with the utilization of supervised classification and regression methods, an online process monitoring methodology for AM is implemented to identify the actual process quality status. Finally, in the numerical simulation and real-world case studies, the proposed method demonstrates excellent performance in both prediction accuracy and computational efficiency.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Manifold Learning-Based Polynomial Chaos Expansions for High-Dimensional Surrogate Models

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantities of interest of the computational or analytical model. For this purpose, we employ Grassmannian diffusion maps, a two-step nonlinear dimension reduction technique which allows us to reduce the dimensionality of the data and identify meaningful geometric descriptions in a parsimonious and inexpensive manner. Polynomial chaos expansion is then used to construct a mapping between the stochastic input parameters and the diffusion coordinates of the reduced space. An adaptive clustering technique is proposed to identify an optimal number of clusters of points in the latent space. The similarity of points allows us to construct a number of geometric harmonic emulators which are finally utilized as a set of inexpensive pretrained models to perform an inverse map of realizations of latent features to the ambient space and thus perform accurate out-of-sample predictions. Thus, the proposed method acts as an encoder-decoder system which is able to automatically handle very high-dimensional data while simultaneously operating successfully in the small-data regime. The method is demonstrated on two benchmark problems and on a system of advection-diffusion-reaction equations which model a first-order chemical reaction between two species. In all test cases, the proposed method is able to achieve highly accurate approximations which ultimately lead to the significant acceleration of UQ tasks.

42 ENGINEERING↗
NASA NTRS1985

A tomographic technique for aerodynamics at transonic speeds

Computer aided tomography (CAT) provides a means of noninvasively measuring the air density distribution around an aerodynamic model. This technique is global in that a large portion of the flow field can be measured. A test of the applicability of CAT to transonic velocities was studied. A hemispherical-nose cylinder afterbody model was tested at a Mach number of 0.8 with a new laser holographic interferometer at the 2- by 2-Foot Transonic Wind Tunnel. Holograms of the flow field were taken and were reconstructed into interferograms. The fringe distribution (a measure of the local densities) was digitized for subsequent data reduction. A computer program based on the Fourier-transform technique was developed to convert the fringe distribution into three-dimensional densities around the model. Theoretical aerodynamic densities were calculated for evaluating and assessing the accuracy of the data obtained from the tomographic method.

Lee, G.↗
NASA NTRS1985

A tomographic technique for aerodynamics at transonic speeds

Computer aided tomography (CAT) provides a means of noninvasively measuring the air density distribution around an aerodynamic model. This technique is global in that a large portion of the flow field can be measured. A test of the applicability of CAT to transonic velocities was studied. A hemispherical-nose cylinder afterbody model was tested at a Mach number of 0.8 with a new laser holographic interferometer at the 2- by 2-Foot Transonic Wind Tunnel. Holograms of the flow field were taken and were reconstructed into interferograms. The fringe distribution (a measure of the local densities) was digitized for subsequent data reduction. A computer program based on the Fourier-transform technique was developed to convert the fringe distribution into three-dimensional densities around the model. Theoretical aerodynamic densities were calculated for evaluating and assessing the accuracy of the data obtained from the tomographic method.

Lee, G.↗
NASA NTRS1972

Heat transfer rate distribution on North American Rockwell delta wing orbiter determined by phase change paint technique at a Mach number of 8, volume 1

The results of a wind tunnel test program to determine aerodynamic heat transfer distributions on an orbiter configuration are presented. Heat-transfer rates were determined by the phase change paint technique on 0.013-scale Stycast models using Tempilaq as the surface temperature indicator. The nominal test conditions were; Mach 8, length Reynolds numbers of 6.0 x 1 million and 8.9 x 1 million, and angles of attack from 10 to 50 deg in 10-deg increments. At the higher Reynolds number, data were obtained with and without boundary layer trips. Model details, test conditions, and reduced heat-transfer data are presented. Data reduction of the phase-change paint photographs was performed by utilizing a new technique which is described in the data presentation section.

Matthews, R. K.↗
DOE OSTI2022

AMM: Adaptive Multilinear Meshes

Adaptive representations are increasingly indispensable for reducing the in-memory and on-disk footprints of large-scale data. Usual solutions are designed broadly along two themes: reducing data precision, e.g., through compression, or adapting data resolution, e.g., using spatial hierarchies. Additionally, recent research suggests that combining the two approaches, i.e., adapting both resolution and precision simultaneously, can offer significant gains over using them individually. However, there currently exist no practical solutions to creating and evaluating such representations at scale. In this work, we present a new resolution-precision-adaptive representation to support hybrid data reduction schemes and offer an interface to existing tools and algorithms. Through novelties in spatial hierarchy, our representation, Adaptive Multilinear Meshes (AMM), provides considerable reduction in the mesh size. AMM creates a piecewise multilinear representation of uniformly sampled scalar data and can selectively relax or enforce constraints on conformity, continuity, and coverage, delivering a flexible adaptive representation. AMM also supports representing the function using mixed-precision values to further the achievable gains in data reduction. We describe a practical approach to creating AMM incrementally using arbitrary orderings of data and demonstrate AMM on six types of resolution and precision datastreams. By interfacing with state-of-the-art rendering tools through VTK, we demonstrate the practical and computational advantages of our representation for visualization techniques. With an open-source release of our tool to create AMM, we make such evaluation of data reduction accessible to the community, which we hope will foster new opportunities and future data reduction schemes.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2010

Development of an Uncertainty Model for the National Transonic Facility

This paper introduces an uncertainty model being developed for the National Transonic Facility (NTF). The model uses a Monte Carlo technique to propagate standard uncertainties of measured values through the NTF data reduction equations to calculate the combined uncertainties of the key aerodynamic force and moment coefficients and freestream properties. The uncertainty propagation approach to assessing data variability is compared with ongoing data quality assessment activities at the NTF, notably check standard testing using statistical process control (SPC) techniques. It is shown that the two approaches are complementary and both are necessary tools for data quality assessment and improvement activities. The SPC approach is the final arbiter of variability in a facility. Its result encompasses variation due to people, processes, test equipment, and test article. The uncertainty propagation approach is limited mainly to the data reduction process. However, it is useful because it helps to assess the causes of variability seen in the data and consequently provides a basis for improvement. For example, it is shown that Mach number random uncertainty is dominated by static pressure variation over most of the dynamic pressure range tested. However, the random uncertainty in the drag coefficient is generally dominated by axial and normal force uncertainty with much less contribution from freestream conditions.

Walter, Joel A.↗
DOE OSTI2018

Radiation dose rate distributions of spent fuel dry casks estimated with MAVRIC based on detailed geometry and continuous-energy models

This study presents a detailed comparison of the dose rate distributions of a dry TN–32 fuel cask with two geometry models and two cross-sectional datasets. The accuracies of radiation dose rate estimation and computational efficiencies of each geometry model with two cross-sectional data-sets are compared. The use of automated variance reduction techniques can significantly improve the computational efficiency of such a realistic, deep penetration problem that involves radiation transport from different volumetric sources, thereby eliciting only a small statistical error. Monaco with Automated Variance Reduction using Importance Calculations (MAVRIC) is a computational sequence within the SCALE 6.2 code package based on consistent adjoint driven importance sampling (CADIS), a type of automated variance reduction technique. Homogenous and full fuel assembly models are built herein, and two nuclear cross-section libraries (V7–200N47G and continuous energy) are applied in this work. Based on the detailed comparisons, we found that neutron dose rate estimation is more dependent on geometry modeling than on cross-section data. For neutron-induced gamma rays, the dose rate distribution depends on both the spatial self-shielding effect and the cross-section library. The primary gamma rays respectively contribute to the total dose rate by ~91% and ~ 99% on side and top surfaces, and the dose rate accuracy is more dependent on the cross-section library than on geometry modeling. In terms of the computation efficiency and efforts spent on geometry modeling, the homogenous fuel assembly model with the MG library can produce an acceptable dose rate distribution, but the detailed fuel assembly model with the continuous-energy library is required for more precise dose rate estimation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Scalable edge clustering of dynamic graphs via weighted line graphs

Timestamped relational datasets consisting of records (or connections) between pairs of entities are ubiquitous in network science. For applications like peer-to-peer communication, email, various social network interactions, and computer network security, it is useful to organize these records into groups based on how and when they are occurring. Weighted line graphs offer a natural way to model how records are related in such datasets but for large real-world graph topologies, building and utilizing the line graph is prohibitively expensive. Here, we present the framework to cluster the edges of a dynamic graph via the associated line graph that contains two major contributions. The first is a method to work with the line graph implicitly and the second is a distributed scale implementation of an agglomerative hierarchical graph clustering algorithm. We outline a novel hierarchical dynamic graph edge clustering approach that efficiently breaks massive relational datasets into small sets of edges containing events at various timescales. This is in stark contrast to traditional graph clustering algorithms that prioritize highly connected (clique-like) community structures. Our approach relies on constructing a sufficient subgraph of a weighted line graph and applying a hierarchical agglomerative clustering. This approach is related to scalable techniques from spatial clustering, nonlinear-dimension reduction, topological data analysis, and draws particular inspiration from HDBSCAN. As an edge clustering, this method yields an overlapping node clustering. Our algorithm is parallelizable and we demonstrate efficient clustering of a billion-scale, real-world dynamic graph into small edge sets that correlate in topology and time. The entire clustering process for a graph with tens of billions of edges takes just a few minutes of run time on 256 nodes of a distributed compute environment. We argue how the output of the edge clustering is useful for a multitude of data visualization and powerful machine learning tasks, both involving the original massive dynamic graph data and metadata associated with the nodes and edges. Finally, we describe how this approach can be extended to dynamic hypergraphs and dynamic graphs/hypergraphs with unstructured data living on vertices and edges.

Data Analysis↗
NASA NTRS1968

Nth order redundancy reduction.

Curve fitting for optimum redundance reduction on sampled data, describing polygonal and approximate polygonal techniques

Wilkins, L. C.↗
NASA NTRS1975

Demonstration of short-haul aircraft aft noise reduction techniques on a twenty inch (50.8) diameter fan, volume 2

Aft fan noise reduction techniques were investigated. The 1/3 octave band sound data were plotted with the following plots included: perceived noise level vs acoustic angle at 2 fan speeds; PWL vs frequency at 2 fan speeds; and sound pressure level vs frequency at 2 aft angles and 2 fan speeds. The source noise plots included: band pass filter sound pressure level vs acoustic angle at 2 fan speeds; and 2nd harmonic SPL acoustic angle at 2 fan speeds.

Stimpert, D. L.↗
NASA NTRS1984

Flight testing the digital electronic engine control in the F-15 airplane

The digital electronic engine control (DEEC) is a full-authority digital engine control developed for the F100-PW-100 turbofan engine which was flight tested on an F-15 aircraft. The DEEC hardware and software throughout the F-15 flight envelope was evaluated. Real-time data reduction and data display systems were implemented. New test techniques and stronger coordination between the propulsion test engineer and pilot were developed which produced efficient use of test time, reduced pilot work load, and greatly improved quality data. The engine pressure ratio (EPR) control mode is demonstrated. It is found that the nonaugmented throttle transients and engine performance are satisfactory.

Myers, L. P.↗
NASA NTRS1986

Analysis of Jimsphere pairs for use in assessing Space Vehicle ascent capability

A data base of paired detailed wind profiles was developed for use in evaluating Shuttle Transportation System (STS) ascent capability. Since launch decision is based on a wind measured about 3.5 hr before launch, a data base of paired detailed profiles is needed. Method and technique on the reduction process and analysis is also presented. Guidelines used in selecting the pairs of profiles were established to insure a valid and representative data base. Sigma-U values for 3.5 hr at 12 km altitude show 8 percent increase from the transition case to the winter case and 18 percent decrease from the transition case to the summer case. Sigma-Values for 3.5 hr at 12 km altitude shows 12 percent increase from the transition case to the winter case and 17 percent decrease from the transition case to the summer case. A special feature of the 7- and 10.5-hr cases is that sigma-V increases by as much as 30 percent from the transition to the winter profiles. This large increase does not appear in the sigma-U data. Comparisons of the calculated values of 3.5 hr standard deviations of U and V with actual component deviations measured during space shuttle launch conditions confirm that the statistical values are representative.

Hill, C. K.↗