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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Application of Temperature Sensitivities During Iterative Strain-Gage Balance Calibration Analysis

A new method is discussed that may be used to correct wind tunnel strain-gage balance load predictions for the influence of residual temperature effects at the location of the strain-gages. The method was designed for the iterative analysis technique that is used in the aerospace testing community to predict balance loads from strain-gage outputs during a wind tunnel test. The new method implicitly applies temperature corrections to the gage outputs during the load iteration process. Therefore, it can use uncorrected gage outputs directly as input for the load calculations. The new method is applied in several steps. First, balance calibration data is analyzed in the usual manner assuming that the balance temperature was kept constant during the calibration. Then, the temperature difference relative to the calibration temperature is introduced as a new independent variable for each strain--gage output. Therefore, sensors must exist near the strain--gages so that the required temperature differences can be measured during the wind tunnel test. In addition, the format of the regression coefficient matrix needs to be extended so that it can support the new independent variables. In the next step, the extended regression coefficient matrix of the original calibration data is modified by using the manufacturer specified temperature sensitivity of each strain--gage as the regression coefficient of the corresponding temperature difference variable. Finally, the modified regression coefficient matrix is converted to a data reduction matrix that the iterative analysis technique needs for the calculation of balance loads. Original calibration data and modified check load data of NASA's MC60D balance are used to illustrate the new method.

Ulbrich, N.↗
NASA NTRS1973

Design and flight testing of a nullable compressor face rake

A compressor face rake with an internal valve arrangement to permit nulling was designed, constructed, and tested in the laboratory and in flight at the NASA Flight Research Center. When actuated by the pilot in flight, the nullable rake allowed the transducer zero shifts to be determined and then subsequently removed during data reduction. Design details, the fabrication technique, the principle of operation, brief descriptions of associated digital zero-correction programs and the qualification tests, and test results are included. Sample flight data show that the zero shifts were large and unpredictable but could be measured in flight with the rake. The rake functioned reliably and as expected during 25 hours of operation under flight environmental conditions and temperatures from 230 K (-46 F) to greater than 430 K (314 F). The rake was nulled approximately 1000 times. The in-flight zero-shift measurement technique, as well as the rake design, was successful and should be useful in future applications, particularly where accurate measurements of both steady-state and dynamic pressures are required under adverse environmental conditions.

Holzman, J. K.↗
DOE OSTI2019

Model reduction for fractured porous media: a machine learning approach for identifying main flow pathways

Discrete fracture networks (DFN) are often used to model flow and transport in fractured porous media. The accurate resolution of flow and transport behavior on a large DFN involving thousands of fractures is computationally expensive. This makes uncertainty quantification studies of quantities of interest such as travel time through the network computationally intractable, since hundreds to thousands of runs of the DFN model are required to get bounds on the uncertainty of the predictions. Prior works on the subject demonstrated that the complexity of a DFN could be reduced by considering a sub-network of it (often termed a “backbone” sub-network), one whose flow and transport properties were then shown to be similar to that of the full network. The technique is tantamount to partitioning the complete set of fractures of a network into two disjoint sets, one of which is the backbone sub-network while the other its complement. It is in this context that we present a system-reduction technique for DFNs using supervised machine learning via a Random Forest Classifier that selects a backbone sub-network from the full set of fractures. The in-sample errors (in terms of precision and recall scores) of the trained classifier are found to be very accurate indicators of the out-of-sample errors, thus exhibiting that the classifier generalizes well to test data. Moreover, this system-reduction technique yields sub-networks as small as 12% of the full DFN that still recover transport characteristics of the full network such as the peak dosage and tailing behavior for late times. Most importantly, the sub-networks remain connected, and their size can be controlled by a single dimensionless parameter. Furthermore, measures of KL-divergence and KS-statistic for the breakthrough curves of the sub-networks with respect to the full network show physically realistic trends in that the measures decrease monotonically as the size of the sub-networks increase. In conclusion, the computational efficiency gained by this technique depends on the size of the sub-network, but large reductions in computational time can be expected for small sub-networks, yielding as much as 90% computational savings for sub-networks that are as small as 10-12% of the full network.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1982

Mission definition study for a VLBI station utilizing the Space Shuttle

The uses of the Space Shuttle transportation system for orbiting VeryLong-Baseline Interferometry (OVLBI) were examined, both with respect to technical feasibility and its scientific possibilities. The study consisted of a critical look at the adaptability of current technology to an orbiting environment, the suitability of current data reduction facilities for the new technique, and a review of the new science that is made possible by using the Space Shuttle as a moving platform for a VLBI terminal in space. The conclusions are positive in all respects: no technological deficiencies exist that would need remedy, the data processing problem can be handled easily by straightforward adaptations of existing systems, and there is a significant new research frontier to be explored, with the Space Shuttle providing the first step. The VLBI technique utilizes the great frequency stability of modern atomic time standards, the power of integrated circuitry to perform real-time signal conditioning, and the ability of magnetic tape recorders to provide essentially error-free data recording, all of which combine to permit the realization of radio interferometry at arbitrarily large baselines.

Burke, B. F.↗
DOE OSTI2024

Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values

Non-negative matrix factorization (NMF) is a dimensionality reduction technique that has shown promise for analyzing noisy data, especially astronomical data. For these datasets, the observed data may contain negative values due to noise even when the true underlying physical signal is strictly positive. Prior NMF work has not treated negative data in a statistically consistent manner, which becomes problematic for low signal-to-noise data with many negative values. In this paper we present two algorithms, Shift-NMF and Nearly-NMF, that can handle both the noisiness of the input data and also any introduced negativity. Both of these algorithms use the negative data space without clipping or masking and recover non-negative signals without any introduced positive offset that occurs when clipping or masking negative data. We demonstrate this numerically on both simple and more realistic examples, and prove that both algorithms have monotonically decreasing update rules.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1974

Lateral conduction effects on heat-transfer data obtained with the phase-change paint technique

A computerized tool, CAPE, (Conduction Analysis Program using Eigenvalues) has been developed to account for lateral heat conduction in wind tunnel models in the data reduction of the phase-change paint technique. The tool also accounts for the effects of finite thickness (thin wings) and surface curvature. A special reduction procedure using just one time of melt is also possible on leading edges. A novel iterative numerical scheme was used, with discretized spatial coordinates but analytic integration in time, to solve the inverse conduction problem involved in the data reduction. A yes-no chart is provided which tells the test engineer when various corrections are large enough so that CAPE should be used. The accuracy of the phase-change paint technique in the presence of finite thickness and lateral conduction is also investigated.

Maise, G.↗
NASA NTRS1991

A long-range laser velocimeter

A long-range laser velocimeter (LV) developed for remote operation from within the flow fields of large wind tunnels is described. Emphasis is placed on recent improvements in optical hardware as well as recent additions to data acquisition and processing techniques. The method used for data reduction of photon resolved signals is outlined in detail, and measurement accuracy is discussed. To study the performance of the LV and verify the measurement accuracy, laboratory measurements were made in the flow field of a 10-cm-diameter, 30-m/s axisymetric jet. The measured velocity and turbulence intensity surveys are compared with measurements made with a hot-wire anemometer. Additionally, the LV was used during the flow calibration of the 80-ft x 120-ft wind tunnel to measure the test-section boundary-layer thickness at the maximum wind tunnel speed of 51.5 m/s. The requirements and techniques used to seed the flow are discussed, and boundary-layer surveys of mean velocity and turbulence intensity of the streamwise component and the component normal to the surface are presented. The streamwise component of mean velocity is compared with data obtained with a total pressure rake.

Reinath, Michael S.↗
NASA NTRS1991

Channel coding for digital HDTV terrestrial broadcasting

The Federal Communications Commission of the United States has ruled that high-definition television (HDTV) will occupy no more than 6 MHz of the VHF and UHF bands now used for conventional TV. In order to transmit the HDTV signal in 6 MHz, the four United States digital HDTV proponents, the DigiCipher, DSC-HDTV, ADTV, and ATVA-P systems, are reducing the video data rate of HDTV to 15-17 Mb/s, a compression ratio of approximately 60-70 times. The high compression dictates that channel coding be used to avoid block errors and multiframe error propagation. High efficiency in channel utilization required by the 6-MHz limitation means that the channel must be properly equalized and that the multipath and interfering signals must be severely limited. The channel coding techniques used for error reduction include data interleaving, error detection and replacement, and error correction at different levels of protection for bits and blocks of unequal importance.

Beakley, Guy W.↗
DOE OSTI2019

Inverse regression for ridge recovery: a data-driven approach for parameter reduction in computer experiments

Parameter reduction can enable otherwise infeasible design and uncertainty studies with modern computational science models that contain several input parameters. In statistical regression, techniques for sufficient dimension reduction (SDR) use data to reduce the predictor dimension of a regression problem. A computational scientist hoping to use SDR for parameter reduction encounters a problem: a computer prediction is best represented by a deterministic function of the inputs, so data comprised of computer simulation queries fail to satisfy the SDR assumptions. To address this problem, here we interpret SDR methods sliced inverse regression (SIR) and sliced average variance estimation (SAVE) as estimating the directions of a ridge function, which is a composition of a low-dimensional linear transformation with a nonlinear function. Within this interpretation, SIR and SAVE estimate matrices of integrals whose column spaces are contained in the ridge directions’ span; we analyze and numerically verify convergence of these column spaces as the number of computer model queries increases. Moreover, we show example functions that are not ridge functions but whose inverse conditional moment matrices are low-rank. Consequently, the computational scientist should beware when using SIR and SAVE for parameter reduction, since SIR and SAVE may mistakenly suggest that truly important directions are unimportant.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

SO(3)-invariant PCA with application to molecular data

Principal component analysis (PCA) is a fundamental technique for dimensionality reduction and denoising; however, its application to three-dimensional data with arbitrary orientations -- common in structural biology -- presents significant challenges. A naive approach requires augmenting the dataset with many rotated copies of each sample, incurring prohibitive computational costs. In this paper, we extend PCA to 3D volumetric datasets with unknown orientations by developing an efficient and principled framework for SO(3)-invariant PCA that implicitly accounts for all rotations without explicit data augmentation. By exploiting underlying algebraic structure, we demonstrate that the computation involves only the square root of the total number of covariance entries, resulting in a substantial reduction in complexity. We validate the method on real-world molecular datasets, demonstrating its effectiveness and opening up new possibilities for large-scale, high-dimensional reconstruction problems.

Fraiman, Michael [Tel Aviv Univ., Tel Aviv (Israel↗
NASA NTRS1975

EMGAN: A computer program for time and frequency domain reduction of electromyographic data

An experiment in electromyography utilizing surface electrode techniques was developed for the Apollo-Soyuz test project. This report describes the computer program, EMGAN, which was written to provide first order data reduction for the experiment. EMG signals are produced by the membrane depolarization of muscle fibers during a muscle contraction. Surface electrodes detect a spatially summated signal from a large number of muscle fibers commonly called an interference pattern. An interference pattern is usually so complex that analysis through signal morphology is extremely difficult if not impossible. It has become common to process EMG interference patterns in the frequency domain. Muscle fatigue and certain myopathic conditions are recognized through changes in muscle frequency spectra.

Hursta, W. N.↗
DOE OSTI2025

Generative learning for slow manifolds and bifurcation diagrams

In dynamical systems characterized by separation of time scales, the approximation of so called “slow manifolds”, on which the long term dynamics lie, is a useful step for model reduction. Initializing on such slow manifolds is a useful step in modeling, since it circumvents fast transients, and is crucial in multiscale algorithms (like the equation-free approach) alternating between fine scale (fast) and coarser scale (slow) simulations. In a similar spirit, when one studies the infinite time dynamics of systems depending on parameters, the system attractors (e.g., its steady states) lie on bifurcation diagrams (curves for one-parameter continuation, and more generally, on manifolds in state parameter space. Sampling these manifolds gives us representative attractors (here, steady states of ODEs or PDEs) at different parameter values. Algorithms for the systematic construction of these manifolds (slow manifolds, bifurcation diagrams) are required parts of the “traditional” numerical nonlinear dynamics toolkit. In more recent years, as the field of Machine Learning develops, conditional score-based generative models (cSGMs) have been demonstrated to exhibit remarkable capabilities in generating plausible data from target distributions that are conditioned on some given label. It is tempting to exploit such generative models to produce samples of data distributions (points on a slow manifold, steady states on a bifurcation surface) conditioned on (consistent with) some quantity of interest (QoI, observable). In this work, we present a framework for using cSGMs to quickly (a) initialize on a low-dimensional (reduced-order) slow manifold of a multi-time-scale system consistent with desired value(s) of a QoI (a “label”) on the manifold, and (b) approximate steady states in a bifurcation diagram consistent with a (new, out-of-sample) parameter value. This conditional sampling can help uncover the geometry of the reduced slow-manifold and/or approximately “fill in” missing segments of steady states in a bifurcation diagram. Finally, the quantity of interest, which determines how the sampling is conditioned, is either known a priori or identified using manifold learning-based dimensionality reduction techniques applied to the training data.

Dynamical systems↗
NASA NTRS1995

The performance and calibration of WFPC2 on the Huble Space Telescope

The Wide Field Planetary Camera 2 (WFPC2) was installed in the Hubble Space Telescope (HST) in 1993 December. Since then, the instrument has been providing high-quality images. A significant among of calibration data has been collected to aid in the understanding of the on-orbit performance of the instrument. Generally, the behavior of the camera is similar to its performance during the system-level thermal vacuum test at JPL in 1993 May. Surprises were a significant charge-transfer-efficiency (CTE) problem and a significant growth rate in hot pixels at the original operating temperature of the CCDs (-76 deg C). The operating temperature of the WFPC2 CCDs was changed to -88 deg C on 1994 April 23, and significant improvements in CTE and hot pixels are seen at this temperature. In this paper we describe the on-orbit performance of the WFPC2. We discuss the optical and thermal history, the instrument throughput and stability, the Point Spread Function (PSF), the effects of undersampling on photometry, the properties of cosmic rays observed on-orbit, and the geometric distortion in the camera. We present the best techniques for the reduction of WFPC2 data, and describe the construction of calibration products including superbiases, superdarks, and flat fields.

Holtzman, Jon↗
DOE OSTI2026

Overview of IMPACT Data Acquisition System and Data Reduction Process

This report documents the development of the data acquisition system (DAS) and data reduction methodologies for the Irradiated Material Property Accelerated Characterization Test (IMPACT) experiment at the Advanced Test Reactor (ATR). The IMPACT experiment is designed to enable in-pile measurement of thermal conductivity in metallic nuclear fuels, specifically U-10Zr, using an instrumented thermal conductivity probe. The DAS supports both passive temperature monitoring and active thermal interrogation of the probe through controlled AC and DC excitation. Significant modifications to laboratory-scale systems were required to accommodate the higher resistance paths associated with the in-pile application. Custom electronics and relay-controlled measurement sequencing were developed to enable the measurement and sufficient power delivery to the sensing region. A reduced-order, axisymmetric thermal model based on the thermal quadrupoles method is presented to support data interpretation. This model enables efficient evaluation of transient heat transfer behavior and facilitates solution of the inverse problem required to extract thermal properties from measured signals. Multiple boundary condition formulations are discussed to address varying experimental time scales and geometries. Additionally, machine learning techniques are introduced to support data reduction and improve confidence in inverse solutions. Convolutional neural networks are applied to identify the presence of gas gaps and other evolving geometric features that significantly impact thermal response during irradiation. These efforts contribute to the broader integration of digital twin frameworks and real-time modeling capabilities within the Advanced Fuels Campaign.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2026

Scientific Data Compression for Large Scale Computational Fluid Dynamics (CFD) Simulations

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and General Electric (GE) investigated methods for reducing the size of large computational fluid dynamics (CFD) simulation datasets using scientific data compression techniques. The work focused on adapting the MultiGrid Adaptive Reduction of Data (MGARD) compression framework and integrating it with high-performance I/O and visualization tools used in CFD workflows. MGARD uses hierarchical multilevel decomposition to enable error-controlled compression of floating-point scientific data while preserving quantities of interest. During the project, MGARD compression was integrated with the ADIOS I/O framework and visualization tools such as ParaView to enable efficient storage, transfer, and analysis of simulation data. The collaboration also explored approaches for improving compression performance for CFD data defined on unstructured meshes. Results demonstrate that scientific data compression can significantly reduce storage requirements and improve data management for large-scale CFD simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Deliver Signal Phase and Timing (SPAT) for Energy Optimization of Vehicle Cohort Via Cloud-Computing and LTE Communications

Predictive Signal Phase and Timing (SPAT) message set is one fundamental building block for vehicle-to-infrastructure (V2I) applications such as Eco-Approach and Departure (EAD) at traffic signal controlled urban intersections. Among the two complementary communication methods namely short-range sidelink (PC5) and long-range cellular radio link (Uu), this paper documents the work with long-range link: the complete data chain includes connecting to the traffic signals via existing backhaul communication network, collecting the raw signal phase state data, predicting the signal state changes and delivering the SPAT data via a geofenced service to requests over HTTP protocols. An Application Programming Interface (API) library is developed to support various cellular data transmission reduction and latency improvement techniques. An emulation-based algorithm is applied to predict the traffic signal state changes to provide adequate prediction horizon (e.g., at minimum 2 minutes) for the cohort energy optimization. In fact, the same connectivity and SPAT delivery methodology has been applied to traffic signalized intersections nationwide in the United States upon public agency approvals for access to their firewalled traffic control network and signal control systems or directly to individual controllers. This methodology proves its effectiveness and potential for rapid growth of such SPAT deliveries at mass production scale without needing infrastructure hardware retrofit or excessive communication means. To support the energy optimization of light and heavy-duty vehicle cohorts of mixed automation and propulsion systems (EV, ICE and hybrid), the connection and SPAT deliveries at two sites were completed, including public roads in Washtenaw County, Michigan and closed track test sites at American Center for Mobility (ACM) in Ypsilanti, Michigan. However, only closed test track results at ACM will be presented in this paper. A neuroevolution based optimizer is developed and implemented to control the speed of a vehicle cohort with different propulsion systems and automation levels. Closed track tests showed significant energy savings of the cohort operation.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS1972

An evaluation of random analysis methods for the determination of panel damping

An analysis is made of steady-state and non-steady-state methods for the measurement of panel damping. Particular emphasis is placed on the use of random process techniques in conjunction with digital data reduction methods. The steady-state methods considered use the response power spectral density, response autocorrelation, excitation-response crosspower spectral density, or single-sided Fourier transform (SSFT) of the response autocorrelation function. Non-steady-state methods are associated mainly with the use of rapid frequency sweep excitation. Problems associated with the practical application of each method are evaluated with specific reference to the case of a panel exposed to a turbulent airflow, and two methods, the power spectral density and the single-sided Fourier transform methods, are selected as being the most suitable. These two methods are demonstrated experimentally, and it is shown that the power spectral density method is satisfactory under most conditions, provided that appropriate corrections are applied to account for filter bandwidth and background noise errors. Thus, the response power spectral density method is recommended for the measurement of the damping of panels exposed to a moving airflow.

Bhat, W. V.↗