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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Flight testing a propulsion-controlled aircraft emergency flight control system on an F-15 airplane

Flight tests of a propulsion-controlled aircraft (PCA) system on an F-15 airplane have been conducted at the NASA Dryden Flight Research Center. The airplane was flown with all flight control surfaces locked both in the manual throttles-only mode and in an augmented system mode. In the latter mode, pilot thumbwheel commands and aircraft feedback parameters were used to position the throttles. Flight evaluation results showed that the PCA system can be used to land an airplane that has suffered a major flight control system failure safely. The PCA system was used to recover the F-15 airplane from a severe upset condition, descend, and land. Pilots from NASA, U.S. Air Force, U.S. Navy, and McDonnell Douglas Aerospace evaluated the PCA system and were favorably impressed with its capability. Manual throttles-only approaches were unsuccessful. This paper describes the PCA system operation and testing. It also presents flight test results and pilot comments.

Burcham, F. W., Jr.↗
NASA NTRS1997

Piloted Simulation Tests of Propulsion Control as Backup to Loss of Primary Flight Controls for a B747-400 Jet Transport

Partial failures of aircraft primary flight control systems and structural damages to aircraft during flight have led to catastrophic accidents with subsequent loss of lives (e.g. DC-10, B-747, C-5, B-52, and others). Following the DC-10 accident at Sioux City, Iowa in 1989, the National Transportation Safety Board recommended 'Encourage research and development of backup flight control systems for newly certified wide-body airplanes that utilize an alternate source of motive power separate from that source used for the conventional control system.' This report describes the concept of a propulsion controlled aircraft (PCA), discusses pilot controls, displays, and procedures; and presents the results of a PCA piloted simulation test and evaluation of the B747-400 airplane conducted at NASA Ames Research Center in December, 1996. The purpose of the test was to develop and evaluate propulsion control throughout the full flight envelope of the B747-400 including worst case scenarios of engine failures and out of trim moments. Pilot ratings of PCA performance ranged from adequate to satisfactory. PCA performed well in unusual attitude recoveries at 35,000 ft altitude, performed well in fully coupled ILS approaches, performed well in single engine failures, and performed well at aft cg. PCA performance was primarily limited by out-of-trim moments.

Bull, John↗
NASA NTRS1998

Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many f ree parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation,-, algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance.

Trejo, Leonard J.↗
NASA NTRS2002

Indirect Field Measurement of Wine-Grape Vineyard Canopy Leaf Area Index

Leaf area index (LAI) indirect measurements were made at 12 study plots in California's Napa Valley commercial wine-grape vineyards with a LI-COR LI-2000 Plant Canopy Analyzer (PCA). The plots encompassed different trellis systems, biological varieties, and planting densities. LAI ranged from 0.5 - 2.25 sq m leaf area/ sq m ground area according to direct (defoliation) measurements. Indirect LAI reported by the PCA was significantly related to direct LAI (r(exp 2) = 0.78, p less than 001). However, the PCA tended to underestimate direct LAI by about a factor of two. Narrowing the instrument's conical field of view from 148 deg to 56 deg served to increase readings by approximately 30%. The PCA offers a convenient way to discern relative differences in vineyard canopy density. Calibration by direct measurement (defoliation) is recommended in cases where absolute LAI is desired. Calibration equations provided herein may be inverted to retrieve actual vineyard LAI from PCA readings.

Johnson, Lee F.↗
NASA NTRS1998

High Performance Parallel Architectures

Traditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operational. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, aimed at concentrating the vital information and discarding redundant data. One such transformation, which is widely used in remote sensing, is the Principal Components Analysis (PCA). This report summarizes our progress on the development of a parallel PCA and its implementation on two Beowulf cluster configuration; one with fast Ethernet switch and the other with a Myrinet interconnection. Details of the implementation and performance results, for typical sets of multispectral and hyperspectral NASA remote sensing data, are presented and analyzed based on the algorithm requirements and the underlying machine configuration. It will be shown that the PCA application is quite challenging and hard to scale on Ethernet-based clusters. However, the measurements also show that a high- performance interconnection network, such as Myrinet, better matches the high communication demand of PCA and can lead to a more efficient PCA execution.

El-Ghazawi, Tarek↗
NASA NTRS2003

Ising model of cardiac thin filament activation with nearest-neighbor cooperative interactions

We have developed a model of cardiac thin filament activation using an Ising model approach from equilibrium statistical physics. This model explicitly represents nearest-neighbor interactions between 26 troponin/tropomyosin units along a one-dimensional array that represents the cardiac thin filament. With transition rates chosen to match experimental data, the results show that the resulting force-pCa (F-pCa) relations are similar to Hill functions with asymmetries, as seen in experimental data. Specifically, Hill plots showing (log(F/(1-F)) vs. log [Ca]) reveal a steeper slope below the half activation point (Ca(50)) compared with above. Parameter variation studies show interplay of parameters that affect the apparent cooperativity and asymmetry in the F-pCa relations. The model also predicts that Ca binding is uncooperative for low [Ca], becomes steeper near Ca(50), and becomes uncooperative again at higher [Ca]. The steepness near Ca(50) mirrors the steep F-pCa as a result of thermodynamic considerations. The model also predicts that the correlation between troponin/tropomyosin units along the one-dimensional array quickly decays at high and low [Ca], but near Ca(50), high correlation occurs across the whole array. This work provides a simple model that can account for the steepness and shape of F-pCa relations that other models fail to reproduce.

Evaluation Studies↗
NASA NTRS1999

Feature extraction of event-related potentials using wavelets: an application to human performance monitoring

This report describes the development and evaluation of mathematical models for predicting human performance from discrete wavelet transforms (DWT) of event-related potentials (ERP) elicited by task-relevant stimuli. The DWT was compared to principal components analysis (PCA) for representation of ERPs in linear regression and neural network models developed to predict a composite measure of human signal detection performance. Linear regression models based on coefficients of the decimated DWT predicted signal detection performance with half as many free parameters as comparable models based on PCA scores. In addition, the DWT-based models were more resistant to model degradation due to over-fitting than PCA-based models. Feed-forward neural networks were trained using the backpropagation algorithm to predict signal detection performance based on raw ERPs, PCA scores, or high-power coefficients of the DWT. Neural networks based on high-power DWT coefficients trained with fewer iterations, generalized to new data better, and were more resistant to overfitting than networks based on raw ERPs. Networks based on PCA scores did not generalize to new data as well as either the DWT network or the raw ERP network. The results show that wavelet expansions represent the ERP efficiently and extract behaviorally important features for use in linear regression or neural network models of human performance. The efficiency of the DWT is discussed in terms of its decorrelation and energy compaction properties. In addition, the DWT models provided evidence that a pattern of low-frequency activity (1 to 3.5 Hz) occurring at specific times and scalp locations is a reliable correlate of human signal detection performance. Copyright 1999 Academic Press.

Evoked Potentials↗
NASA NTRS2008

RXTE and BeppoSAX Observations of the Transient X-ray Pulsar XTE J 18591+083

We present observations of the 9.8 s X-ray pulsar XTE J159+083 made with the All-Sky Monitor (ASM) and Proportional Counter Array (PCA) on board the Rossi X-ray Timing Explorer (RXTE), and the Wide Field Cameras (WFC) on board BeppoSAX. The ASM data cover a 12 year time interval and show that an extended outburst occurred between approximately MJD50, 250, and 50, 460 (1996 June 16 to 1997 January 12). The ASM data excluding this outburst interval suggest a possible 61 day modulation. Eighteen sets of PCA observations were obtained over an approx. one month interval in 1999. The flux variability measured with the PCA appears consistent with the possible period found with the ASM. The PCA measurements of the pulse period showed it to decrease non-monotonically and then to increase significantly. Doppler shifts due to orbital motion rather than accretion torques appear to be better able to explain the pulse period changes. Observations with the WFC during the extended outburst give an error box which is consistent with a previously determined PCA error box but is significantly smaller. The transient nature of XTE J1859+083 and the length of its pulse period are consistent with it being a Be/neutral star binary. The possible 61 day orbital period would be of the expected length for a Be star system with a 9.8 s pulse period.

Corbet, R. H. D.↗
NASA NTRS2011

Jovian Chromophore Characteristics from Multispectral HST Images

The chromophores responsible for coloring the jovian atmosphere are embedded within Jupiter's vertical aerosol structure. Sunlight propagates through this vertical distribution of aerosol particles, whose colors are defined by omega-bar (sub 0)(lambda), and we remotely observe the culmination of the radiative transfer as I/F(lambda). In this study, we employed a radiative transfer code to retrieve omega-bar (sub 0)(lambda) for particles in Jupiter's tropospheric haze at seven wavelengths in the near-UV and visible regimes. The data consisted of images of the 2008 passage of Oval BA to the south of the Great Red Spot obtained by the Wide Field Planetary Camera 2 on-board the Hubble Space Telescope. We present derived particle colors for locations that were selected from 14 weather regions, which spanned a large range of observed colors. All omega-bar (sub 0)(lambda) curves were absorbing in the blue, and omega-bar (sub 0)(lambda) increased monotonically to approximately unity as wavelength increased. We found accurate fits to all omega-bar (sub 0)(lambda) curves using an empirically derived functional form: omega-bar (sub 0)(lambda) = 1 A exp(-B lambda). The best-fit parameters for the mean omega-bar (sub 0)(lambda) curve were A = 25.4 and B = 0.0149 for lambda in units of nm. We performed a principal component analysis (PCA) on our omega-bar (sub 0)(lambda) results and found that one or two independent chromophores were sufficient to produce the variations in omega-bar (sub 0)(lambda). A PCA of I/F(lambda) for the same jovian locations resulted in principal components (PCs) with roughly the same variances as the omega-bar (sub 0)(lambda) PCA, but they did not result in a one-to-one mapping of PC amplitudes between the omega-bar (sub 0)(lambda) PCA and I/F(lambda) PCA. We suggest that statistical analyses performed on I/ F(lambda) image cubes have limited applicability to the characterization of chromophores in the jovian atmosphere due to the sensitivity of 1/ F(lambda) to horizontal variations in the vertical aerosol distribution.

Strycker, Paul D.↗
NASA NTRS2012

Advances in the RXTE Proportional Counter Array Calibration: Nearing the Statistical Limit

During its 16 years of service Rossi X-ray Timing Explorer (RXTE) mission has provided an extensive archive of data, which will serve as a primary source of high cadence observation of variable X-ray sources for fast timing studies. It is, therefore, very important to have the most reliable calibration of RXTE instruments. The Proportional Counter Array (PCA) is the primary instrument on-board RXTE which provides data in 2-50 keY with higher than millisecond time resolution in up to 256 energy channels. In 2009 RXTE team revised the response residual minimization method used to derive the parameters of the PCA physical model. The procedure is now based on the residual minimization between the model spectrum for Crab nebula emission and a calibration data set consisting of a number of spectra from the Crab and the on-board Am241 calibration source, uniformly covering a whole RXTE span. The new method led to a much more effective model convergence and allowed for better understanding of the behavior of the PCA energy-to-channel relationship. It greatly improved the response matrix performance. We describe the new version of the RXTE/PCA response generator PCARMF vll.7 along with the corresponding energy-to-channel conversion table (version e05v04) and their difference from the previous releases of PCA calibration. The new PCA response adequately represents the spectrum of the calibration sources and successfully predicts the energy of the narrow iron emission line in Cas-A throughout the RXTE mission.

Shaposhnikov, Nikolai↗
NASA NTRS2017

Petrology of Anomalous Mafic Achondrite Polymict Breccia Pasamonte

The most common asteroidal igneous meteorites are eucrite-type basalts and gabbros - rocks composed of ferroan pigeonite and augite, calcic plagioclase, silica, ilmenite, troilite, Ca-phosphate, chromite and Fe-metal [1]. These rocks are thought to have formed on a single asteroid, widely considered to be 4 Vesta, along with howardites and diogenites [1, 2]. High precision O-isotopic analyses have shown that some eucrites have small, well-resolved O-isotopic differences from the group mean [3-5]. These Oanomalous eucrites are thought to hail from asteroidal parents that are distinct from that of eucrites [5]. Three O-anomalous eucrites are PCA 82502, PCA 91007 (paired) and Pasamonte, all of which have the same O-isotopic composition [5, 6]. Our petrologic studies have shown that PCA 82502 and PCA 91007 have well-resolved anomalies in low-Ca pyroxene Fe/Mn compared to eucrites [6]. Divalent Mn and Fe are homologous species that do not greatly fractionate during igneous processes; mafic mineral Fe/Mn can be used to fingerprint parent object sources [7]. Previous petrological studies of Pasamonte [8-10] have not yielded sufficiently precise Fe/Mn ratios to allow distinction of anomalies of the scale of those found for the PCA basalts. We have begun petrological study of Pasamonte for comparison with our results on normal and anomalous eucrites [6], and to constrain its origin.

Mittlefehldt, D. W.↗
DOE OSTI2024

Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10 –6 ) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10 –6 ) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10 –6 ) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Using principal component analysis to distinguish different dynamic phases in superconducting vortex matter

Vortices in type-II superconductors driven over random disorder are known to exhibit a remarkable variety of distinct nonequilibrium dynamical phases that arise owing to the competition between vortex-vortex interactions, the quenched disorder, and the drive. These include pinned states, elastic flows, plastic or disordered flows, and dynamically reordered moving crystal or moving smectic states. The plastic flow phases can be particularly difficult to characterize since the flows are strongly disordered. Here, we perform principal component analysis (PCA) on the positions and velocities of vortex matter moving over random disorder for different disorder strengths and drives. We find that PCA can distinguish the known dynamic phases as well as or better than previous measures based on transport signatures or topological defect densities. In addition, PCA recognizes distinct plastic flow regimes, a slowly changing channel flow and a moving amorphous fluid flow, that do not produce distinct signatures in the standard measurements. In conclusion, our results suggest that this position and velocity-based PCA approach could be used to characterize dynamic phases in a broader class of systems that exhibit depinning and nonequilibrium phase transitions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Application of Partial Least Squares Approaches to Pyroprocessing ER Data

Multivariate approaches show promise for application to process monitoring for safeguards of pyroprocessing. Past MPACT work explored the application of Principal Component Analysis (PCA) to detect off-normal conditions in pyroprocessing electrorefiner (ER) data from in the Hot Fuel Examination Facility (HFEF) at Idaho National Laboratory (INL) known as the Scalable Pyrochemical Recycling testbed (SPyRe) ER. PCA, however, does not consider the output variables. In FY24, multivariate analysis was extended from PCA to Partial Least Squares (PLS) analysis. PLS maximizes the variance between both the input signals and output variables. In the case of this work, PLS was applied in two different manners: Predictive PLS and Discriminant PLS. Predictive PLS maximizes the covariance between the process variables of the ER and the measured U concentration from in-situ voltammetry. Discriminant PLS maximizes the covariance between the process variables and a set of training process “states” such as known off-normal conditions. By projecting into the latent variable space in PLS, the process variables can be regressed onto the outputs and predictions can be made for new data sets. In this work, by applying predictive PLS, a penalized non-linear PLS approach was able to make predictions of concentration based on test and training data and detect when operations were off-normal. However, the predictive PLS does not classify the signals to which off-normal operations are attributable. Discriminant PLS can be used to classify off-normal operations but is inadequate to properly classify specific off-normal classes like power supply faults when the Discriminant PLS model is only specifically trained to detect that off-normal class. When all faults are trained against the observation data, all three operational classes are accurately classified and distinguished. Thus, future application of latent variable techniques should not select any given method, but should use a mixture of PCA, Predictive PLS, and Discriminant PLS.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRS1981

Development of a solar receiver for an organic Rankine cycle engine

A prototype power conversion assembly (PCA) developed by an American aerospace company is considered. The PCA will be mounted at the focal point of a 12 meter parabolic dish and will produce approximately 20 kW of 3 kHz ac power to a ground-mounted rectifier. The PCA includes a cavity receiver coupled to an organic Rankine cycle engine. The engine working fluid is toluene. The performance goals of the receiver design are to maximize both the thermal efficiency and the heat capacity of the core. The latter goal is desired for stabilizing the PCA operation during intermittent cloud cover. The receiver design is based upon the utilization of a direct-heated copper shell. It was necessary to develop a feasible manufacturing process for assuring a good braze joint between the stainless steel, containing the toluene, and the copper shell.

Haskins, H. J.↗
NASA NTRS1985

Martian Fretted Terrain Morphometry Interpreted Using Principal Components Analysis

Martian fretted terrain is an extensive area of complex topography characterized by smooth, flat-floor valleys and mesas separated by abrupt escarpments along the boundary of heavily cratered uplands. The Deuteronilus-Protonilus region of the fretted terrain between latitude 35 degrees N and 50 degrees N and between 305 degrees W and 350 degrees W longitude was selected for quantitative landform study with the use of principal components analysis (PCA). The morphometry of upland mesa remnants and debris aprons was digitized with respect to latitude, longitude, and distance from the cratered terrain boundary (CTB). The PCA demonstrates that there are definable spatial variations in morphology with respect to longitude and latitude which suggest that the degradation along the CTB proceeded toward the southwest. The PCA also showed that there are organized relationships within the fretted landforms irrespective of their location. Finally, PCA indicated that an orientation of the degraded mesas exists which may be controlled by structure.

Kochell, R. C.↗
NASA NTRS1986

Brayton cycle solarized advanced gas turbine

Described is the development of a Brayton Engine/Generator Set for solar thermal to electrical power conversion, authorized under DOE/NASA Contract DEN3-181. The objective was to design, fabricate, assemble, and test a small, hybrid, 20-kW Brayton-engine-powered generator set. The latter, called a power conversion assembly (PCA), is designed to operate with solar energy obtained from a parobolic dish concentrator, 11 meters in diameter, or with fossil energy supplied by burning fuels in a combustor, or by a combination of both (hybrid model). The CPA consists of the Brayton cycle engine, a solar collector, a belt-driven 20-kW generator, and the necessary control systems for automatic operation in solar-only, fuel-only, and hybrid modes to supply electrical power to a utility grid. The original configuration of the generator set used the GTEC Model GTP36-51 gas turbine engine for the PCA prime mover. However, subsequent development of the GTEC Model AGT101 led to its selection as the powersource for the PCA. Performance characteristics of the latter, thermally coupled to a solar collector for operation in the solar mode, are presented. The PCA was successfully demonstrated in the fuel-only mode at the GTEC Phoenix, Arizona, facilities prior to its shipment to Sandia National Laboratory in Albuquerque, New Mexico, for installation and testing on a test bed concentractor (parabolic dish). Considerations relative to Brayton-engine development using the all-ceramic AGT101 when it becomes available, which would satisfy the DOE heat engine efficiency goal of 35 to 41 percent, are also discussed in the report.

Source record↗
NASA NTRS1993

Fourier domain target transformation analysis in the thermal infrared

Remote sensing uses of principal component analysis (PCA) of multispectral images include band selection and optimal color selection for display of information content. PCA has also been used for quantitative determination of mineral types and abundances given end member spectra. The preliminary results of the investigation of target transformation PCA (TTPCA) in the fourier domain to both identify end member spectra in an unknown spectrum, and to then calculate the relative concentrations of these selected end members are presented. Identification of endmember spectra in an unknown sample has previously been performed through bandmatching, expert systems, and binary classifiers. Both bandmatching and expert system techniques require the analyst to select bands or combinations of bands unique to each endmember. Thermal infrared mineral spectra have broad spectral features which vary subtly with composition. This makes identification of unique features difficult. Alternatively, whole spectra can be used in the classification process, in which case there is not need for an expert to identify unique spectra. Use of binary classifiers on whole spectra to identify endmember components has met with some success. These techniques can be used, along with a least squares fit approach on the endmembers identified, to derive compositional information. An alternative to the approach outlined above usese target transformation in conjunction with PCA to both identify and quantify the composition of unknown spectra. Preprocessing of the library and unknown spectra into the fourier domain, and using only a specific number of the components, allows for significant data volume reduction while maintaining a linear relationship in a Beer's Law sense. The approach taken here is to iteratively calculate concentrations, reducing the number of endmember components until only non-negative concentrations remain.

Anderson, D. L.↗