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Results for “Functional Principal Components”

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
DOE OSTI2019

Seasonal-to-Interannual Response of Southern Ocean Mixed Layer Depth to the Southern Annular Mode from a Global 1/10° Ocean Model

The relationship between the southern annular mode (SAM) and Southern Ocean mixed layer depth (MLD) is investigated herein using a global 0.1° resolution ocean model. The SAM index is defined as the principal component time series of the leading empirical orthogonal function of extratropical sea level pressure from September to December, when the zonally symmetric SAM feature is most prominent. Following positive phases of the SAM, anomalous deep mixed layers occur in the subsequent fall season, starting in May, particularly in the southeast Pacific. Composite analyses reveal that for positive SAM phases enhanced surface cooling caused by anomalously strong westerlies weakens the stratification of the water column, leading to deeper mixed layers during spring when the SAM signal is at its strongest. During the subsequent summer, the surface warms and the mixed layer shoals. However, beneath the warm surface layer, anomalously weak stratification persists throughout the summer and into fall. When the surface cools again during fall, the mixed layer readily deepens due to this weak interior stratification, a legacy from the previous springtime conditions. Therefore, the spring SAM–fall MLD relationship is interpreted here as a manifestation of reemergence of interior water mass anomalies. The opposite occurs after negative phases of the SAM, with anomalously shallow mixed layers resulting. Additional analyses reveal that for the MLD region in the southeast Pacific, the effects of salinity variations and Ekman heat advection are negligible, although Ekman heat transport may play an important role in other regions where mode water is formed, such as south of Australia and in the Indian Ocean.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1985

Forest Species Identification with High Spectral Resolution Data

Data collected over the Sleeping Bear Sand Dunes Test Site and the Saginaw Forest Test Site (Michigan) with the JPL Airborne Imaging Spectrometer and the Collins' Airborne Spectroradiometer are being used for forest species identification. The linear discriminant function has provided higher identification accuracies than have principal components analyses. Highest identification accuracies are obtained in the 450 to 520 nm spectral region. Spectral bands near 1,300, 1,685 and 2,220 nm appear to be important, also.

Olson, C. E., Jr.↗
NASA NTRS1991

Intraseasonal oscillations in the global atmosphere. I - Northern Hemisphere and tropics

Oscillatory modes in the Northern Hemisphere and in the tropics were examined systematically. The 700 mb heights were used to analyze extratropical oscillations, and the outgoing longwave radiation to study tropical oscillations in convection. All datasets were band-pass filtered to focus on the intraseasonal (IS) band of 10-120 days. Leading spatial patterns of variability were obtained by applying empirical orthogonal function analysis to these IS data. The leading principal components were subjected to singular spectrum analysis. In the Northern Hemisphere, there are two important modes of oscillation with periods near 48 and 23 days, respectively. The 48-day mode is the most important of the two. It has both traveling and standing components, and is dominated by a zonal wavenumber two. The 23-day mode has the spatial structure and propagation properties described by Branstator and by Kushnir (1987). In the tropics, the 40-50 day oscillation documented by Madden and Julian (1972), Weickmann (1983), Lau, and their colleagues, dominates the Indian and Pacific oceans from 60 deg E to the date line. From 170 deg W to 90 deg W, however, a 24-28 day oscillation is equally strong. The extratropical modes are often independent of, and sometimes lead, the tropical modes.

Ghil, Michael↗
NASA NTRS2005

Foam core shield (FCS) systems : a new dual - purpose technology for shielding against meteoroid strike damage and for thermal control of spacecrafts/satellite components

A new technology is being developed that can protect spacecraft and satellite components against damage from meteoroid strikes and control the thermal environment of the protected components. This technology, called Foam Core Shield (FCS) systems, has the potential to replace the multi-layer insulation blankets (MLI) that have been used on spacecraft for decades. In order to be an attractive candidate for replacing MLI, FCS systems should not only provide superior protection against meteoroid strikes but also provide an equal or superior ability to control the temperature of the protected component. Properly designed FCS systems can provide these principal functions, meteoroid strike protection and thermal control, with lower system mass and a smaller system envelope than ML.

thermal environment↗
NASA NTRS1993

Standards in the architectural effort

The NASA Scientific and Technical Information (STI) Project Architecture Group was established to build a framework for modernization, provide guidance and direction for standardization and integration efforts, and to develop a set of concepts, interfaces, and entities to specifiy standards. The STI data processing architecture is a set of diagrams and descriptions that characterize the principal functions and services as well as hardware and software components. The use of standards promotes scalability and interchangeability.

Markham, Howard↗
NASA NTRS2006

Annual Cycles of Surface Shortwave Radiative Fluxes

The annual cycles of surface shortwave flux are investigated using the 8-yr dataset of the surface radiation budget (SRB) components for the period July 1983-June 1991. These components include the downward, upward, and net shortwave radiant fluxes at the earth's surface. The seasonal cycles are quantified in terms of principal components that describe the temporal variations and empirical orthogonal functions (EOFs) that describe the spatial patterns. The major part of the variation is simply due to the variation of the insolation at the top of the atmosphere, especially for the first term, which describes 92.4% of the variance for the downward shortwave flux. However, for the second term, which describes 4.1% of the variance, the effect of clouds is quite important and the effect of clouds dominates the third term, which describes 2.4% of the variance. To a large degree the second and third terms are due to the response of clouds to the annual cycle of solar forcing. For net shortwave flux at the surface, similar variances are described by each term. The regional values of the EOFs are related to climate classes, thereby defining the range of annual cycles of shortwave radiation for each climate class.

Wilber, Anne C.↗
DOE OSTI2021

Accurate Thermochemistry of Complex Lignin Structures via Density Functional Theory, Group Additivity, and Machine Learning

A molecular-level understanding of lignin structures and bond dissociation energies could facilitate depolymerization technologies. Still, this information is currently limited due to the lack of databases and the simplification of surrogate models. Here, substitution effects on seven common linkages in lignin polymers are systematically investigated. An automated reaction network generator is employed to create a database of structures. A new group additivity (GA) model based on principal component analysis (PCA) descriptors is introduced and trained on gas-phase density functional theory data of 4100 species at the M06-2X/6-311++G(d,p) level. Hydrogen bonds, local steric, and nonaromatic ring contributions are also incorporated. Lastly, we improve the accuracy of the group additivity model to reach the G4 theory by computing a data set of 770 species at this level and using a data fusion approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing

Big Area Additive Manufacturing (BAAM) of composites requires significant time, energy, and material, so it is critical to reduce production inefficiencies to make functional parts without multiple iterations. Statistical process control coupled with Principal Component Analysis (PCA) is a powerful technique that provides a quick, computationally inexpensive, and intuitive way for operators to detect defects that form in a manufacturing process without massive datasets. Recently, a combined index that is a weighted sum of the Hotelling's T 2 and squared residual error statistics has been proposed that can be monitored in one chart, improving interpretation accuracy and simplicity. However, the literature does not offer a formal method to optimise the weights. Here, we introduce two new approaches to the traditional weight selection approach using simulated and BAAM image data. Approach 1 uses a theoretically motivated optimum inspired by probabilistic principal component analysis. Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. Surprisingly, we also show that choosing a more complex PCA model has a minimal negative impact on anomaly detection performance compared to a more simplistic model.

3-dimensional printing↗
NASA NTRS1985

CELCAP: A Computer Model for Cogeneration System Analysis

A description of the CELCAP cogeneration analysis program is presented. A detailed description of the methodology used by the Naval Civil Engineering Laboratory in developing the CELCAP code and the procedures for analyzing cogeneration systems for a given user are given. The four engines modeled in CELCAP are: gas turbine with exhaust heat boiler, diesel engine with waste heat boiler, single automatic-extraction steam turbine, and back-pressure steam turbine. Both the design point and part-load performances are taken into account in the engine models. The load model describes how the hourly electric and steam demand of the user is represented by 24 hourly profiles. The economic model describes how the annual and life-cycle operating costs that include the costs of fuel, purchased electricity, and operation and maintenance of engines and boilers are calculated. The CELCAP code structure and principal functions of the code are described to how the various components of the code are related to each other. Three examples of the application of the CELCAP code are given to illustrate the versatility of the code. The examples shown represent cases of system selection, system modification, and system optimization.

Source record↗
NASA NTRS1988

Constraints on the origin of Archean trondhjemites based on phase relationships of Nuk gneiss with H2O at 15 kbar

Constraints on the origin of Archean trondhemites were investigated by studying the T-X(H2O) phase relationships, as a function of water content, of the trondhjemitic Nuk gneiss (the principal component of the second phase of Archean igneous activity in the Godthab region, Greenland) with water at 15-kbar pressure. High-pressure experiments were completed in a piston-cylinder apparatus wih samples enclosed in gold capsules, and phase assemblages exposed on polished surfaces of run products and analyzed using optical microscope, SEM, and electron microprobe. The data obtained were found to be consistent with the multistage development model for the Archean crust in Finland, developed by Jahn et al. (1984), which calls for at least three stages of crustal development.

Johnston, A. Dana↗
NASA NTRS1991

Psychophysical evaluation of three-dimensional auditory displays

Work during this reporting period included the completion of our research on the use of principal components analysis (PCA) to model the acoustical head related transfer functions (HRTFs) that are used to synthesize virtual sources for three dimensional auditory displays. In addition, a series of studies was initiated on the perceptual errors made by listeners when localizing free-field and virtual sources. Previous research has revealed that under certain conditions these perceptual errors, often called 'confusions' or 'reversals', are both large and frequent, thus seriously comprising the utility of a 3-D virtual auditory display. The long-range goal of our work in this area is to elucidate the sources of the confusions and to develop signal-processing strategies to reduce or eliminate them.

Wightman, Frederic L.↗
NASA NTRS2017

Large-Scale Circulation and Climate Variability

The causes of regional climate trends cannot be understood without considering the impact of variations in large-scale atmospheric circulation and an assessment of the role of internally generated climate variability. There are contributions to regional climate trends from changes in large-scale latitudinal circulation, which is generally organized into three cells in each hemisphere-Hadley cell, Ferrell cell and Polar cell-and which determines the location of subtropical dry zones and midlatitude jet streams. These circulation cells are expected to shift poleward during warmer periods, which could result in poleward shifts in precipitation patterns, affecting natural ecosystems, agriculture, and water resources. In addition, regional climate can be strongly affected by non-local responses to recurring patterns (or modes) of variability of the atmospheric circulation or the coupled atmosphere-ocean system. These modes of variability represent preferred spatial patterns and their temporal variation. They account for gross features in variance and for teleconnections which describe climate links between geographically separated regions. Modes of variability are often described as a product of a spatial climate pattern and an associated climate index time series that are identified based on statistical methods like Principal Component Analysis (PC analysis), which is also called Empirical Orthogonal Function Analysis (EOF analysis), and cluster analysis.

Perlwitz, J.↗
NASA NTRS1990

Regional climate change predictions from the Goddard Institute for Space Studies high resolution GCM

Model simulations of global climate change are seen as an essential component of any program aimed at understanding human impact on the global environment. A major weakness of current general circulation models (GCMs), however, is their inability to predict reliably the regional consequences of a global scale change, and it is these regional scale predictions that are necessary for studies of human/environmental response. This research is directed toward the development of a methodology for the validation of the synoptic scale climatology of GCMs. This is developed with regard to the Goddard Institute for Space Studies (GISS) GCM Model 2, with the specific objective of using the synoptic circulation form a doubles CO2 simulation to estimate regional climate change over North America, south of Hudson Bay. This progress report is specifically concerned with validating the synoptic climatology of the GISS GCM, and developing the transfer function to derive grid-point temperatures from the synoptic circulation. Principal Components Analysis is used to characterize the primary modes of the spatial and temporal variability in the observed and simulated climate, and the model validation is based on correlations between component loadings, and power spectral analysis of the component scores. The results show that the high resolution GISS model does an excellent job of simulating the synoptic circulation over the U.S., and that grid-point temperatures can be predicted with reasonable accuracy from the circulation patterns.

Crane, Robert G.↗
DOE OSTI2021

Validation of non-negative matrix factorization for rapid assessment of large sets of atomic pair distribution function data

The use of the non-negative matrix factorization (NMF) technique is validated for automatically extracting physically relevant components from atomic pair distribution function (PDF) data from time-series data such as in situ experiments. The use of two matrix-factorization techniques, principal component analysis and NMF, on PDF data is compared in the context of a chemical synthesis reaction taking place in a synchrotron beam, applying the approach to synthetic data where the correct composition is known and on measured PDFs from previously published experimental data. The NMF approach yields mathematical components that are very close to the PDFs of the chemical components of the system and a time evolution of the weights that closely follows the ground truth. Lastly, it is discussed how this would appear in a streaming context if the analysis were being carried out at the beamline as the experiment progressed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Beyond PCA: Additional Dimension Reduction Techniques to Consider in the Development of Climate Fingerprints

Abstract Dimension reduction techniques are an essential part of the climate analyst’s toolkit. Due to the enormous scale of climate data, dimension reduction methods are used to identify major patterns of variability within climate dynamics, to create compelling and informative visualizations, and to quantify major named modes such as El Niño–Southern Oscillation. Principal components analysis (PCA), also known as the method of empirical orthogonal functions (EOFs), is the most commonly used form of dimension reduction, characterized by a remarkable confluence of attractive mathematical, statistical, and computational properties. Despite its ubiquity, PCA suffers from several difficulties relevant to climate science: high computational burden with large datasets, decreased statistical accuracy in high dimensions, and difficulties comparing across multiple datasets. In this paper, we introduce several variants of PCA that are likely to be of use in climate sciences and address these problems. Specifically, we introduce non-negative , sparse , and tensor PCA and demonstrate how each approach provides superior pattern recognition in climate data. We also discuss approaches to comparing PCA-family results within and across datasets in a domain-relevant manner. We demonstrate these approaches through an analysis of several runs of the E3SM climate model from 1991 to 1995, focusing on the simulated response to the Mt. Pinatubo eruption; our findings are consistent with a recently identified stratospheric warming fingerprint associated with this type of stratospheric aerosol injection.

Weylandt, Michael↗
DOE OSTI2026

Image Distinguishability Analysis Testing Through Principal Components and Its Application to Hot Spot Scale Invariance

Hot spots are spatial regions of intense energy localization that govern initiation of secondary high explosives. Studies that characterize or compare simulated hot spots are frequently either qualitatively descriptive or resort to quantitative distribution functions that neglect stochastic variations and spatial correlations—effects that are also neglected in common comparison tests like the Kolmogorov–Smirnov test. To this end, we develop an image distinguishability analysis (IDA) test based on principal component (PC) analysis that makes pixel-by-pixel comparisons between small, for example, O(<10), image data sets. The IDA test makes comparisons through a generalized distance metric in the PC space and a test statistic that is derived to calculate mathematical equation-values. Here, we derive a statistical distribution and criticality criterion to determine whether images are distinguishable from established baselines. We apply the IDA test on images generated from molecular dynamics simulations of hot spots from pore collapse in TATB to assess scale invariance in the complex patterns of hot spots that form in a representative high explosive crystal. The IDA test shows that TATB hot spot spatial temperature fields and their derived temperature histograms exhibit scale-invariant features over specific intervals of shock orientation, strength, and initial pore diameter. However, the IDA test also shows that qualitatively different conclusions regarding invariance can be reached depending on whether the hot spot is treated as a spatially correlated field as opposed to a distribution function that lacks spatial information.

organic↗
NASA NTRS1981

Measurements of He D3 profiles in solar plages

Measurements of the profiles of the He D3 lines in solar plages are presented as a probe of the plage atmosphere. Observations were made at selected positions in quiescent plages during the period October 1978-April 1979 by the 25-cm coronagraph/3-m coude spectrograph at the Mees Solar Observatory on Mount Haleakala. Individual D3 plage profiles are fit well with a two-component, differentially moving atmosphere with constant source function and total Doppler width. The principal absorption is attributed to nearly stationary material, with a kinetic temperature less than 17,200 K, and velocity distribution is found to represent a net downflow in the plage atmosphere extending to around 12 km/sec. The present results for individual plage profiles are also noted to be consistent with those of Namba (1963). Time series of D3 profiles at fixed plage positions and the velocity spectra for nearby photospheric lines are observed to show general enhancements at periods of around 5 min, with redshift leading variations in line width by about 49 deg. Interpretations of the width variation as temperature variations are found not to be consistent with the properties of acoustic wave modes in a stratified atmosphere, presumably indicating magnetic field effects.

Landman, D. A.↗
DOE OSTI2020

Non-Gaussianity in the weak lensing correlation function likelihood – implications for cosmological parameter biases

ABSTRACT We study the significance of non-Gaussianity in the likelihood of weak lensing shear two-point correlation functions, detecting significantly non-zero skewness and kurtosis in 1D marginal distributions of shear two-point correlation functions in simulated weak lensing data. We examine the implications in the context of future surveys, in particular LSST, with derivations of how the non-Gaussianity scales with survey area. We show that there is no significant bias in 1D posteriors of Ωm and σ8 due to the non-Gaussian likelihood distributions of shear correlations functions using the mock data (100 deg2). We also present a systematic approach to constructing approximate multivariate likelihoods with 1D parametric functions by assuming independence or more flexible non-parametric multivariate methods after decorrelating the data points using principal component analysis (PCA). While the use of PCA does not modify the non-Gaussianity of the multivariate likelihood, we find empirically that the 1D marginal sampling distributions of the PCA components exhibit less skewness and kurtosis than the original shear correlation functions. Modelling the likelihood with marginal parametric functions based on the assumption of independence between PCA components thus gives a lower limit for the biases. We further demonstrate that the difference in cosmological parameter constraints between the multivariate Gaussian likelihood model and more complex non-Gaussian likelihood models would be even smaller for an LSST-like survey. In addition, the PCA approach automatically serves as a data compression method, enabling the retention of the majority of the cosmological information while reducing the dimensionality of the data vector by a factor of ∼5.

79 ASTRONOMY AND ASTROPHYSICS↗