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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Differential credibility assessment for statistical downscaling

Climate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. Here, the approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact relevant CLIMDEX temperature indices that are assigned credibility based on (1) the reproduction of relevant large-scale predictors by the GCMs (i.e. fraction of regression beta-weights derived from predictors that are well-reproduced) and (2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations, between the two GCM and is generally higher for minimum temperature than maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision makers regarding projection confidence, and/or extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Four decades of implicit Monte Carlo

In 1971, Fleck and Cummings derived a system of equations to enable robust Monte Carlo simulations of time-dependent, thermal radiative transfer problems. Denoted the “Implicit Monte Carlo” (IMC) equations, their solution remains the de facto standard of high-fidelity radiative transfer simulations. Over the course of 44 years, their numerical properties have become better understood, and accuracy enhancements, novel acceleration methods, and variance reduction techniques have been suggested. In this review, we rederive the IMC equations—explicitly highlighting assumptions as they are made—and outfit the equations with a Monte Carlo interpretation. We put the IMC equations in context with other approximate forms of the radiative transfer equations and present a new demonstration of their equivalence to another well-used linearization solved with deterministic transport methods for frequency-independent problems. We discuss physical and numerical limitations of the IMC equations for asymptotically small time steps, stability characteristics and the potential of maximum principle violations for large time steps, and solution behaviors in an asymptotically thick diffusive limit. We provide a new stability analysis for opacities with general monomial dependence on temperature. Here, we consider spatial accuracy limitations of the IMC equations and discussion acceleration and variance reduction techniques.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Goal-oriented sensitivity analysis for lattice kinetic Monte Carlo simulations

In this paper we propose a new class of coupling methods for the sensitivity analysis of high dimensional stochastic systems and in particular for lattice Kinetic Monte Carlo (KMC). Sensitivity analysis for stochastic systems is typically based on approximating continuous derivatives with respect to model parameters by the mean value of samples from a finite difference scheme. Instead of using independent samples the proposed algorithm reduces the variance of the estimator by developing a strongly correlated-“coupled”- stochastic process for both the perturbed and unperturbed stochastic processes, defined in a common state space. The novelty of our construction is that the new coupled process depends on the targeted observables, e.g., coverage, Hamiltonian, spatial correlations, surface roughness, etc., hence we refer to the proposed method as goal-oriented sensitivity analysis. In particular, the rates of the coupled Continuous Time Markov Chain are obtained as solutions to a goal-oriented optimization problem, depending on the observable of interest, by considering the minimization functional of the corresponding variance. We show that this functional can be used as a diagnostic tool for the design and evaluation of different classes of couplings. Furthermore, the resulting KMC sensitivity algorithm has an easy implementation that is based on the Bortz–Kalos–Lebowitz algorithm's philosophy, where events are divided in classes depending on level sets of the observable of interest. Finally, we demonstrate in several examples including adsorption, desorption, and diffusion Kinetic Monte Carlo that for the same confidence interval and observable, the proposed goal-oriented algorithm can be two orders of magnitude faster than existing coupling algorithms for spatial KMC such as the Common Random Number approach. We also provide a complete implementation of the proposed sensitivity analysis algorithms, including various spatial KMC examples, in a supplementary MATLAB source code.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

A fully Bayesian method for jointly fitting instrumental calibration and X-ray spectral models

Owing to a lack of robust principled methods, systematic instrumental uncertainties have generally been ignored in astrophysical data analysis despite wide recognition of the importance of including them. Ignoring calibration uncertainty can cause bias in the estimation of source model parameters and can lead to underestimation of the variance of these estimates. We previously introduced a pragmatic Bayesian method to address this problem. The method is 'pragmatic' in that it introduced an ad hoc technique that simplified computation by neglecting the potential information in the data for narrowing the uncertainty for the calibration product. Following that work, we use a principal component analysis to efficiently represent the uncertainty of the effective area of an X-ray (or γ-ray) telescope. Here, however, we leverage this representation to enable a principled, fully Bayesian method that coherently accounts for the calibration uncertainty in high-energy spectral analysis. In this setting, the method is compared with standard analysis techniques and the pragmatic Bayesian method. The advantage of the fully Bayesian method is that it allows the data to provide information not only for estimation of the source parameters but also for the calibration product—here the effective area, conditional on the adopted spectral model. In this way, it can yield more accurate and efficient estimates of the source parameters along with valid estimates of their uncertainty. Provided that the source spectrum can be accurately described by a parameterized model, this method allows rigorous inference about the effective area by quantifying which possible curves are most consistent with the data.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1976

Analytical and experimental design and analysis of an optimal processor for image registration

The author has identified the following significant results. A quantitative measure of the registration processor accuracy in terms of the variance of the registration error was derived. With the appropriate assumptions, the variance was shown to be inversely proportional to the square of the effective bandwidth times the signal to noise ratio. The final expressions were presented to emphasize both the form and simplicity of their representation. In the situation where relative spatial distortions exist between images to be registered, expressions were derived for estimating the loss in output signal to noise ratio due to these spatial distortions. These results are in terms of a reduction factor.

Mcgillem, C. D.↗
NASA NTRS1996

Analysis of Darwin Rainfall Data: Implications on Sampling Strategy

Rainfall data collected by radar in the vicinity of Darwin, Australia, have been analyzed in terms of their mean, variance, autocorrelation of area-averaged rain rate, and diurnal variation. It is found that, when compared with the well-studied GATE (Global Atmospheric Research Program Atlantic Tropical Experiment) data, Darwin rainfall has larger coefficient of variation (CV), faster reduction of CV with increasing area size, weaker temporal correlation, and a strong diurnal cycle and intermittence. The coefficient of variation for Darwin rainfall has larger magnitude and exhibits larger spatial variability over the sea portion than over the land portion within the area of radar coverage. Stationary, and nonstationary models have been used to study the sampling errors associated with space-based rainfall measurement. The nonstationary model shows that the sampling error is sensitive to the starting sampling time for some sampling frequencies, due to the diurnal cycle of rain, but not for others. Sampling experiments using data also show such sensitivity. When the errors are averaged over starting time, the results of the experiments and the stationary and nonstationary models match each other very closely. In the small areas for which data are available for I>oth Darwin and GATE, the sampling error is expected to be larger for Darwin due to its larger CV.

Rafael, Qihang Li↗
NASA NTRS1980

Delta V errors due to attitude correction maneuvers of a spinning spacecraft

A statistical error analysis has been performed to evaluate the change in velocity of a spin stabilized spacecraft due to thruster induced attitude correction maneuvers. Analytical expressions were developed to determine the mean and variance of the velocity in terms of the nominal values and uncertainties of key spacecraft parameters. These parameters include thrust, thruster timing, thruster alignment, plume impingement force and estimated spacecraft mass. Typical values of these parameters and their uncertainties for NASA's Galileo spacecraft were used to evaluate these expressions. The results of the analysis allow the effect of attitude correction maneuvers to be evaluated with respect to the functional requirements on nongravitational acceleration. Other results include the identification of major sources of delta V error and studies of the sensitivity of delta V to variations in the error sources.

Zipursky, H. J.↗
DOE OSTI2022

Genetic correlations between switchgrass performance in sward conditions and surrogate measures

Switchgrass (Panicum virgatum L.) breeders use spaced-seedling plots for evaluations of biomass yield, but prior studies are ambiguous about the relationship between performance in spaced plots and commercial seeded sward performance. This study used a split-plot incomplete block design to evaluate 52 half-sib switchgrass families across two years to determine the correlations among four different plot types (including seeded swards) and four individual plant traits. Plots (1.62 m 2 ) that contained 3, 6, and 18 plants had mean yields of 5.92, 8.04, and 11.06 Mg ha –1 , respectively, while seeded-sward plots had a mean of 5.37 Mg ha –1 . In a mixed-model analysis including family-, year-, and plot-type interactions with a spatial-spline correction, the largest proportion of family variance was accounted for by the main family effect (48%), while the plot type-by-family effect was only 17% of family variance. These results indicate that spaced-plant plots are reasonable surrogates for biomass performance in sward conditions. Yield reliability was 0.50 across all plot types with a mean of 0.46 for individual plot types. Surrogates for sward yield varied due to genetic background of the families. Yield of families derived from the Liberty cultivar (n = 32) had genetic correlations with winter survivorship scores (0.66). Diverse lowland ecotype families (n = 20) had genetic correlations with plant height and heading date. This study highlighted a major difference in performance among the plot types, specifically that seeded swards have depressed yield due to high plant density. This should encourage further research for the mechanism behind this difference in performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

A note on the Bayesian analysis of experiments with correlated multiple responses using a matrix-logarithmic covariance model

In the literature, analysis of multiple responses from experiments with replicates has modeled the covariance matrix directly as linear models of the transformed variances and correlations, i.e., covariance modeling. This article considers models based on the matrix- logarithm of the covariance matrix. This so-called log-covariance modeling is illustrated with data from actual experiments and compared with the traditional covariance modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluating Retention Index Score Assumptions to Refine GC–MS Metabolite Identification

As metabolomics grows into a high-throughput and high demand research field, current metrics for the identification of small molecules in gas chromatography–mass spectrometry (GC–MS) still require manual verification. Though steps have been taken to improve scoring metrics by combining spectral similarity (SS) and retention index (RI), the problem persists. A large body of literature has analyzed and refined SS scores, but few studies have explicitly studied improvements to RI scores. Here, in this study, we examined whether uninvestigated assumptions of the RI score are valid and propose ways to improve them. Query RIs were matched to library RI with a generous window of ±35 to avoid unintentional removal of valid compound identifications. Each match was manually verified as a true positive (TP), true negative, or unknown. Metabolites with at least 30 TP identifications were included in downstream analyses, resulting in a total of 87 metabolites from samples of varying complexity and type (e.g., amino acid mixtures, human urine, fungal species, and so on.). Our results showed that the RI score assumptions of normality, consistent variance across metabolites, and a mean error centered at 0 are often violated. We demonstrated through a cross-validation analysis that modifying these underlying assumptions according to empirical metabolite-specific distributions improved the TP and negative rankings. Further, we statistically determined the minimum number of samples required to estimate distributional parameters for scoring metrics. Overall, this work proposes a robust statistical pipeline to reduce the time bottleneck of metabolite identification by improving RI scores and thus minimize the effort to complete manual verification.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2012

BULK FLOW OF HALOS IN {Lambda}CDM SIMULATION

Analysis of the Pangu N-body simulation validates that the bulk flow of halos follows a Maxwellian distribution with variance that is consistent with the prediction of the linear theory of structure formation. We propose that the consistency between the observed bulk velocity and theories should be examined at the effective scale of the radius of a spherical top-hat window function yielding the same smoothed velocity variance in linear theory as the sample window function does. We compared some recently estimated bulk flows from observational samples with the prediction of the {Lambda}CDM model we used; some results deviate from expectation at a level of {approx}3{sigma}, but the discrepancy is not as severe as previously claimed. We show that bulk flow is only weakly correlated with the dipole of the internal mass distribution, that the alignment angle between the mass dipole and the bulk flow has a broad distribution peaked at {approx}30 Degree-Sign -50 Degree-Sign , and also that the bulk flow shows little dependence on the mass of the halos used in the estimation. In a simulation of box size 1 h {sup -1} Gpc, for a cell of radius 100 h {sup -1} Mpc the maximal bulk velocity is >500 km s{sup -1}; dipoles of the environmental mass outside the cell are not tightly aligned with the bulk flow, but are rather located randomly around it with separation angles {approx}20 Degree-Sign -40 Degree-Sign . In the fastest cell there is a slightly smaller number of low-mass halos; however, halos inside are clustered more strongly at scales {approx}> 20 h {sup -1} Mpc, which might be a significant feature since the correlation between bulk flow and halo clustering actually increases in significance beyond such scales.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

FRONTIER FIELDS: HIGH-REDSHIFT PREDICTIONS AND EARLY RESULTS

The Frontier Fields program is obtaining deep Hubble and Spitzer Space Telescope images of new ''blank'' fields and nearby fields gravitationally lensed by massive galaxy clusters. The Hubble images of the lensed fields are revealing nJy sources (AB mag > 31), the faintest galaxies yet observed. The full program will transform our understanding of galaxy evolution in the first 600 million years (z > 9). Previous programs have yielded a dozen or so z > 9 candidates, including perhaps fewer than expected in the Ultra Deep Field and more than expected in shallower Hubble images. In this paper, we present high-redshift (z > 6) number count predictions for the Frontier Fields and candidates in three of the first Hubble images. We show the full Frontier Fields program may yield up to ∼70 z > 9 candidates (∼6 per field). We base this estimate on an extrapolation of luminosity functions observed between 4 < z < 8 and gravitational lensing models submitted by the community. However, in the first two deep infrared Hubble images obtained to date, we find z ∼ 8 candidates but no strong candidates at z > 9. We defer quantitative analysis of the z > 9 deficit (including detection completeness estimates) to future work including additional data. At these redshifts, cosmic variance (field-to-field variation) is expected to be significant (greater than ±50%) and include clustering of early galaxies formed in overdensities. The full Frontier Fields program will significantly mitigate this uncertainty by observing six independent sightlines each with a lensing cluster and nearby blank field.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

PHOTOMETRIC REDSHIFT PROBABILITY DISTRIBUTIONS FOR GALAXIES IN THE SDSS DR8

We present redshift probability distributions for galaxies in the Sloan Digital Sky Survey (SDSS) Data Release 8 imaging data. We used the nearest-neighbor weighting algorithm to derive the ensemble redshift distribution N(z), and individual redshift probability distributions P(z) for galaxies with r < 21.8 and u < 29.0. As part of this technique, we calculated weights for a set of training galaxies with known redshifts such that their density distribution in five-dimensional color-magnitude space was proportional to that of the photometry-only sample, producing a nearly fair sample in that space. We estimated the ensemble N(z) of the photometric sample by constructing a weighted histogram of the training-set redshifts. We derived P(z)'s for individual objects by using training-set objects from the local color-magnitude space around each photometric object. Using the P(z) for each galaxy can reduce the statistical error in measurements that depend on the redshifts of individual galaxies. The spectroscopic training sample is substantially larger than that used for the DR7 release. The newly added PRIMUS catalog is now the most important training set used in this analysis by a wide margin. We expect the primary sources of error in the N(z) reconstruction to be sample variance and spectroscopic failures: The training sets are drawn from relatively small volumes of space, and some samples have large incompleteness. Using simulations we estimated the uncertainty in N(z) due to sample variance at a given redshift to be {approx}10%-15%. The uncertainty on calculations incorporating N(z) or P(z) depends on how they are used; we discuss the case of weak lensing measurements. The P(z) catalog is publicly available from the SDSS Web site.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2011

The multi-stream flows and the dynamics of the cosmic web

A new numerical technique to identify the cosmic web is proposed. It is based on locating multi-stream flows, i.e. the places where the velocity field is multi-valued. The method is local in Eulerian space, simple and computationally efficient. This technique uses the velocities of particles and thus takes into account the dynamical information. This is in contrast with the majority of standard methods that use the coordinates of particles only. Two quantities are computed in every mesh cell: the mean and variance of the velocity field. Ideally in the cells where the velocity is single-valued the variance must be equal to zero exactly, therefore the cells with non-zero variance are identified as multi-stream flows. The technique has been tested in the Zel'dovich approximation and in the N-body simulation of the ΛCDM model. The effect of numerical noise is discussed. The web identified by the new method has been compared with the web identified by the standard technique using only the particle coordinates. The comparison has shown overall similarity of two webs as expected, however they by no means are identical. For example, the isocontours of the corresponding fields have significantly different shapes and some density peaks of similar heights exhibit significant differences in the velocity variance and vice versa. This suggests that the density and velocity variance have a significant degree of independence. The shape of the two-dimensional pdf of density and velocity variance confirms this proposition. Thus, we conclude that the dynamical information probed by this technique introduces an additional dimension into analysis of the web.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Modeled production, oxidation, and transport processes of wetland methane emissions in temperate, boreal, and Arctic regions

Abstract Wetlands are the largest natural source of methane (CH 4 ) to the atmosphere. The eddy covariance method provides robust measurements of net ecosystem exchange of CH 4 , but interpreting its spatiotemporal variations is challenging due to the co‐occurrence of CH 4 production, oxidation, and transport dynamics. Here, we estimate these three processes using a data‐model fusion approach across 25 wetlands in temperate, boreal, and Arctic regions. Our data‐constrained model—iPEACE—reasonably reproduced CH 4 emissions at 19 of the 25 sites with normalized root mean square error of 0.59, correlation coefficient of 0.82, and normalized standard deviation of 0.87. Among the three processes, CH 4 production appeared to be the most important process, followed by oxidation in explaining inter‐site variations in CH 4 emissions. Based on a sensitivity analysis, CH 4 emissions were generally more sensitive to decreased water table than to increased gross primary productivity or soil temperature. For periods with leaf area index (LAI) of ≥20% of its annual peak, plant‐mediated transport appeared to be the major pathway for CH 4 transport. Contributions from ebullition and diffusion were relatively high during low LAI (<20%) periods. The lag time between CH 4 production and CH 4 emissions tended to be short in fen sites (3 ± 2 days) and long in bog sites (13 ± 10 days). Based on a principal component analysis, we found that parameters for CH 4 production, plant‐mediated transport, and diffusion through water explained 77% of the variance in the parameters across the 19 sites, highlighting the importance of these parameters for predicting wetland CH 4 emissions across biomes. These processes and associated parameters for CH 4 emissions among and within the wetlands provide useful insights for interpreting observed net CH 4 fluxes, estimating sensitivities to biophysical variables, and modeling global CH 4 fluxes.

Ueyama, Masahito↗
NASA NTRS1980

Microstructure of the interplanetary magnetic field near 4 and 5 AU

Interplanetary magnetic field fluctuations near 4 and 5 AU are investigated on the basis of Pioneer 10 and 11 vector magnetic field measurements in order to identify extant wave modes and propagation directions as functions of frequency in the range 0.00004 to 0.009 Hz. Analysis of the approximately 500 spectral matrices as functions of frequency obtained from each spacecraft indicates the distributions of maximum variance directions to be peaked along the normal to the plane of the minimum variance direction and the mean magnetic field, as would be expected for a planar, small-amplitude Alfven wave with a minimum variance direction close to the wave vector. It is thus inferred that single small-amplitude Alfven waves are occasionally present in the temporal and frequency bands analyzed, however waves with wave vectors not well approximated by the minimum variance direction, Alfven wave ensembles, finite-amplitude waves and nonplanar fluctuations may also be present. Observations are also noted to suggest the predominance of the Alfven mode over the fast and slow modes, however do not support theoretical models of outward propagating plane Alfven waves of solar origin.

Parker, G. D.↗
NASA NTRS1984

Infrared parameterization and simple climate models

The geographical distribution of the annual mean and the annual cycle in surface temperature and satellite-observed IR is examined, and the spherical harmonic representation of the data fields is exploited to demonstrate how variances and covariances are dominated by the largest space and time scales. The geographical distribution of the annual cycle in the T and IR fields is explored; the strong imprint of the continents in both is clearly evident. The influence of the cloudiness of seasonal precipitation regimes on the IR annual cycle is also quite striking, especially over the subtropics. Analysis of the data shows that the simple form IR = A+BT (with A = 204 W/sq m and B = 1.93 W/sq m/K) explains 90 percent of the area-weighted variance in the annual mean and annual cycle of the zonally averaged IR field.

Short, D. A.↗