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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Cylindrical Kernel Density Estimators For Monte Carlo Neutron Transport Reactor Physics Problems

Kernel Density Estimators (KDEs) have been investigated as an alternative to histogram tallies in Monte Carlo radiation transport simulations for capturing spatially-resolved quantities such as scalar flux and reaction rate densities. KDEs are non-parametric estimators that show potential for producing smooth estimates of quantities of interest with reduced variance compared to traditional histogram tallies. Furthermore, KDEs obtain estimates of the underlying distribution at user-defined points without requiring an underlying mesh, thus making KDEs an attractive potential alternative to histogram tallies. Recent work has introduced the Mean Free Path based KDE (MFP KDE) in order to capture distributions with large curvature at material interfaces, e.g. absorption reaction rates in UO{sub 2} fuel in light water reactors. However, the multivariate MFP KDE produces inaccuracies in the form of localized spikes in problems with material cross sections that contain large resonances. Since the MFP KDE works well in one dimension, it is hypothesized that a different MFP KDE formulation where the normalization coefficient only contains one factor of the cross section would be able to capture these distributions with large curvature without producing spikes in the estimate. This can be accomplished by using a cylindrical KDE whose 2-D support area is wedge-shaped and applying the MFP KDE to the radial component of the cylindrical KDE. These cylindrical KDEs use 2-D (r,θ) cylindrical coordinates with no dependence in axial dimension z. This paper derives the cylindrical KDE and the cylindrical MFP KDE and tests them on a reactor physics pin-cell benchmark problem, producing accurate estimates of flux and reaction rate densities with differences less than 0.5% when compared to a reference histogram. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1994

Parameter dimension of turbulence-induced phase errors and its effects on estimation in phase diversity

The method of phase diversity has been used in the context of incoherent imaging to estimate jointly an object that is being imaged and phase aberrations induced by atmospheric turbulence. The method requires a parametric model for the phase-aberration function. Typically, the parameters are coefficients to a finite set of basis functions. Care must be taken in selecting a parameterization that properly balances accuracy in the representation of the phase-aberration function with stability in the estimates. It is well known that over parameterization can result in unstable estimates. Thus a certain amount of model mismatch is often desirable. We derive expressions that quantify the bias and variance in object and aberration estimates as a function of parameter dimension.

Thelen, Brian J.↗
DOE OSTI2018

Convergence and Efficiency of Adaptive Importance Sampling Techniques with Partial Biasing

We propose a new Monte Carlo method to efficiently sample a multimodal distribution (known up to a normalization constant). We consider a generalization of the discrete-time Self Healing Umbrella Sampling method, which can also be seen as a generalization of well-tempered metadynamics. The dynamics is based on an adaptive importance technique. The importance function relies on the weights (namely the relative probabilities) of disjoint sets which form a partition of the space. These weights are unknown but are learnt on the fly yielding an adaptive algorithm. In the context of computational statistical physics, the logarithm of these weights is, up to an additive constant, the free-energy, and the discrete valued function defining the partition is called the collective variable. The algorithm falls into the general class of Wang–Landau type methods, and is a generalization of the original Self Healing Umbrella Sampling method in two ways: (i) the updating strategy leads to a larger penalization strength of already visited sets in order to escape more quickly from metastable states, and (ii) the target distribution is biased using only a fraction of the free-energy, in order to increase the effective sample size and reduce the variance of importance sampling estimators. We prove the convergence of the algorithm and analyze numerically its efficiency on a toy example.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints

The primary goal of this paper is to provide an efficient solution algorithm based on the augmented Lagrangian framework for optimization problems with a stochastic objective function and deterministic constraints. Our main contribution is combining the augmented Lagrangian framework with adaptive sampling, resulting in an efficient optimization methodology validated with practical examples. To achieve the presented efficiency, here we consider inexact solutions for the augmented Lagrangian subproblems, and through an adaptive sampling mechanism, we control the variance in the gradient estimates. Furthermore, we analyze the theoretical performance of the proposed scheme by showing equivalence to a gradient descent algorithm on a Moreau envelope function, and we prove sublinear convergence for convex objectives and linear convergence for strongly convex objectives with affine equality constraints. The worst-case sample complexity of the resulting algorithm, for an arbitrary choice of penalty parameter in the augmented Lagrangian function, is $\mathscr{O}$(ϵ -3-δ ) , where ϵ > 0 is the expected error of the solution and δ > 0 is a user-defined parameter. If the penalty parameter is chosen to be $\mathscr{O}$(ϵ -1 ), we demonstrate that the result can be improved to $\mathscr{O}$(ϵ -2 ) , which is competitive with the other methods employed in the literature. Moreover, if the objective function is strongly convex with affine equality constraints, we obtain $\mathscr{O}$(ϵ -1 log(1/ϵ)) complexity. Finally, we empirically verify the performance of our adaptive sampling augmented Lagrangian framework in machine learning optimization and engineering design problems, including topology optimization of a heat sink with environmental uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Multiscale analysis of the CMB temperature derivatives

We study the Planck CMB temperature at different scales through its derivatives up to second order, which allows one to characterize the local shape and isotropy of the field. The problem of having an incomplete sky in the calculation and statistical characterization of the derivatives is addressed in the paper. The analysis confirms the existence of a low variance in the CMB at large scales, which is also noticeable in the derivatives. Moreover, deviations from the standard model in the gradient, curvature and the eccentricity tensor are studied in terms of extreme values on the data. As it is expected, the Cold Spot is detected as one of the most prominent peaks in terms of curvature, but additionally, when the information of the temperature and its Laplacian are combined, another feature with similar probability at the scale of 10{sup o} is also observed. However, the p -value of these two deviations increase above the 6% when they are referred to the variance calculated from the theoretical fiducial model, indicating that these deviations can be associated to the low variance anomaly. Finally, an estimator of the directional anisotropy for spinorial quantities is introduced, which is applied to the spinors derived from the field derivatives. An anisotropic direction whose probability is <1% is detected in the eccentricity tensor.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

Influence of phase connectivity on the relationship among capillary pressure, fluid saturation, and interfacial area in two-fluid-phase porous medium systems

Here, multiphase flow in porous medium systems is typically modeled using continuum mechanical representations at the macroscale in terms of averaged quantities. These models require closure relations to produce solvable forms. One of these required closure relations is an expression relating fluid pressures, fluid saturations, and, in some cases, the interfacial area between the fluid phases, and the Euler characteristic. An unresolved question is whether the inclusion of these additional morphological and topological measures can lead to a non-hysteretic closure relation compared to the hysteretic forms that are used in traditional models, which typically do not include interfacial areas, or the Euler characteristic. We develop a lattice-Boltzmann (LB) simulation approach to investigate the equilibrium states of a two-fluid-phase porous medium system, which include disconnected now- wetting phase features. The proposed approach is applied to a synthetic medium consisting of 1,964 spheres arranged in a random, non-overlapping, close-packed manner, yielding a total of 42,908 different equilibrium points. This information is evaluated using a generalized additive modeling approach to determine if a unique function from this family exists, which can explain the data. The variance of various model estimates is computed, and we conclude that, except for the limiting behavior close to a single fluid regime, capillary pressure can be expressed as a deterministic and non-hysteretic function of fluid saturation, interfacial area between the fluid phases, and the Euler characteristic. This work is unique in the methods employed, the size of the data set, the resolution in space and time, the true equilibrium nature of the data, the parameterizations investigated, and the broad set of functions examined. The conclusion of essentially non-hysteretic behavior provides support for an evolving class of two-fluid-phase flow in porous medium systems models.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Stochastic Richardson Extrapolation Based Numerical Error Estimation for Kinetic Plasma Simulations

We present a numerical error estimation technique specifically formulated to deal with stochastic code output with multiple discretization parameters. This method is based on multiple fits to an error model with arbitrary convergence rates and cross-coupling terms, performed using nonlinear optimization. The fitting approach varies by the type of residual norm which influences the importance of outliers, and weights which influences the relative importance of data points in the coarse and refined regions of discretization parameter space. To account for the influence of stochastic noise, these fits are performed on multiple bootstrap values based on the underlying response data set. Using an automated discretization domain selection scheme, the fits are performed on a series of reduced sets of discretization levels in order to find an optimal fully-converged result estimate in the minimum variance sense; this automated approach enables straightforward analysis of multiple quantities of interest and/or time and spatially-dependent response data. The overall numerical error analysis method is useful for verification and validation problems for stochastic simulation methods and forms a key component in the overall uncertainty quantification process. The method was demonstrated for steady and unsteady electron diode problems simulated using a particle-in-cell kinetic plasma code, demonstrating excellent results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1979

Polar motion results from GEOS 3 laser ranging

The observability of polar motion from laser range data has been investigated, and the contributions from the dynamical and kinematical effects have been evaluated. Using 2-day arcs with GEOS 3 laser data, simultaneous solutions for pole position components and orbit elements have been obtained for a 2-week interval spanning August 27 to September 10, 1975, using three NASA Goddard Space Flight Center stations located at Washington, D.C., Bermuda, and Grand Turk. The results for the y-component of pole position from this limited data set differenced with the BIH linearly interpolated values yield a mean of 39 cm and a standard deviation of 1.07 m. Consideration of the variance associated with each estimate yields a mean of 20 cm and a standard deviation of 81 cm. The results for the x-component of pole position indicate that the mean value is in fair agreement with the BIH; however, the x-coordinate determination is weaker than the y-coordinate determination due to the distribution of laser sites (all three are between 77 deg W and 65 deg W) which results in greater sensitivity to the data distribution. In addition, the sensitivity of these results to various model parameters is discussed.

Schutz, B. E.↗
NASA NTRS1981

Fiscal year 1980-81 implementation plan in support of technical development and integration of sampling and aggregation procedures

The specific objectives of the FY 1980-81 tasks are: (1) further refinements to the weighted aggregation procedure; (2) improved approaches for estimating within-stratum variance; (3) more intensive investigation of alternative sampling strategies such as full-frame sampling strategy, and (4) further developments in regard to a simulated approach for assessing the performance of the overall designed sampling and aggregation system.

Source record↗
NASA NTRS1981

FOCIS: A forest classification and inventory system using LANDSAT and digital terrain data

Accurate, cost-effective stratification of forest vegetation and timber inventory is the primary goal of a Forest Classification and Inventory System (FOCIS). Conventional timber stratification using photointerpretation can be time-consuming, costly, and inconsistent from analyst to analyst. FOCIS was designed to overcome these problems by using machine processing techniques to extract and process tonal, textural, and terrain information from registered LANDSAT multispectral and digital terrain data. Comparison of samples from timber strata identified by conventional procedures showed that both have about the same potential to reduce the variance of timber volume estimates over simple random sampling.

Strahler, A. H.↗
NASA NTRS1986

Digital Filter Separates Signal From Noise

Variance of signal-estimation error minimized. Mathematical technique extracts best estimates of signal component from periodic digital samples of signal plus noise. Technique combines Kalman- and smoothingfilter algorithms to minimize mean-square estimation error based on past, present, and predicted samples of signal plus noise. Technique useful in image analysis and other applications involving processing of noisy signals.

Lear, W. M.↗
NASA NTRS1989

Estimation and tracking for deep-space optical communications

The importance of pointing and tracking is demonstrated with current deep-space optical communications system concepts. Maximum-likelihood (ML), minimum square counting-error (MSCE), and maximum product (MP) estimation algorithms (or decision rules) are derived to estimate the location of the receiving station to subpixel resolution. Comparisons of the above algorithms are made, via Monte Carlo computer simulation, in terms of estimator's bias and variance. Optical communication link analyses are made for a typical earth-Mars scenario, to gain engineering insights. It is observed that both the ML rule and the MSCE rule perform better than the MP rule.

Win, Moe Zaw↗
NASA NTRS1992

The relation of local measures of Hubble's constant to its global value

The distributions of fractional deviations of local values form global H0 that observers with perfect distance data would find if they surveyed specified volumes of the universe are examined here using new very large scale calculations of cold dark matter (CDM) and primordial isocurvature baryonic (PIB) scenarios for the origin of structure. It is found that the expected deviations due to large-scale motions are larger than quoted observational errors unless very large volumes are surveyed. Even perfect sampling and distances of all galaxies within a sphere extending out to the distances of the Virgo and Coma clusters would leave 45 percent and 3 percent rms uncertainties, respectively, in the global value of H0 in the CDM model. It is shown that the local versus global error in an H0 determination can be roughly estimated by the angular variance seen over the sky in the expansion rate, and that a very rough correction from the local to the global H0 value can be derived.

Turner, Edwin L.↗
NASA NTRS1993

Space transportation vehicle design evaluation using saturated designs

An important objective in the preliminary design and evaluation of space transportation vehicles is to find the best values of design variables that optimize the performance characteristic (e.g. dry weight). For a given configuration, the vehicle performance can be determined by the use of complex sizing and performance evaluation computer programs. These complex computer programs utilize iterative algorithms and they are generally too expensive and/or difficult to use directly in multidisciplinary design optimization. An alternative is to use response surface methodology (RSM) and obtain quadratic polynomial approximations to the functional relationships between performance characteristics and design variables. In RSM, these approximation models are then used to determine optimum design parameter values and for rapid sensitivity studies. Constructing a second-order model requires that 'n' design parameters be studied at least at 3 levels (values) so that the coefficients in the model can be estimated. There, 3(n) factorial experiments (point designs or observations) may be necessary. For small values of 'n' such as two or three, this design works well. However, when a large number of design parameters are under study, the number of design points required for a full-factorial design may become excessive. Fortunately, these quadratic polynomial approximations can be obtained by selecting an efficient design matrix using central composite designs (CCD) from design of experiments theory. Each unique point design from the CCD matrix is then conducted using computerized analysis tools (e.g. POST, CONSIZ, etc.). In the next step, least squares regression analysis is used to calculate the quadratic polynomial coefficients from the data. However, in some multidisciplinary applications involving a large number of design variables and several disciplines, the computerized performance synthesis programs may get too time consuming and expensive to run even with the use of efficient central composite designs. In such cases, it may be preferable to keep the number of design points to an absolute minimum and trade some model accuracy with cost. For this purpose, another class of experimental designs, called saturated D-optimal designs may be utilized for generating a matrix of vehicle designs. A design is called saturated when the number of design points is exactly equal to the number of terms in the model to be fitted. As a result, saturated designs require the absolute minimum number of design points ((n+1)(n+2)/s) to estimate the quadratic polynomial model coefficients. Saturated designs can be generated using the D-optimality criterion. A good saturated design should give rise to least squares estimates with minimum generalized variance.

Unal, Resit↗
NASA NTRS1994

Microrover research at JPL

Planetary exploration with microrovers can be extended beyond the baseline short-range capability proposed for the Mars Environmental Survey (MESUR) microrover mission. The useful range of the microrover can be increased by using local landmarks to accurately reach desired science sites with a minimal number of Earth commands. Furthermore, onboard processing can give indications of excessive sinkage or slippage, which comprise hazards which may not be detectable from imaging, as well as to improve dead reckoning performance. Lastly, it is important to estimate the mean and variance of mission parameters such as time-to-reach-goal, energy-to-reach-goal, and likelihood-of-finding landmark. The author of the paper discusses research focused on accomplishing these objectives.

Wilcox, Brian↗
NASA NTRS1994

Correlations of galaxies at z = 0.3

The angular correlation functions for galaxies with magnitudes 20 less than b(sub J) less than 21.5 and 21 less than b(sub J) less than 22.5 are measured from photographic images of nine distinct fields. New expressions for the variance of correlation function estimates are used, which show that the nine fields are consistent with a single angular correlation function w(theta) and with previous estimates of its amplitude and slope -- though the errors on previously published w(theta) values are probably underestimated. Assuming a spatial correlation function of the form xi(r,z) = (r/r(sub 0))(exp -gamma)(1 + z)(exp -3-epsilon), and using the measured N(z) for these magnitude ranges, we derive xi(r = 250 kpc/h, z = 0.18) = 48 +/- 10, and xi(r = 250 kpc/h, z = 0.27) = 55(+11; -9) for galaxies in these samples, largely independent of gamma and epsilon (1 sigma errors). This is approximately 2 times lower than the small-scale clustering seen in nearby galaxies at b(sub J) less than 18 (after allowing for modest growth in clustering since z = 0.27), yet very similar to the clustering of the nearby IRAS-selected galaxy population. The correlations of the reddest third of the b(sub J) approximately 22 galaxies, however, are larger and consistent with the b(sub J) less than 18 clustering, not the IRAS clustering, for a clustering growth rate of epsilon equal to or greater than -1. Thus to b(sub J) less than 22.5, most of the apparent evolution in clustering of blue-selected galaxies appears to be due to an increasing fraction of late-type or star-forming galaxies at fainter magnitudes, combined with weaker small-scale clustering for late types. These conclusions are entirely empirical and independent of galaxy evolution models.

Bernstein, G. M.↗
NASA NTRS2000

Long-Term Global Morphology of Gravity Wave Activity Using UARS Data

This report provides a broad outline of the total body of research conducted during the past three years. We report on detailed model studies of the precise way in which infrared limb scanning satellites explicitly detect gravity waves, and use these insights to resolve earlier discrepant zonal mean estimates of gravity wave variances from satellite limb scanners. Detailed analysis of CRISTA temperature fluctuations are outlined, which provide new global information on long-wavelength stratospheric gravity waves generated by mountains, tropical convection and the mid-latitude jet stream-vortex system, as well as interactions higher in the mesosphere with the diurnal tide. The detailed insights gained from analyzing CRISTA data are applied to provide a multiyear analysis of stratospheric mountain waves over the Andes evident in MLS limb-track data. We also demonstrate for the first time that stratospheric temperature data from the CLAES instrument on UARS resolved gravity waves, and we highlight mountain wave information in a subset of these data. Final conclusions and recommendations are set forth, and the present findings are directly related to the original goals of the research contract. A full list of publications that resulted from this research is provided.

Eckermann, Stephen D.↗