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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Golden Ratio Versus Pi as Random Sequence Sources for Monte Carlo Integration

We discuss here the relative merits of these numbers as possible random sequence sources. The quality of these sequences is not judged directly based on the outcome of all known tests for the randomness of a sequence. Instead, it is determined implicitly by the accuracy of the Monte Carlo integration in a statistical sense. Since our main motive of using a random sequence is to solve real world problems, it is more desirable if we compare the quality of the sequences based on their performances for these problems in terms of quality/accuracy of the output. We also compare these sources against those generated by a popular pseudo-random generator, viz., the Matlab rand and the quasi-random generator ha/ton both in terms of error and time complexity. Our study demonstrates that consecutive blocks of digits of each of these numbers produce a good random sequence source. It is observed that randomly chosen blocks of digits do not have any remarkable advantage over consecutive blocks for the accuracy of the Monte Carlo integration. Also, it reveals that pi is a better source of a random sequence than theta when the accuracy of the integration is concerned.

Sen, S. K.↗
NASA NTRS1987

Imaginary time path integral Monte Carlo route to rate coefficients for nonadiabatic barrier crossing

Nonadiabatic transitions are central to many areas of chemical and condensed matter physics, ranging from biological electron transfer to the optical properties of one-dimensional conductors. Here, a path integral Monte Carlo method is used to simulate such transitions, based on the observation that nonadiabatic rate coefficients are often dominated by saddle point trajectories that correspond to an imaginary time. Simple analytic theories can be used to continue these imaginary time correlation functions to determine rate coefficients. The advantages and drawbacks of this approach are discussed.

Wolynes, Peter G.↗
NASA NTRS1976

Multivariate normal integration

Monte Carlo program evaluates integrals over rectangular regions for dimensions less than six and over elliptical regions in bivariate case. Program gives positive definite symmetric variance/covariance matrix factorization and calculates reciprocal of lower triangular matrix and product of diagonal elements of triangular matrix.

Falls, L. W.↗
NASA NTRS1993

Monte Carlo analysis of the Titan III/Transfer Orbit Stage guidance system for the Mars Observer mission

An important part of space launch vehicle mission planning for a planetary mission is the integrated analysis of guidance and performance dispersions for both booster and upper stage vehicles. For the Mars Observer mission, an integrated trajectory analysis was used to maximize the scientific payload and to minimize injection errors by optimizing the energy management of both vehicles. This was accomplished by designing the Titan III booster vehicle to inject into a hyperbolic departure plane, and the Transfer Orbit Stage (TOS) to correct any booster dispersions. An integrated Monte Carlo analysis of the performance and guidance dispersions of both vehicles provided sensitivities, an evaluation of their guidance schemes and an injection error covariance matrix. The polynomial guidance schemes used for the Titan III variable flight azimuth computations and the TOS solid rocket motor ignition time and burn direction derivations accounted for a wide variation of launch times, performance dispersions, and target conditions. The Mars Observer spacecraft was launched on 25 September 1992 on the Titan III/TOS vehicle. The post flight analysis indicated that a near perfect park orbit injection was achieved, followed by a trans-Mars injection with less than 2sigma errors.

Bell, Stephen C.↗
NASA NTRS2002

A Simplified, Closed-Form Method for Screening Spacecraft Orbital Heating Variations

A closed-form analytical technique has been developed to screen orbital average heating variations as a function of beta angle, altitude, surface area, and surface optical properties. Using planetary view factor equations for surfaces parallel-to and normal-to the local vertical, a cylindrical umbral shadow approximation, and a simplified albedo flux model, heating rate equations are formulated and then integrated to obtain orbital average heating. The results are compared to detailed analytical predictions using Monte Carlo integration and an assessment of error is presented.

Rickman, S. L.↗
NASA NTRS2004

Neural network uncertainty assessment using Bayesian statistics: a remote sensing application

Neural network (NN) techniques have proved successful for many regression problems, in particular for remote sensing; however, uncertainty estimates are rarely provided. In this article, a Bayesian technique to evaluate uncertainties of the NN parameters (i.e., synaptic weights) is first presented. In contrast to more traditional approaches based on point estimation of the NN weights, we assess uncertainties on such estimates to monitor the robustness of the NN model. These theoretical developments are illustrated by applying them to the problem of retrieving surface skin temperature, microwave surface emissivities, and integrated water vapor content from a combined analysis of satellite microwave and infrared observations over land. The weight uncertainty estimates are then used to compute analytically the uncertainties in the network outputs (i.e., error bars and correlation structure of these errors). Such quantities are very important for evaluating any application of an NN model. The uncertainties on the NN Jacobians are then considered in the third part of this article. Used for regression fitting, NN models can be used effectively to represent highly nonlinear, multivariate functions. In this situation, most emphasis is put on estimating the output errors, but almost no attention has been given to errors associated with the internal structure of the regression model. The complex structure of dependency inside the NN is the essence of the model, and assessing its quality, coherency, and physical character makes all the difference between a blackbox model with small output errors and a reliable, robust, and physically coherent model. Such dependency structures are described to the first order by the NN Jacobians: they indicate the sensitivity of one output with respect to the inputs of the model for given input data. We use a Monte Carlo integration procedure to estimate the robustness of the NN Jacobians. A regularization strategy based on principal component analysis is proposed to suppress the multicollinearities in order to make these Jacobians robust and physically meaningful.

Neural Networks (Computer)↗
NASA NTRS2002

Theoretical Studies of Liquid He-4 Near the Superfluid Transition

We performed theoretical studies of liquid helium by applying state of the art simulation and finite-size scaling techniques. We calculated universal scaling functions for the specific heat and superfluid density for various confining geometries relevant for experiments such as the confined helium experiment and other ground based studies. We also studied microscopically how the substrate imposes a boundary condition on the superfluid order parameter as the superfluid film grows layer by layer. Using path-integral Monte Carlo, a quantum Monte Carlo simulation method, we investigated the rich phase diagram of helium monolayer, bilayer and multilayer on a substrate such as graphite. We find excellent agreement with the experimental results using no free parameters. Finally, we carried out preliminary calculations of transport coefficients such as the thermal conductivity for bulk or confined helium systems and of their scaling properties. All our studies provide theoretical support for various experimental studies in microgravity.

Manousakis, Efstratios↗
NASA NTRS2003

Bose Condensation at He-4 Interfaces

Path Integral Monte Carlo was used to calculate the Bose-Einstein condensate fraction at the surface of a helium film at T = 0:77 K, as a function of density. Moving from the center of the slab to the surface, the condensate fraction was found to initially increase with decreasing density to a maximum value of 0.9, before decreasing. Long wavelength density correlations were observed in the static structure factor at the surface of the slab. A surface dispersion relation was calculated from imaginary-time density-density correlations. Similar calculations of the superfluid density throughout He-4 droplets doped with linear impurities (HCN)(sub n) are presented. After deriving a local estimator for the superfluid density distribution, we find a decreased superfluid response in the first solvation layer. This effective normal fluid exhibits temperature dependence similar to that of a two-dimensional helium system.

Draeger, E. W.↗
NASA NTRS2017

Assimilation of All-Weather GMI and ATMS Observations into HWRF

We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of TCs. These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated to provide accurate initial conditions for the NWP models. The technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI).

Moradi, Isaac↗
NASA NTRS2018

Modeling the Proton Radiation Belt With Van Allen Probes Relativistic Electron-Proton Telescope Data

An empirical model of the proton radiation belt is constructed from data taken during 2013-2017 by the Relativistic Electron-Proton Telescopes on the Van Allen Probes satellites. The model intensity is a function of time, kinetic energy in the range 18-600 megaelectronvolts, equatorial pitch angle, and L shell of proton guiding centers. Data are selected, on the basis of energy deposits in each of the nine silicon detectors, to reduce background caused by hard proton energy spectra at low L. Instrument response functions are computed by Monte Carlo integration, using simulated proton paths through a simplified structural model, to account for energy loss in shielding material for protons outside the nominal field of view. Overlap of energy channels, their wide angular response, and changing satellite orientation require the model dependencies on all three independent variables be determined simultaneously. This is done by least squares minimization with a customized steepest descent algorithm. Model uncertainty accounts for statistical data error and systematic error in the simulated instrument response. A proton energy spectrum is also computed from data taken during the 8 January 2014 solar event, to illustrate methods for the simpler case of an isotropic and homogeneous model distribution. Radiation belt and solar proton results are compared to intensities computed with a simplified, on-axis response that can provide a good approximation under limited circumstances.

Selesnick, R. S.↗
NASA NTRS2018

Assimilation of All-Weather GMI and ATMS Observations into HWRF

We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of TCs. These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated to provide accurate initial conditions for the NWP models. The technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI).

Moradi, Isaac↗
NASA NTRS2017

MCRadar: A Monte Carlo Solver for Cloud and Precipitation Radar

Multiple scattering produces anomalous echoes in observed radar profiles that cannot be explained by other phenomena. These effects are most obvious for spaceborne platforms, at shorter wavelengths, and in convection, as multiple scattering is governed by antenna beam width, optical depth, and albe do; however, multiple scattering has been observed in a range of precipitating conditions. To account for the effects of multiple scattering, Monte Carlo integration is employed in a flexible framework to enable arbitrary radar configurations with finite Gaussian beams for three-dimensional atmospheric scenarios. The three-dimensional nature of the model, coupled with the finite antenna response, also allows for consideration of nonuniform beam filling effects that often coincide with multiple scattering. Examples of airborne and spaceborne radars at various wavelengths are used to illustrate the effects of multiple scattering and nonuniform beam filling. The MCRadar code is currently available in the development version of ARTS.

Adams, Ian Stuart↗
NASA NTRS2018

Assimilation of GMI and ATMS Observations in the Rainbands of Hurricanes

We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of tropical cyclones (TC). These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated into numerical models to provide accurate initial conditions for the NWP models. The BMCI technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI). The retrieved profiles are then assimilated into Hurricane WRF using GSI data assimilation system. This talk presents the development of the BMCI retrieval system as well as the preliminary results of assimilating the retrievals into NASA GEOS-5 model.

Moradi, Isaac↗
NASA NTRS2019

Assimilation of Microwave Observations in the Rainbands of Tropical Cyclones

We propose a novel Bayesian Monte Carlo Integration (BMCI) technique to retrieve the profiles of temperature, water vapor, and cloud liquid/ice water content from microwave cloudy measurements in the presence of tropical cyclones (TC). These retrievals then can either be directly used by meteorologists to analyze the structure of TCs or be assimilated into numerical models to provide accurate initial conditions for the NWP (Numerical Weather Prediction) models. The BMCI technique is applied to the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) and Global Precipitation Measurement (GPM) Microwave Imager (GMI). The retrieved profiles are then assimilated into Hurricane WRF (Weather Research and Forecasting) using the GSI (Gridpoint Statistical Interpolation) data assimilation system.

Moradi, Isaac↗