by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, D.C, Springfield, Va .
Written in English
|Statement||Adrian L. Melott.|
|Series||[NASA contractor report] -- NASA CR-197609., NASA contractor report -- NASA CR-197609.|
|Contributions||United States. National Aeronautics and Space Administration.|
|The Physical Object|
We have recently conducted a controlled comparison of a number of approximations for gravitational clustering against the same n-body simulations. These include ordinary linear perturbation theory (Eulerian), the lognormal approximation, the adhesion approximation, the frozen-flow approximation, the Zel'dovich approximation (describable as first-order Lagrangian perturbation Cited by: The adhesion approximation produced the most accurate nonlinear power spectrum and density distribution, but its phase errors suggest mass condensations were moved somewhat incorrectly. Due to its better reproduction of the mass density distribution function and power spectrum, adhesion might be preferred for some : A L Melott. Comparison of dynamical approximation schemes for nonlinear gravitaional clustering. By Adrian L. Melott. Abstract. We have recently conducted a controlled comparison of a number of approximations for gravitational clustering against the same n-body simulations. These include ordinary linear perturbation theory (Eulerian), the lognormal Author: Adrian L. Melott. Approximation methods for non-linear gravitational clustering. We compare the results of full dynamical evolution using particle codes and the various other approximation schemes. To put the models we discuss into perspective, we give a brief review of the observed properties of galaxy clustering and the statistical methods used to quantify.
Title: Approximation Methods for Non-linear Gravitational Clustering. We compare the results of full dynamical evolution using particle codes and the various other approximation schemes. To put the models we discuss into perspective, we give a brief review of the observed properties of galaxy clustering and the statistical methods used to. State estimation scheme for nonlinear dynamical systems based on the stochastic approximation Convergence Conditions of Dynamic Stochastic Approximation Method for Nonlinear Stochastic Discrete-Time Dynamic Systems. IEEE Trans. To have a more convenient comparison, the results are depicted in some performance profiles. Moreover, the. The cluster's core radius and half-mass radius are indicated by dashed red circles. Figure (b) shows the density profile of the best-fitting King model (with W 0 = ; see Section ). The cluster's tidal radius lies well outside the image shown in part (a); for many clusters, the tidal radius is determined from the best-fitting King model. Approximation of large-scale dynamical systems: An overview A.C. Antoulas and D.C. Sorensen Aug Abstract In this paper we review the state of affairs in the area of approximation of large-scale systems. We distinguish among three basic categories, namely the SVD-based, the Krylov-based and the SVD-Krylov-based approxi-mation methods.
Get this from a library! Comparison of dynamical approximation schemes for non-linear gravitational clustering. [Adrian L Melott; United States. National Aeronautics and . Nonlinear approximation methods such as the Zeldovich approximation, and more recently the frozen flow and linear potential approximations, are sometimes used to simulate nonlinear gravitational. dynamical correlations in addition to the local dynamics of the dynamical mean-ﬁeld approximation while preserving causality. The technique is based on an iterative self-consistency scheme on a ﬁnite-size periodic cluster. The dynamical mean-ﬁeld approximation~exact result! is obtained by taking the cluster to a single site. A gravitational model is proposed that relates the terrestrially measured value of the gravitational constantG directly to the density and angular velocity of the galaxy. The model indicates a constant scalar value forG within most regions of our galaxy, but predicts thatG will be different in other galaxies and zero in intergalactic space. The model offers explanations for galactic cluster.