by NASA Ames Research Center, Research Institute for Advanced Computer Science, National Technical Information Service, distributor in [Moffett Field, Calif.], [Springfield, Va .
Written in English
|Statement||E. Gallopoulos, Y. Saad.|
|Series||RIACS technical report -- 89-19., NASA contractor report -- CR-180363., NASA contractor report -- NASA CR-180363.|
|Contributions||Saad, Y., Research Institute for Advanced Computer Science (U.S.), Ames Research Center.|
|The Physical Object|
Some of the methods designed for the numerical solution of differential equations and which present an efficient implementation within a parallel environment are briefly surveyed. Included among these are: the Domain Decomposition and Multigrid hyper-algorithms, the Piecewise Parabolic method, the spectral (frequency) approach, some strategies Author: Carlos A. de Moura. Abstract. We consider the parallel solution of time-dependent partial differential equations. Due to the fact that time is a one-way dimension, traditional methods attack this type of equation by solving the resulting sequence of problems in a sequential : Graham Horton, Ralf Knirsch, Hermann Vollath. For the solution of QGD equations the upwind-type splitting scheme was used based on the additional dissipative terms of QGD system . The QGD equations form a mixed system of hyperbolic and parabolic equations, i.e., a system of the same type as the time-dependent Navier-Stokes equations. Therefore, one can apply the methods used for solving. Parallel Computations focuses on parallel computation, with emphasis on algorithms used in a variety of numerical and physical applications and for many different types of parallel computers. Topics covered range from vectorization of fast Fourier transforms (FFTs) and of the incomplete Cholesky conjugate gradient (ICCG) algorithm on the Cray
() A new parallel algorithm for solving parabolic equations. Advances in Difference Equations () Mass-preserving time second-order explicit–implicit domain decomposition schemes for solving parabolic equations with variable by: Domain decomposition methods are divide and conquer methods for the parallel and computational solution of partial differential equations of elliptic or parabolic type. They include iterative algorithms for solving the discretized equations, techniques for non-matching grid discretizations and techniques for heterogeneous : $ Fluid Mechanics Problems for Qualifying Exam (Fall ) 1. Consider a steady, incompressible boundary layer with thickness, δ(x), that de-velops on a ﬂat plate with leading edge at x = 0. Based on a control volume analysis for the dashed box, answer the following: a) Provide an expression for the mass ﬂux ˙m based on ρ,V ∞,andδ. Efficient Parallel Solution of Nonlinear Parabolic Partial Differential Equations by a Probabilistic Domain Decomposition Article (PDF Available) in Journal of Scientific Computing 43(2)
An efficient method for solving parabolic systems is presented. The proposed method is based on the splitting-up principle in which the problem is reduced to a series of independent 1D problems. Efficient Solution of Parabolic Equations by Krylov Approximation Methods E. Gallopoulos* and Y. Saad* Abstract Inthis paper we take a new look at numerical techniques for solving parabolic equations by the method of lines. The main motivation for the proposed approach is the possibility of exploiting a high degree of parallelism in a simple File Size: 1MB. Domain decomposition methods are divide and conquer computational methods for the parallel solution of partial differential equations of elliptic or parabolic type. The methodology includes iterative algorithms, and techniques for non-matching grid discretizations and heterogeneous approximations. Numerical Analysis of Differential Equations Series solution by separation of variables: in special cases an ana-lytic representation of the (exact) solution may be constructed using the technique ofseparation of variables. This is helpful for checking numeri-cal approximations and provides important insight into the structure of the Size: KB.