Parallelization of direct simulation Monte Carlo method combined with monotonic Lagrangian grid

Abstract

The monotonic Lagrangian grid (MLG) and the direct simulation Monte Carlo (DSMC) methodology were combined on the Thinking Machines CM-5 to create a fast DSMC-MLG code with automatic grid adaptation based on local number densities. The MLG is a data structure in which particles that are close in physical space are also close in computer memory. Using the MLG data structure, physical space is divided into a number of templates (cells), each containing the same number of particles. An MLG-regularization method, stochastic grid restructuring, is implemented to minimize the occurrence of highly skewed cells. Parallelization of the DSMC-MLG is achieved by two different mapping techniques. First, simulated particles are mapped onto the parallel processors for the particle-oriented processes, such as convection, boundary interactions, and MLG sorting. Second, particle templates are mapped onto the processors for computing the macroscopic quantities (i.e., pressure, velocity, density, and temperature) and statistical sampling. In both levels of mapping, the code logic focuses on the structured and fast communications on the CM-5 architecture. The computing time required by the parallel DSMC-MLG code was significantly decreased compared with other parallel efforts and its parallel efficiency on 512 processors achieved approximately 80% for simulation involving one-half million particles.

Simulations of High Knudsen Number Flows in a Channel-Wedge Configuration

“…In the DSMC±MLG, there is, by de® nition, always enough particles in each cell to maintain optimal collision statistics. 9 Oh et al 13 showed very high parallel ef® ciencies and speed ups of two orders of magnitude or more compared to the serial DSMC±MLG, and the ef® ciency increases dramatically as the number of particles increases. DSMC±MLG computations are scalable and allow simulations of millions of particles for engineering computation.…”

“…The MLG was combined with the DSMC resulting in a method that allows automatic grid adaptationbased on the local number densities in the gas. 9 A recentparallelizationof the DSMC±MLG dramaticallyreduced the computationaltime, 13 making applicationsof the method to more complex¯ows possible. The current DSMC±MLG code optimizes the grid by using stochastic grid restructuring(SGR) to ® nd the best grid for the problem considered.…”

Algorithm of two-level parallelization for direct simulation Monte Carlo of unsteady flows in molecular gasdynamics

“…For realization of these algorithms it is necessary to use a computer with architecture which is adequate to a given algorithm. The examples of this type of algorithm is the data parallelization [12,13].…”

“…The fourth type is the combined decomposition which includes all types considered precedingly. The decomposition of computational domain with data parallelization are carried out in [12]. In this paper we shall consider two-level algorithms which include methods of first and third type.…”