In this paper, we describe the structure of a new Direct Simulation Monte Carlo (DSMC) code that takes advantage of combinatorial geometry (CG) to simulate any rarefied gas flows Medias. The developed code, called DgSMC-B, has been written in FORTRAN90 language with capability of parallel processing using OpenMP framework. The DgSMC-B is capable of handling 3-dimensional (3D) geometries, which is created with first-and second-order surfaces. It performs independent particle tracking for the complex geometry without the intervention of mesh. In addition, it resolves the computational domain boundary and volume computing in border grids using hexahedral mesh. The developed code is robust and selfgoverning code, which does not use any separate code such as mesh generators. The results of six test cases have been presented to indicate its ability to deal with wide range of benchmark problems with sophisticated geometries such as airfoil NACA 0012. The DgSMC-B code demonstrates its performance and accuracy in a variety of problems. The results are found to be in good agreement with references and experimental data. C 2016 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license

The implementation of Monte Carlo simulation on the CUDA Fortran requires a fast random number generation with good statistical properties on GPU. In this study, a GPU-based parallel pseudo random number generator (GPPRNG) have been proposed to use in high performance computing systems. According to the type of GPU memory usage, GPU scheme is divided into two work modes including GLOBAL_MODE and SHARED_MODE. To generate parallel random numbers based on the independent sequence method, the combination of middle-square method and chaotic map along with the Xorshift PRNG have been employed. Implementation of our developed PPRNG on a single GPU showed a speedup of 150x and 470x (with respect to the speed of PRNG on a single CPU core) for GLOBAL_MODE and SHARED_MODE, respectively. To evaluate the accuracy of our developed GPPRNG, its performance was compared to that of some other commercially available PPRNGs such as MATLAB, FORTRAN and Miller-Park algorithm through employing the specific standard tests. The results of this comparison showed that the developed GPPRNG in this study can be used as a fast and accurate tool for computational science applications.

This paper focuses on a new direct simulation Monte Carlo (DSMC) code based on combinatorial geometry (CG) for simulation of any rarefied gas flow. The developed code, called DgSMC-A, has been supplied with an improved CG modeling able to significantly optimize the particle-tracking process, resulting in a highly reduced runtime compared to the conventional codes. The improved algorithm inserts a grid over the geometry and saves those grid elements containing some part of the geometry border. Since only a small part of a grid is engaged with the geometry border, significant time can be saved using the proposed algorithm. Embedding the modified algorithm in the DgSMC-A resulted in a fast, robust and self-governing code needless to any mesh generator. The code completely handles complex geometries created with first-and second-order surfaces. In addition, we developed a new surface area calculator in the CG methodology for complex geometries based on the Monte Carlo method with acceptable accuracy. Several well-known test cases are examined to indicate the code ability to deal with a wide range of realistic problems. Results are also found to be in good agreement with references and experimental data.

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