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Presented here is the QDS method modified to employ an arbitrary governing velocity probability distribution. An algorithm is presented for the computation of QDS particle 'blueprints'. The method, which employs a known continuous velocity probability distribution function, uses a set of fixed QDS particle 'weights', which can be arbitrarily selected. Provided the weights, particle 'blueprint' velocities are computed by taking multiple moments around the governing velocity probability distribution function to provide the discrete representation employed by QDS. In particular, we focus on the results obtained when the governing distribution function is the Chapman-Enskog distribution function. Results are shown for several benchmark tests including a one dimensional standing shock wave and a two dimensional lid driven cavity problem. Finally, the performance of QDS when applied to General Purpose computing on Graphics Processing Units (GPGPU) is demonstrated. [ABSTRACT FROM AUTHOR]

A method of transient adaptive sub-cells (TAS) suitable for unstructured grids in Direct Simulation Monte Carlo (DSMC) simulations is introduced. The TAS algorithm is implemented within the frame work of a parallelized DSMC code (PDSC). Benchmarking tests are conducted for driven cavity flow, hypersonic flow over a two-dimensional cylinder and the unsteady vortex shedding behind a two-dimensional cylinder. The use of TAS enabled a reduction in the computational expense of the simulation since larger sampling cells and less simulation particles could be employed. Furthermore, the collision quality of the simulation was maintained or improved and the preservation of property gradients and vorticity at the scale of the sub-cells enabled correct unsteady vortex shedding frequencies to be predicted. The use of TAS in a parallel DSMC code allows simulations of unsteady processes at a near continuum level to be carried out efficiently, accurately and with acceptable computational times. [ABSTRACT FROM AUTHOR]

A technique called the DSMC Rapid Ensemble Averaging Method (DREAM) for reducing the statistical scatter in the output from unsteady DSMC simulations is introduced. During post-processing by DREAM, the DSMC algorithm is re-run multiple times over a short period before the temporal point of interest thus building up a combination of time- and ensemble-averaged sampling data. The particle data is regenerated several mean collision times before the output time using the particle data generated during the original DSMC run. This methodology conserves the original phase space data from the DSMC run and so is suitable for reducing the statistical scatter in highly non-equilibrium flows. In this paper, the DREAM-II method is investigated and verified in detail. Propagating shock waves at high Mach numbers (Mach 8 and 12) are simulated using a parallel DSMC code (PDSC) and then post-processed using DREAM. The ability of DREAM to obtain the correct particle velocity distribution in the shock structure is demonstrated and the reduction of statistical scatter in the output macroscopic properties is measured. DREAM is also used to reduce the statistical scatter in the results from the interaction of a Mach 4 shock with a square cavity and for the interaction of a Mach 12 shock on a wedge in a channel. [ABSTRACT FROM AUTHOR]