Parallel Monte Carlo simulation of three-dimensional flow over a flat plate

Nance, Robert P.; Wilmoth, Richard G.; Moon, Bongki; Hassan, H. A.; Saltz, Joel

doi:10.2514/3.689

Cited by 23 publications

(17 citation statements)

References 19 publications

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“…In the past two decades, a number of parallel-DSMC schemes have been implemented and reported in the literature [10][11][12][13]. These schemes utilized either structured or unstructured meshes and mostly static domain decomposition.…”

Section: Introductionmentioning

confidence: 99%

Implementation of unsteady sampling procedures for the parallel direct simulation Monte Carlo method

Cave

Tseng

et al. 2008

Journal of Computational Physics

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Simulations are then conducted of two applications involving the interaction of shocks over wedges. The results of these simulations are compared to experimental data and simulations from the literature where there these are available. In general it was found that ten ensembled runs of DREAM processing could reduce the statistical uncertainty in the raw PDSC data by 2.5-3.3 times, based on the limited number of cases in the present study.

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Section: Introductionmentioning

confidence: 99%

Implementation of unsteady sampling procedures for the parallel direct simulation Monte Carlo method

Cave

Tseng

et al. 2008

Journal of Computational Physics

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“…Thus, the partitioning runtime library, PJOSTLE [11], described in the above is used to repartition the mesh based on some kind of decision policy. It has been shown that, for some problems using DSMC, remapping the domain at fixed intervals leads to poor parallel performance [2]. Therefore, it is desirable to determine a monitoring policy to decide when to repartition.…”

Section: Decision Policy For Repartitioningmentioning

confidence: 99%

“…Repartitioning is not performed until the first local minimum of the degradation function is detected. This decision policy for repartitioning the domain is inherently advantageous in which no prior knowledge of the evolution of the problem is necessary for the determination of the repartitioning interval, and the repartitioning can be expected to follow the dynamics of the problem from previous experience [2,7,9].…”

Section: Decision Policy For Repartitioningmentioning

confidence: 99%

“…As compared with other N-S equation based numerical schemes, the DSMC method possesses much higher parallelism. Thus, the parallel DSMC method represents an opportunity to simulate flows at higher densities with an acceptable runtime [2].…”

Section: Introductionmentioning

confidence: 99%

“…Around the same period of time, dynamic load balancing technique, using Stop At Rise (SAR) [7], which compares the cost of re-mapping with the cost of not re-mapping, based on a degradation function, was used in conjunction with the parallel implementation of DSMC method using a runtime library, CHAOS, for data communication and data structure manipulations on a structured mesh [2]. Results show that it yields significantly faster execution times than the scalar code, although only 25% of parallel efficiency is achieved for 64 processors.…”

Section: Introductionmentioning

confidence: 99%

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Concurrent DSMC Method Using Dynamic Domain Decomposition

Wu¹,

Tseng²

2003

AIP Conference Proceedings

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Abstract. In the current study, a parallel two-dimensional direct simulation Monte Carlo method is reported, which incorporates a multi-level graph-partitioning technique to dynamically decompose the computational domain. The current DSMC method is implemented on an unstructured mesh using particle ray-tracing technique, which takes the advantages of the cell connectivity information. Standard Message Passage Interface (MPI) is used to communicate data between processors. In addition, different strategies applying the Stop at Rise (SAR) [7] scheme is utilized to determine when to adapt the workload distribution among processors. Corresponding analysis of parallel performance is reported using the results of a high-speed driven cavity flow on IBM-SP2 parallel machines (memory-distributed, CPU 160 MHz, RAM 256 MB each) up to 64 processors. Small, medium and large problems, based on the number of particles and cells, are simulated. Results, applying SAR scheme every two time steps, show that parallel efficiency is 57%, 90% and 107% for small, medium and large problems, respectively, at 64 processors. In general, benefits of applying SAR scheme at larger periods decrease gradually with increasing problem size. Detailed time analysis shows that degree of imbalance levels off very rapidly at a relatively low value (30%~40%) with increasing number of processors applying dynamic load balancing, while it, at a value of 5~6 times larger, increases with increasing number of processors without dynamic load balancing. At the end, the completed code is applied to compute a near-continuum gas flow to demonstrate its superior computational capability.

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Parallel DSMC method using dynamic domain decomposition

Tseng

2005

Int. J. Numer. Meth. Engng

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SUMMARYA general parallel direct simulation Monte Carlo method using unstructured mesh is introduced, which incorporates a multi-level graph-partitioning technique to dynamically decompose the computational domain. The current DSMC method is implemented on an unstructured mesh using particle raytracing technique, which takes the advantages of the cell connectivity information. In addition, various strategies applying the stop at rise (SAR) (IEEE Trans Comput 1988; 39:1073-1087) scheme is studied to determine how frequent the domain should be re-decomposed. A high-speed, bottom-driven cavity flow, including small, medium and large problems, based on the number of particles and cells, are simulated. Corresponding analysis of parallel performance is reported on IBM-SP2 parallel machine up to 64 processors. Analysis shows that degree of imbalance among processors with dynamic load balancing is about 1 6 -1 2 of that without dynamic load balancing. Detailed time analysis shows that degree of imbalance levels off very rapidly at a relatively low value with increasing number of processors when applying dynamic load balancing, which makes the large problem size fairly scalable for processors more than 64. In general, optimal frequency of activating SAR scheme decreases with problem size. At the end, the method is applied to compute two two-dimensional hypersonic flows, a three-dimensional hypersonic flow and a three-dimensional near-continuum twin-jet gas flow to demonstrate its superior computational capability and compare with experimental data and previous simulation data wherever available.

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Parallel Monte Carlo simulation of three-dimensional flow over a flat plate

Cited by 23 publications

References 19 publications

Implementation of unsteady sampling procedures for the parallel direct simulation Monte Carlo method

Implementation of unsteady sampling procedures for the parallel direct simulation Monte Carlo method

Concurrent DSMC Method Using Dynamic Domain Decomposition

Parallel DSMC method using dynamic domain decomposition

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