An Efficient Hybrid DSMC/MD Algorithm for Accurate Modeling of Micro Gas Flows
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AbstractAiming at simulating micro gas flows with accurate boundary conditions, an efficient hybrid algorithmis developed by combining themolecular dynamics (MD) method with the direct simulationMonte Carlo (DSMC)method. The efficiency comes from the fact that theMD method is applied only within the gas-wall interaction layer, characterized by the cut-off distance of the gas-solid interaction potential, to resolve accurately the gas-wall interaction process, while the DSMC method is employed in the remaining portion of the flow field to efficiently simulate rarefied gas transport outside the gas-wall interaction layer. A unique feature about the present scheme is that the coupling between the two methods is realized by matching the molecular velocity distribution function at the DSMC/MD interface, hence there is no need for one-toone mapping between a MD gas molecule and a DSMC simulation particle. Further improvement in efficiency is achieved by taking advantage of gas rarefaction inside the gas-wall interaction layer and by employing the "smart-wall model" proposed by Barisik et al. The developed hybrid algorithm is validated on two classical benchmarks namely 1-D Fourier thermal problem and Couette shear flow problem. Both the accuracy and efficiency of the hybrid algorithm are discussed. As an application, the hybrid algorithm is employed to simulate thermal transpiration coefficient in the free-molecule regime for a system with atomically smooth surface. Result is utilized to validate the coefficients calculated from the pure DSMC simulation with Maxwell and Cercignani-Lampis gas-wall interaction models. ©c 2014 Global-Science Press.
CitationLiang T, Ye W (2013) An Efficient Hybrid DSMC/MD Algorithm for Accurate Modeling of Micro Gas Flows. CiCP. Available: http://dx.doi.org/10.4208/cicp.141112.160513a.
SponsorsThis work is supported in part by Award No. SA-C0040/UK-C0016, made by King Abdullah University of Science and Technology, and in part by Hong Kong Research Grants Council under Competitive Earmarked Research Grant 621408.
PublisherGlobal Science Press