High-Order Shock-capturing Methods for Study of Shock-induced Turbulent Mixing with Adaptive Mesh Refinement Simulations

Speaker: Man Long Wong, Science and Technology Corporation / NASA Advanced Supercomputing Division
June 6, 2019

Presentation

Abstract

The shock-induced instabilities at interfaces separating materials of different densities and the subsequent turbulent mixing are commonly found in natural phenomena and engineering applications with high-speed flows. Accurate and efficient numerical methods are essential for computations of this kind of flows that involve both shock waves and turbulent motions. A type of high-order shock-capturing schemes is improved to preserve fine-scale features in smooth regions with minimal dissipation while still has the ability to provide sufficient numerical dissipation to capture shocks and discontinuities robustly. The high-order scheme is implemented in an adaptive mesh refinement (AMR) framework which can efficiently employ the computational resources by dynamically allocating fine grid cells only to regions containing features of interest during simulations. The AMR framework is used to conduct high-resolution simulations for the study of the shock-induced mixing at planar interfaces between sulphur hexafluoride and air. The flows under 2D and 3D configurations are compared and the effects of the Reynolds number on the mixing are also studied through varying the physical transport coefficients. Further analysis is conducted on the variable-density flows in the post-transition regime to examine the validity of the modeling assumptions in the Besnard-Harlow-Rauenzahn-type turbulence model for the unclosed terms in the Favre-averaged Navier-Stokes equations.

Biography

Man Long Wong received his Ph.D. in Aeronautics & Astronautics from Stanford University in 2019 under the supervision of Sanjiva Lele. His research interests include high-order numerical methods, adaptive meshing, and multi-species turbulent mixing. Man Long joined the LAVA group at the NASA Advanced Supercomputing Division at NASA Ames Research Center in April 2019 and is currently working on the development of numerical methods.

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