Department of Mechanical Engineering
A striking feature of particle-laden turbulent flows is the tendency of the disperse phase to cluster. This phenomenon, known as preferential concentration, is pervasive at all scales, e.g. atmospheric and oceanic flows (planetary scale), urban flows (human-habitat scale) and biological flows (human scale). For Computational Fluid Dynamics to contribute to engineering design and physical understanding, affordable computations of transport processes at the macroscale are required. However, the stringent resolution requirement imposed by the smallest scales is generally incompatible with this objective, which motivates the pursuit of large-eddy simulation wherein only the largest scales are resolved by the computational grid. When the effect of the unresolved small scales on the transport and interphase exchange of momentum and energy are significant, appropriate subgrid-scale modeling is required. In this talk, I will focus on three subgrid-scale modeling strategies for tackling this challenging problem of large-eddy simulation for particle-laden turbulent flows. The first is an approximate deconvolution based on elliptic differential filters. The second is based on the deterministic synthesis of small-scale turbulence. Both do not require any tunable parameters and are flexible enough to be deployed in any type of flow solver and grid. Finally, I will present an alternative wavelet-based modeling strategy, which enables dynamic refinement of the grid around particle clusters, particularly relevant for radiative heat transfer simulations.
Maxime Bassenne is a Ph.D. candidate in Mechanical Engineering at Stanford University. He is currently working with Professor Parviz Moin, specializing in physics-based subgrid-scale model development for particle-laden turbulent flows. His research activities also include wavelet-based statistical methods and robust numerical schemes. Last summer, he interned in the Computational Physics Methods group at Los Alamos National Laboratory. Maxime previously received a M.S. in Mechanical Engineering from Stanford University and a M.S. in Engineering from Ecole Centrale Paris. Maxime is the recipient of the 2017 APS/DFD Milton van Dyke award for his work on a pedagogical visualization of the turbulent energy cascade using wavelets.