Ph.D. Oral Examination - Department of Mechanical Engineering, Stanford University (open portion)

Title: Subgrid-Scale Modeling and Wavelet Analysis for Inertial Point Particles in Turbulence Abstract: A striking feature of particle-laden turbulent flows is the presence of particle clouds that result from the tendency of inertial particles to preferentially sample specific regions of the flow field. This phenomenon is central to a number of important physical processes. However, computational predictions of preferential concentration at high Reynolds numbers are challenging, since the resolution of the participating scales is typically unaffordable. In this talk, I present two new subgrid-scale models that tackle the problem of predicting preferential concentration in large-eddy simulations (LES) of particle-laden turbulence. Both models are dynamic and are formulated in physical space. The first one employs an approximate deconvolution technique based on differential filters that provides velocity fluctuations on the LES grid. The second model regenerates small scales on a finer grid based on non-linear convective interactions between resolved eddies. A wavelet-based analysis of preferential concentration is also presented that supports the philosophy of the modeling efforts. The performance of these new models is addressed in LES of homogeneous-isotropic turbulence and wall-modeled LES of turbulent channel flow laden with a dilute suspension of inertial point particles. Improved predictions are observed in most of the cases when these models are used. Advisor: Parviz Moin