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Tuesday, February 19, 2019 at 4:00pm
ABSTRACT: Many environmental and industrial flows involve the turbulent transport of a dispersed phase, such as aerosols in urban environments, water droplets in clouds, and sediments in the ocean. A common phenomenon in turbulent dispersed multiphase flows is the preferential sampling of the flow structures by the dispersed phase, which unevenly distribute in space and form clusters. The properties of these clusters are central to key physical processes, thus resolving or modeling their characteristic scales is required in simulations of turbulent dispersed multiphase flows. Although the equations that describe these flows have been known for decades, direct numerical predictions of clustering at high Reynolds numbers typically remain unfeasible, because resolving the participating hydrodynamic scales is computationally demanding. Therefore, reduced-order modeling strategies are required to capture the effect of the scales unresolved by the coarse computational grid on the transport of the dispersed phase.
In this talk, I will present two new subgrid-scale models that tackle the problem of predicting preferential concentration in large-eddy simulations (LES) of turbulent dispersed multiphase flows. Both models are formulated in physical space and contain no adjustable parameters. The first approach employs an approximate deconvolution technique based on elliptic 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 structures. I will also introduce a wavelet-based multi-resolution analysis framework that enables the study of the spatially localized spectral characteristics of preferential concentration. The analysis supports the modeling efforts and suggests a third, complementary, modeling strategy based on the dynamic refinement of the computational grid around clusters. The performance of the 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.
BIOGRAPHICAL SKETCH: Maxime Bassenne completed his Ph.D. in Mechanical Engineering at the Center for Turbulence Research at Stanford University in 2019. He developed physics-based reduced-order models of small-scale turbulence to bridge the predictive capability gap between single-phase and multiphase flow simulations. He also designed wavelet-based analysis tools to advance the understanding of various multi-physics thermo-fluid systems, and built a general methodology for achieving accurate and highly stable numerical solutions in existing computational solvers. He currently works on enabling interpretable data-driven computer-aided tools in the Stanford Department of Radiation Oncology at the Laboratory of Artificial Intelligence in Medicine and Biomedical Physics. His research interests center around bridging mathematical and physical disciplines, data-driven and equation-based modeling approaches, and bioinspired engineering principles to tackle important problems in fluid mechanics and medical physics to motivate technological innovations. Bassenne previously received an M.S. in Mechanical Engineering from Stanford University and an M.S. in Engineering from Ecole Centrale Paris. He is the recipient of the Milton Van Dyke and the Gallery of Fluid Motion awards from the American Physical Society, and the Centennial Teaching Assistant award from the Stanford School of Engineering.