Spring ’18 Joint CSC@USC/CommNetS-MHI Seminar Series

Koji Fukagata, Keio University Flow control and machine learning studies at Keio Fukagata Lab Nov 21, 2019, 2:00pm; RRB 208, Laufer Library (note room change)

Abstract

We introduce the recent attempts for turbulent friction drag reduction conducted in our research group. For both the streamwise traveling wave of wall deformation and the uniform blowing, their drag reduction capabilities have been confirmed well by direct numerical simulation at relatively low Reynolds numbers. Prediction of their drag reduction capabilities at higher Reynolds numbers and attempts for experimental confirmation are also ongoing toward their practical implementation. We also introduce our practice on the application of resolvent analysis for designing a more effective feedback control law. In addition, we briefly introduce some recent attempts on the applications of machine learning to turbulent flows.

Biosketch

Koji Fukagata is a Professor in the Department of Mechanical Engineering, Keio University, Japan. He received his PhD degrees from KTH, Sweden, as well as The University of Tokyo, Japan, in year 2000. His main research interest is flow control, especially turbulent drag reduction, and he has been collaborating with Dr. Mitul Luhar (USC) for several years. In addition, he is currently working on applications of machine learning to fluid mechanics. He has served as an Editor of Flow, Turbulence and Combustion since 2015, and currently he is the Chair of the Center for Applied and Computational Mechanics at Keio University as well as one of the Directors of the Japan Society of Fluid Mechanics.