ABSTRACT: We introduce the recent attempts for turbulent friction drag reduction conducted in our research group. While the earlier studies for friction drag reduction mainly targeted at suppression of quasi-streamwise vortices using feedback control, predetermined control methods using streamwise traveling waves or a uniform blowing have also been extensively investigated in the last decade. 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 at 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. In addition, he is currently working on applications of machine learning to fluid mechanics. He has served himself 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.
Date(s) - Nov 20, 2019
12:00 pm - 1:00 pm
38-138 Engineering IV
420 Westwood Plaza, Los Angeles CA 90095