Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session A17: Flow Control: Drag Reduction
8:00 AM–9:57 AM,
Sunday, November 18, 2018
Georgia World Congress Center
Room: B304
Chair: Matthew Juniper, University of Cambridge
Abstract ID: BAPS.2018.DFD.A17.5
Abstract: A17.00005 : Opposition control of turbulent channel flow with wall pressure using deep neural network*
8:52 AM–9:05 AM
Presenter:
Jinhyuk Yun
(Ajou University, Korea)
Authors:
Jinhyuk Yun
(Ajou University, Korea)
Jungil Lee
(Ajou University, Korea)
The opposition feedback control proposed by Choi et al. (JFM, 1994) successfully reduces the skin-friction of wall-bounded turbulent flow by using blowing/suction at the wall in opposition to the near wall velocity. The motivation of this study is to explore the possibility of using the wall pressure as a control input for the opposition control instead of the near wall velocity. For this purpose, we build a deep neural network (DNN) to predict the near wall velocity from the information of wall pressure in turbulent channel flow. For the learning process to build DNN, instantaneous flow data sets are obtained from direct numerical simulation of turbulent channel flow at Reτ = 178. We examine the prediction performance of DNN according to the plane size of wall pressure as an input, type of DNN model, number of learning data sets, and etc. It is found that the near wall velocity can be successfully predicted by DNN with the wall pressure input. Also, we conduct opposition control of turbulent channel flow based on the DNN constructed, and it is shown that its control performance of skin-friction reduction is similar to that of the original opposition control.
*Supported by the NRF Programs (NRF-2014M3C1B1033980 and 2016R1D1A1B03932120)
To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.DFD.A17.5
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