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Drag reduction of a slanted Ahmed body with many actuators using the explorative gradient method

ORAL

Abstract



This study presents the application of Explorative gradient method (EGM) to optimize the actuation command on an Ahmed body for drag reduction. EGM shows an advantage of tackling high-dimensional optimization for active flow control by reducing the simulation cost from O(1000) to O(100). The model is characterized by a slanted edge angle of 35°. Five groupsof steady blowing slot actuators are deployed on all edges of the rear window and the vertical base. The 10-dimensional designed actuation space b includes amplitudes Ui and directions θi, i = 1, … , 5. The drag coefficient is computed by Reynolds-Averaged Navier-Stokes (RANS) simulations and verified by the Large eddy simulation (LES). The optimal actuation command for the Ahmed body found by EGM leads to 17% drag reduction (cD = 0.2586) compared with the unforced flow (cD = 0.3134). It takes only 354 RANS evaluations by the subspace-aided strategy. All peripheral actuators are directed inward. The top and bottom jets have inclinations of 27° and 22° , while side jets feature stronger inward angles of 42° and 44° . The more the wake is elongated the smaller the pressure gradient. A larger pressure in the near wake is related to the lower drag of the bluff body. Moreover, the wake becomes more slender and symmetric as featured by the velocity equilibrium points marking the vortex centers (solid squares). The increased up-down symmetry is facilitated by the upward blowing of the botto jet. This peripheral inward blowing enables aerodynamic boat-tailing as more effective drag reduction mechanism. This is the first time the additive effects by the actuation direction is found on the slanted low-drag Ahmed body.

Publication: Li, Y., Cui, W., Jia, Q., Li, Q., Yang, Z., Morzynski, M. & Noack, B. R. 2022 Explorative gradient method for active drag reduction of the fluidic pinball and slanted ahmed body. J. Fluid Mech. 932, A7.

Presenters

  • Yiqing Li

    Harbin Institute of Technology, Shenzhen, P.R. China, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China

Authors

  • Yiqing Li

    Harbin Institute of Technology, Shenzhen, P.R. China, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China

  • Zhigang Yang

    Tongji University, Shanghai Automotive Wind Tunnel Center, Tongji University, Shanghai 201804, China

  • Bernd R Noack

    Harbin Institute of Technology, Shenzhen, P.R. China, School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China