Active control of flow past an elliptic cylinder using an artificial neural network trained by deep reinforcement learning  

作　　者：Bofu WANG Qiang WANG Quan ZHOU Yulu LIU 

机构地区：[1]Shanghai Key Laboratory of Mechanics in Energy Engineering,Shanghai Institute of Applied Mathematics and Mechanics,School of Mechanics and Engineering Science,Shanghai University,Shanghai 200072,China [2]Shanghai Frontiers Science Base for Mechanoinfomatic,Shanghai University,Shanghai 200072,China [3]School of Science,Shanghai Institute of Technology,Shanghai 201418,China

出　　处：《Applied Mathematics and Mechanics(English Edition)》2022年第12期1921-1934,共14页应用数学和力学（英文版）

基　　金：Project supported by the National Natural Science Foundation of China (Nos.11988102,92052201,11972220,12032016,11825204,91852202,and 11732010);the Key Research Projects of Shanghai Science and Technology Commission of China (Nos.19JC1412802 and 20ZR1419800)。

摘　　要：The active control of flow past an elliptical cylinder using the deep reinforcement learning(DRL)method is conducted.The axis ratio of the elliptical cylinderΓvaries from 1.2 to 2.0,and four angles of attackα=0°,15°,30°,and 45°are taken into consideration for a fixed Reynolds number Re=100.The mass flow rates of two synthetic jets imposed on different positions of the cylinderθ1andθ2are trained to control the flow.The optimal jet placement that achieves the highest drag reduction is determined for each case.For a low axis ratio ellipse,i.e.,Γ=1.2,the controlled results atα=0°are similar to those for a circular cylinder with control jets applied atθ1=90°andθ2=270°.It is found that either applying the jets asymmetrically or increasing the angle of attack can achieve a higher drag reduction rate,which,however,is accompanied by increased fluctuation.The control jets elongate the vortex shedding,and reduce the pressure drop.Meanwhile,the flow topology is modified at a high angle of attack.For an ellipse with a relatively higher axis ratio,i.e.,Γ1.6,the drag reduction is achieved for all the angles of attack studied.The larger the angle of attack is,the higher the drag reduction ratio is.The increased fluctuation in the drag coefficient under control is encountered,regardless of the position of the control jets.The control jets modify the flow topology by inducing an external vortex near the wall,causing the drag reduction.The results suggest that the DRL can learn an active control strategy for the present configuration.

关 键 词：drag reduction deep reinforcement learning(DRL) elliptical cylinder active control 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O357.5[自动化与计算机技术—控制科学与工程]