基于神经网络的减阻沟槽壁面形状优化

For the riblet drag reduction, a neural network⁃based method was used to optimize the shape of the riblet surface.This work employed the channel flow model and the governing equations were the viscous incompressible Navier⁃Stokes (NS) equations.The turbulent flow inside the channel was resolved by the direct numerical simulation (DNS) method.According to the numerical method, a compact fourth⁃order central scheme was used for the discretization of the convective term, a fourth⁃order central scheme was applied to the discretization of the viscous term, and a third⁃order Runge⁃Kutta scheme was employed for the time advancement.In the neural network sub⁃optimization process, the constraint equation was the incompressible NS equation, and an adaptive controller based on online learning was used.In the optimization procedure, the control law was based on weakening the spanwise shear stress, and the control quantities were provided by the wall deformation.The optimization results demonstrated that the maximum wall drag reduction reached 17.41%.For the wall optimization, the turbulence intensity was reduced by 19.68%, besides, the vorticity and Reynolds shear stress at the wall also declined.Since the turbulent flow was unsteady, the shape of the optimized wall also varied with time, but the overall shape of the riblets still illustrated a streamwise riblet⁃like configuration.