Neural networks for active drag reduction in fully turbulent airflows

This paper presents the application of a neural network controller to the problem of active drag reduction in a fully turbulent 3D fluid flow regime. Based on a successful yet infeasible previous active control scheme, we trained a neural network to mimic the control law using only surface spanwise shear stress measurements. We then demonstrate the ability of a neural controller implemented in an adaptive inverse model scheme to maintain a drag-reduced flow in a fully turbulent fluid simulation. By observing the weights of the on-line controller, a simple control law that predicts actuations proportional to the spanwise derivative of the spanwise shear stress is derived. Finally we examine the amount of parameter variation that may be required for a physical implementation of linear and nonlinear neural controllers.