Aerodynamic optimization of a generic light truck under unsteady conditions using gradient-enriched machine learning control

We present the first machine-learned multiple-input multiple-output aerodynamic feedback control under varying operating conditions. Closed-loop control is relevant to many fluid dynamic applications ranging from gust mitigation to drag reduction. Existing machine learning control investigations have been mainly applied under steady conditions. The current study leverages gradient-enriched machine learning control (Cornejo Maceda et al. in J Fluid Mech 917:A42, 2021) to identify optimal control laws under unsteady conditions. The approach is exemplified on a coupled oscillator system with unsteady coupling and demonstrated for a generic truck model undergoing a yawing maneuver. Key enablers of the experiment are a rich set of pneumatic actuators and pressure sensors. The results demonstrate the method’s capabilities in identifying an efficient forcing for control under dynamically changing conditions. This automated and generalizable closed-loop control strategy complements and expands the machine learning control field and promises a new fast-track avenue to efficiently control a broader set of fluid flow problems.