  We investigate open- and closed-loop active control for aerodynamic drag
reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is
examined at $Re_{H}\approx3\times10^{5}$ based on body height. The actuation is
performed with pulsed jets at all trailing edges combined with a Coanda
deflection surface. The flow is monitored with pressure sensors distributed at
the rear side. We apply a model-free control strategy building on Dracopoulos &
Kent (Neural Comput. & Applic., vol. 6, 1997, pp. 214-228) and Gautier et al.
(J. Fluid Mech., vol. 770, 2015, pp. 442-457). The optimized control laws
comprise periodic forcing, multi-frequency forcing and sensor-based feedback
including also time-history information feedback and combination thereof. Key
enabler is linear genetic programming as simple and efficient framework for
multiple inputs (actuators) and multiple outputs (sensors). The proposed linear
genetic programming control can select the best open- or closed-loop control in
an unsupervised manner. Approximately 33% base pressure recovery associated
with 22% drag reduction is achieved in all considered classes of control laws.
Intriguingly, the feedback actuation emulates periodic high-frequency forcing
by selecting one pressure sensor in the optimal control law. Our control
strategy is, in principle, applicable to all multiple actuators and sensors
experiments.
