Drag reduction of a car model by linear genetic programming control

TL;DR

This paper demonstrates that linear genetic programming control can effectively reduce aerodynamic drag on a car model by approximately 22% using open- and closed-loop strategies, with potential broad applicability.

Contribution

The study introduces a model-free, linear genetic programming control framework for aerodynamic drag reduction, capable of selecting optimal open- or closed-loop control laws automatically.

Findings

Achieved 22% drag reduction and 33% pressure recovery.

Control laws include periodic, multi-frequency, and sensor-based feedback.

Feedback actuation mimics high-frequency forcing through sensor selection.

Abstract

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.