Artificial intelligence control of a low-drag Ahmed body using distributed jet arrays

Abstract

This work proposes a machine-learning or artificial intelligence (AI) control of a low-drag Ahmed body with a rear slant angleφ = 35° with a view to finding strategies for efficient drag reduction (DR). The Reynolds numberReinvestigated is 1.7 × 105based on the square root of the body cross-sectional area. The control system comprises of five independently operated arrays of steady microjets blowing along the edges of the rear window and vertical base, twenty-six pressure taps on the rear end of the body and a controller based on an ant colony algorithm for unsupervised learning of a near-optimal control law. The cost function is designed such that bothDRand control power input are considered. The learning process of the AI control discovers forcing that produces aDRup to 18 %, corresponding to a drag coefficient reduction of 0.06, greatly exceeding any previously reportedDRfor this body. Furthermore, the discovered forcings may provide alternative solutions, i.e. a tremendously increased control efficiency given a small sacrifice inDR. Extensive flow measurements performed with and without control indicate significant alterations in the flow structure around the body, such as flow separation over the rear window, recirculation bubbles and C-pillar vortices, which are linked to the pressure rise on the window and base. The physical mechanism forDRis unveiled, along with a conceptual model for the altered flow structure under the optimum control or biggestDR. This mechanism is further compared with that under the highest control efficiency.