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

This work proposes a machine-learning or artificial intelligence (AI) control of a low-drag Ahmed body with a rear slant angle phi = 35 degrees with a view to finding strategies for efficient drag reduction (DR). The Reynolds number Re investigated is 1.7 x 10(5) based 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 both DR and control power input are considered. The learning process of the AI control discovers forcing that produces a DR up to 18 %, corresponding to a drag coefficient reduction of 0.06, greatly exceeding any previously reported DR for this body. Furthermore, the discovered forcings may provide alternative solutions, i.e. a tremendously increased control efficiency given a small sacrifice in DR. 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 for DR is unveiled, along with a conceptual model for the altered flow structure under the optimum control or biggest DR. This mechanism is further compared with that under the highest control efficiency.

Automated summary

This work proposes the use of machine learning or artificial intelligence to control the low-drag Ahmed body and find efficient drag reduction strategies. By blowing air from microjets along the edges of the rear window and vertical base, and using pressure sensors and a controller based on the ant colony algorithm, the near-optimal control law is learned and implemented. The highest drag reduction of 18% is achieved by altering the flow structure.