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 angle φ = 35° with a view to finding strategies for efficient drag reduction (DR). The Reynolds number Re investigated is 1.7 × 105 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.

Physics-agnostic and physics-infused machine learning for thin films flows: modelling, and predictions from small data

“…Machine learning is implemented to derive multifidelity models (Rezaeiravesh, Mukha & Schlatter 2023), and an ensemble of neural networks is applied to develop a wall model for LES based on the assumption that the flow can be thought of as a combination of blocks (Lozano-Durán & Bae 2023). Flow control is also a popular field where machine learning is implemented (Sonoda et al 2023;Zhang, Fan & Zhou 2023). Recently, artificial neural network-based nonlinear algebraic models were presented for the LES of compressible wall-bounded turbulence (Xu et al 2023).…”

“…Flow control is also a popular field where machine learning is implemented (Sonoda et al. 2023; Zhang, Fan & Zhou 2023). Recently, artificial neural network–based nonlinear algebraic models were presented for the LES of compressible wall-bounded turbulence (Xu et al.…”