Active drag reduction of an Ahmed body with a slant angle of $25^{\circ }$, corresponding to the high-drag regime, has been experimentally investigated at Reynolds number $Re=1.7\times 10^{5}$, based on the square root of the model cross-sectional area. Four individual actuations, produced by steady blowing, are applied separately around the edges of the rear window and vertical base, producing a drag reduction of up to 6–14 %. However, the combination of the individual actuations results in a drag reduction 29 %, higher than any previous drag reductions achieved experimentally and very close to the target (30 %) set by automotive industries. Extensive flow measurements are performed, with and without control, using force balance, pressure scanner, hot-wire, flow visualization and particle image velocimetry techniques. A marked change in the flow structure is captured in the wake of the body under control, including the flow separation bubbles, over the rear window or behind the vertical base, and the pair of C-pillar vortices at the two side edges of the rear window. The change is linked to the pressure rise on the slanted surface and the base. The mechanisms behind the effective control are proposed. The control efficiency is also estimated.

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Active drag reduction (DR) of a square-back Ahmed body is experimentally studied based on machine learning or artificial intelligence (AI) control. The control system consists of four independently operated arrays of pulsed microjets, 25 pressure taps and an explorative downhill simplex method controller. Two strategies, i.e. asymmetric and symmetric actuations, are investigated, with 12 and 9 control parameters, respectively. Both achieve a DR by 13%, though with distinct flow physics and control mechanisms behind. A model linking the control parameters with the cost is developed based on Taylor expansion around the K-nearest neighbours of the smallest cost obtained from the AI control, resulting in a substantially reduced deviation between measured and predicted costs, especially when involving a large number of control parameters, compared with that based on Taylor expansion around the optimum cost. Sensitivity analysis, conducted based on the model, indicates that the control efficiency, i.e. the ratio of the power saving from DR to the total power consumption, may reach 55 and 78 for the symmetric and asymmetric strategies, respectively, given a 1–2% sacrifice on DR. This efficiency greatly exceeds that (26.5) obtained by Fan et al. (Fan et al. 2020 Phys. Fluids 32 , 125117. ( doi:10.1063/5.0033156 )), whose independent control parameters are only three.

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