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Turbulent flow manipulation using sliding mode and machine learning control : from the flow over a backward-facing step to a real-world car

Abstract : The present work aims to pre-evaluate flow control parameters to reduce the drag in a real vehicle. Two different actuation mechanisms (Murata’s micro-blower, and air-knives) are characterized and compared to define their advantages and limitations. Murata micro-blowers energized the boundary layer to directly perturb the vortex structures formed in the shear layer region. The air-knife has a rounded surface, adjacent to the slit exit, that could be considered as an active boat-tail (Coanda effect) for drag reduction. Different open-loop and closed-loop control strategies are examined, such as continuous blowing, periodic forcing, sliding mode control (SMC) and machine learning control (MLC). SMC is a robust closed-loop algorithm to track, reach and maintain a predefined set-point; this approach has on-line adaptivity in changing conditions. Machine learning control is a model-free control that learns an effective control law that is judged and optimized with respect to a problem-specific cost/objective function. A hybrid between MLC and SMC may provide adaptive control exploiting the best non-linear actuation mechanisms. Finally, all these parameters are brought together and tested in real experimental applications representative of the mean wake and shear-layer structures related to control of real cars. For the backward-facing step, the goal is to experimentally reduce the recirculation zone. The flow is manipulated by a row of micro-blowers and sensed by pressure sensors. Initial measurements were carried out varying the periodic forcing. MLC is used to improve performance optimizing a control law with respect to a cost function. MLC is shown to outperform periodic forcing. For the Ahmed body, the goal is to reduce the aerodynamic drag of the square-back Ahmed body. The flow is manipulated by an air-knife placed on the top trailing edge and sensed by a force balance. Continuous blowing and periodic forcing are used as open-loop strategies. SMC and MLC algorithms are applied and compared to the open-loop cases. The pre-evaluation of the flow control parameters yielded important information to reduce the drag of a car. The first real vehicle experiments were performed on a race track. The first actuator device concept and sensor mechanism are presented.
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Submitted on : Tuesday, February 12, 2019 - 2:13:12 PM
Last modification on : Tuesday, November 23, 2021 - 9:45:18 AM
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  • HAL Id : tel-02015823, version 1


Camila Chovet. Turbulent flow manipulation using sliding mode and machine learning control : from the flow over a backward-facing step to a real-world car. Fluid mechanics [physics.class-ph]. Université de Valenciennes et du Hainaut-Cambresis, 2018. English. ⟨NNT : 2018VALE0016⟩. ⟨tel-02015823⟩



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