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This paper reports an experimental investigation of the wake flow behind a 1/12 scale notchback MIRA model at Re = UL/ν = 6.9×105 (where U is free-stream velocity, L the length of the model and ν viscosity). Focus is placed on the flow asymmetry over the backlight and decklid. Forty pressure taps are used to map the surface pressure distribution on the backlight and decklid, while the wake topology is investigated by means of 2D Particle Image Velocimetry. The analysis of the instantaneous pressure signals over the notch configuration clearly shows that the pressure presents a bi-stable behavior in the spanwise direction, characterized by the switches between two preferred values, which is not found in the vertical direction.

Due to the increasingly stringent environmental regulations all around the world confronted by exhaust emission and energy consumption, improving fuel economy has been the top priority for most automotive manufacturers. In this context, the basic process for vehicle shape development has evolved into optimizing the design to achieve better aerodynamic characteristics, especially drag reduction. Of all the optimization approaches, the gradient-based adjoint method has currently received extensive attention for its high efficiency in calculating the objective sensitivity with respect to geometry parameters, which is the first and foremost step for subsequent shape modification. In this work, the main goal is to explore the adjoint method through optimizing the vehicle shape for a lower drag based on a production SUV. Firstly, the influence of different mesh schemes was discussed on sensitivity prediction of aerodynamic drag.

Given the increasingly stringent environmental regulations, most automotive manufacturers were confronted with tougher exhaust emission and energy consumption standards, thus, improving fuel economy has been the top priority for OEMs during the past few years. In this context, it is quite essential to improve the aerodynamic characteristics, especially drag reduction in vehicle shape development, considering its close correlation with fuel consumption and E-range. Of all the optimization approaches, the gradient-based adjoint method has currently received growing attention for its proven effectiveness in calculating the drag sensitivity with respect to geometry parameters, which is indispensable for subsequent shape modification. In this work, we aim to utilize the adjoint approach to optimize the vehicle shape for a lower drag on the DrivAer models.