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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.

In order to achieve the purpose of saving energy and reducing emission, the improvement of aerodynamic performance plays an increasingly crucial role for car manufacturers. Previous studies have confirmed the validity of gradient-based adjoint algorithm for its high efficiency in shape optimization. In this paper, two important aspects of adjoint approach were explored. One is vehicle aerodynamic optimization with multiple objectives, and the other is using time-averaged flow results as the primal solution, both are issues of high interest in recent applications. First, adjoint shape optimization with steady-state and time-averaged flow simulations were respectively calculated and comparatively discussed based on a production SUV. The shape modifications of the two cases indicated that the impact of primal solution on design change could not be neglected, due to the different intrinsic codes of steady and transient turbulence models.

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.