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As turbulence modeling has become an indispensable approach to perform flow simulation in a wide range of industrial applications, how to enhance the prediction accuracy has gained increasing attention during the past years. Of all the turbulence models, RANS is the most common choice for many OEMs due to its short turn-around time and strong robustness. However, the default setting of RANS is usually benchmarked through classical and well-studied engineering examples, not always suitable for resolving complex flows in specific circumstances. Many previous researches have suggested a small tuning in turbulence model coefficients could achieve higher accuracy on a variety of flow scenarios. Instead of adjusting parameters by trial and error from experience, this paper introduced a new data-driven method of turbulence model recalibration using adjoint solver, based on Generalized k-ω (GEKO) model, one variant of RANS.

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.

In this paper we present the work which was done at Shanghai-VW for using computational aero-acoustic (CAA) simulation in the vehicle development process to assess and improve the buffeting behavior of a vehicle when the rear side window is open. In the first step, a methodology was established and validated against wind tunnel tests using a Sedan. The methodology consists of a calibration of the CAA model to represent the properties of the cabin interior of the real car in terms of damping, wall compliance and leakage followed by CAA simulations of the full vehicle at different wind speeds to obtain the transient flow field around the exterior shape and inside the passenger compartment. The interior noise spectra are directly calculated from the transient pressure inside the cabin.

The aerodynamic optimization of passenger cars has become a major task in the development process of SVW when developing cars for the Chinese market. The pressure to reduce fuel consumption and emissions is leading to aggressive targets for aerodynamic drag. Furthermore, Chinese regulations require the publication of aerodynamic drag of vehicles sold in the Chinese market. This paper describes the approach taken by Shanghai Volkswagen (SVW) for the aerodynamic development of the New Lavida. During this project, SVW optimized its development process by extensively using CFD simulation to reduce aerodynamic drag in the very early phase of the project, i.e. the design of the upper body. Very often the interaction between styling and aerodynamics is an iterative procedure for finding a compromise between function and styling and a short response time for the aerodynamic evaluation is required.