This paper aims to propose a precise turbulence model for vehicle aerodynamics, especially for vehicle window buffeting noise.
Aiming at the fact that commonly used turbulence models cannot precisely predict laminar-turbulent transition, a transition-code-based improvement is introduced. This improvement includes the introduction of total stress limitation (TSL) and separation-sensitive model. They are integrated into low Reynolds number (LRN) k-ε model to concern transport properties of total stress and precisely capture boundary layer separations. As a result, the ability of LRN k-ε model to predict the transition is improved. Combined with the constructing scheme of constrained large-eddy simulation (CLES) model, a modified LRN CLES model is achieved. Several typical flows and relevant experimental results are introduced to validate this model. Finally, the modified LRN CLES model is used to acquire detailed flow structures and noise signature of a simplified vehicle window. Then, experimental validations are conducted.
Current results indicate that the modified LRN CLES model is capable of achieving acceptable accuracy in prediction of various types of transition at various Reynolds numbers. And, the ability of this model to simulate the vehicle window buffeting noise is greater than commonly used models.
Based on the TSL idea and separation-sensitive model, a modified LRN CLES model concerning the laminar-turbulent transition for the vehicle window buffeting noise is first proposed.
This work was supported by the National Natural Science Foundation of China under Grant No. 50975083; Postdoctoral Fund of Central South University under Grant No. 205441; and National Key Research and Development Program of China under Grant No. 2017YFB1201103-02.
Chen, Z., Gu, Z. and Wang, Z. (2019), "Research on the simplified vehicle window buffeting noise with a modified LRN CLES model using a transition-code based method", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 29 No. 9, pp. 3169-3191. https://doi.org/10.1108/HFF-10-2018-0550
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