Computational analysis of unsteady flow features around a realistic Estate vehicle with hybrid RANS/LES methods

Francois Delassaux (1) , Iraj Mortazavi (1) , Vincent Herbert (2) , Charles Ribes (2)

1 M2N - Modélisation mathématique et numérique
2 Stellantis - PSA Centre Technique de Vélizy

A comparative study is conducted to analyze the flow around a realistic Peugeot Estate car (308 SW) usingRKE and SST RANS models, as well as DDES hybrid RANS/LES approach. The authors used previous workon25◦Ahmed bodies to establish the best grid and DDES parameters for external aerodynamics prediction.The aim of this work is to extend and transpose the numerical methodology to a realistic car. Numericaland physical characteristics related to the simulation of this complex flow are therefore carefully investigated.The numerical results are compared to in-house experiments carried out in S2A Stellantis Wind Tunnel. Twogrids with 115 and 185 million elements, based on 20 prism layers cells near the wall, are used to comparethe three turbulence models. Even with a smaller grid size in the wake and underbody, the predictions ofdrag and lift coefficients are almost identical. Moreover, refining the near-wall region from 20 to 30 prismlayers has no effect on drag coefficient and flow prediction. Consequently, the grid with 20 prism layers and115M cells is used as reference grid in this work. Then, the flow features between RKE, SST and SST DDESmodels are compared to two-dimensional (2D) and three-dimensional (3D) experimental results. RANS givesbetter agreement on aerodynamics forces. However, DDES reproduces accurately the specific three-dimensionaltopology of the flow around the car, in the wake and the underbody when RANS results are globally wrong.

Mots clés: Turbulence modeling; Hybrid methods; Car aerodynamics

Domaines: Mathématiques [math]; Sciences de l'ingénieur [physics]

Francois Delassaux, Iraj Mortazavi, Vincent Herbert, Charles Ribes. Computational analysis of unsteady flow features around a realistic Estate vehicle with hybrid RANS/LES methods. Computers and Fluids, 2023, 262, pp.105935. ⟨10.1016/j.compfluid.2023.105935⟩. ⟨hal-04103180⟩