Turbulence Modeling Effects on the CFD Predictions of Flow over a Detailed Full-Scale Sedan Vehicle

• Main Authors: Chunhui Zhang, Charles Patrick Bounds, Lee Foster, Mesbah Uddin
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Fluids
• Subjects: road vehicle aerodynamics; CFD; turbulence modeling effects; RANS; hybrid LES/RANS; DES; external flow
• Online Access: https://www.mdpi.com/2311-5521/4/3/148

In today&#8217;s road vehicle design processes, Computational Fluid Dynamics (CFD) has emerged as one of the major investigative tools for aerodynamics analyses. The age-old CFD methodology based on the Reynolds Averaged Navier&#8722;Stokes (RANS) approach is still considered as the most popular turbulence modeling approach in automotive industries due to its acceptable accuracy and affordable computational cost for predicting flows involving complex geometries. This popular use of RANS still persists in spite of the well-known fact that, for automotive flows, RANS turbulence models often fail to characterize the associated flow-field properly. It is even true that more often, the RANS approach fails to predict correct integral aerodynamic quantities like lift, drag, or moment coefficients, and as such, they are used to assess the relative magnitude and direction of a trend. Moreover, even for such purposes, notable disagreements generally exist between results predicted by different RANS models. Thanks to fast advances in computer technology, increasing popularity has been seen in the use of the hybrid Detached Eddy Simulation (DES), which blends the RANS approach with Large Eddy Simulation (LES). The DES methodology demonstrated a high potential of being more accurate and informative than the RANS approaches. Whilst evaluations of RANS and DES models on various applications are abundant in the literature, such evaluations on full-car models are relatively fewer. In this study, four RANS models that are widely used in engineering applications, i.e., the realizable <inline-formula> <math display="inline"> <semantics> <mrow> <mi>k</mi> <mo>&#8722;</mo> <mi>&#949;</mi> </mrow> </semantics> </math> </inline-formula> two-layer, Abe&#8722;Kondoh&#8722;Nagano (AKN) <inline-formula> <math display="inline"> <semantics> <mrow> <mi>k</mi> <mo>&#8722;</mo> <mi>&#949;</mi> </mrow> </semantics> </math> </inline-formula> low-Reynolds, SST <inline-formula> <math display="inline"> <semantics> <mrow> <mi>k</mi> <mo>&#8722;</mo> <mi>&#969;</mi> </mrow> </semantics> </math> </inline-formula>, and V2F are evaluated on a full-scale passenger vehicle with two different front-end configurations. In addition, both cases are run with two DES models to assess the differences between the flow predictions obtained using RANS and DES.