Improved CFD prediction of flows past simplified and real-life automotive bodies using modified turbulence model closure coefficients

Abstract

In spite of its shortcomings, faster turnaround time and cost-effectiveness make the Reynolds-averaged Navier–Stokes modeling approach still a popular and widely used methodology in many industrial applications, including the automotive industries in general, but the motorsports sector in particular. Existing literature suggests that all Reynolds-averaged Navier–Stokes models generally fail to predict pressure and velocity flow fields with a reasonable accuracy, especially in the vehicle wake region. Recent numerical works suggest that, when using two-equation eddy viscosity turbulence models, improved correlation between the experiment and computational fluid dynamics is not achievable through only mesh refinements or adding additional corrective terms in the turbulence transport equations, and additional efforts are necessary for better predictions. In this backdrop, the prediction improvement strategy adopted in this paper is based on the realization that the turbulence model closure coefficients are normally specified as constants and their values are determined from either a single observation or a simple functional form is assumed and that these coefficients are constructed and/or constrained to behave correctly in extremely limiting circumstances. Subsequently, this study investigated the influence of a few selected turbulence model closure coefficients on the veracity of computational fluid dynamics predictions by analyzing simulations run with turbulence model closure coefficient values that were different from the commonly used default ones. This was done by first investigating the individual effect of each model parameters on the prediction veracity, and then a combination of model closure coefficient values was formulated in order to obtain a prediction with the best experimental correlation. This procedure was applied to four different test objects which include NACA 4412 airfoil at 12° angle of attack, the 25 and 35° slant angle Ahmed body, and a full-scale sedan type passenger vehicle. The shear-stress transport [Formula: see text] was chosen as the turbulence model because of its popularity in automotive applications. It was observed that, irrespective of the test model, the computational fluid dynamics predictions are somewhat independent of some closure coefficients, while the prediction showed strong dependencies on certain model constant values, especially those in the dissipation equation. The present study demonstrated that, by using a modified set of closure coefficient values, very well correlated computational fluid dynamics predictions were possible. The findings from this study simply reiterate a 20-year-old conclusion by Stephen Pope that the closure coefficients for a model are not static and universal but instead are dynamic and must be calibrated for specific flow situations.

CNN-based flow field prediction for bus aerodynamics analysis

“…This is where the error introduced by the turbulence model comes into play, see Grandemande et al 23 and He et al 24 . Different k–ω SST coefficients will produce better results for the different slant angle configurations as studied by Bounds et al 25 . Additionally, the effect of different turbulence modelling methods on the prediction of the Ahmed body’s wake was studied by Guilmineau 26 , proving also that at different slant angles different turbulence modelling approaches produce more accurate predictions, among them the k–ω SST, which predicts drag with different accuracy for different slant angles.…”

Development of a Numerical Investigation Framework for Ground Vehicle Platooning

“…Furthermore, this model was also found to have the most literature available on the improvement of model predictions. The SST's closure coefficients were then tuned through a testing regime which involved varying the closure coefficients β * , σ ω1 , and σ ω1 which have been shown by Bounds et al [41] to be the most sensitive coefficients. The results of each variation are shown in Table 3, where the length of the recirculation bubble is the main flow feature monitored, and was used as the criterion to determine the veracity of the chosen coefficient values.…”

A Comparison Between Experimental Life Road Simulation and Computational Fluid Dynamics and Fluid Structure Interaction for Sedan Car

“…The experimental test rig was designed to visualize the flow downstream of the road vehicle, using "SKODA OCTAVIA A4", as a field model with the help of flow tufts that simulate the flow at the rear end of the car [16].…”

“…Bounds et al, [16] investigated the influence of the SST k -w turbulence model closure coefficient values on the prediction veracity of force coefficients for three different geometries: NACA 4412 at 12 deg of attack, the Ahmed body model with 25 deg and 35 deg slant angles, and a real-life full-scale passenger car model. Their main finding was that the closure coefficient betta has the strongest influence on the prediction of drag and lift and, hence, the overall flow field.…”