Turbulence modeling effects on the aerodynamic characterizations of a NASCAR Generation 6 racecar subject to yaw and pitch changes

Abstract

The characterization of a racecar’s aerodynamic behavior at various yaw and pitch configurations has always been an integral part of its on-track performance evaluation in terms of lap time predictions. Although computational fluid dynamics has emerged as the ubiquitous tool in motorsports industry, a clarity is still lacking about the prediction veracity dependence on the choice of turbulence models, which is central to the prediction variability and unreliability for the Reynolds Averaged Navier–Stokes simulations, which is by far the most widely used computational fluid dynamics methodology in this industry. Subsequently, this paper presents a comprehensive assessment of three commonly used eddy viscosity turbulence models, namely, the realizable [Formula: see text] (RKE), Abe–Kondoh–Nagano [Formula: see text], and shear stress transport [Formula: see text], in predicting the aerodynamic characteristics of a full-scale NASCAR Monster Energy Cup racecar under various yaw and pitch configurations, which was never been explored before. The simulations are conducted using the steady Reynolds Averaged Navier–Stokes approach with unstructured trimmer cells. The tested yaw and pitch configurations were chosen in consultation with the race teams such that they reflect true representations of the racecar orientations during cornering, braking, and accelerating scenarios. The study reiterated that the prediction discrepancies between the turbulence models are mainly due to the differences in the predictions of flow recirculation and separation, caused by the individual model’s effectiveness in capturing the evolution of adverse pressure gradient flows, and predicting the onset of separation and subsequent reattachment (if there be any). This paper showed that the prediction discrepancies are linked to the computation of the turbulent eddy viscosity in the separated flow region, and using flow-visualizations identified the areas on the car body which are critical to this analysis. In terms of racecar aerodynamic performance parameter predictions, it can be reasonably argued that, excluding the prediction of the %Front prediction, shear stress transport is the best choice between the three tested models for stock-car type racecar Reynolds Averaged Navier–Stokes computational fluid dynamics simulations as it is the only model that predicted directionally correct changes of all aerodynamic parameters as the racecar is either yawed from the 0° to 3° or pitched from a high splitter-ground clearance to a low one. Furthermore, the magnitude of the shear stress transport predicted delta force coefficients also agreed reasonably well with test results.

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

“…Additionally, the DES approach is still cost prohibitive for many applications; for example, for the recently developed DrivAer vehicle, the DES simulation is 17-times more expensive than the RANS computations [2]. In present day high-fidelity CFD analyses, simulations with 120 million cells are very common; even a RANS-based well-converged simulation of this size takes more 3000 core-hours for completion (see [4]), implying that a DES would have taken more than 50,000 core-hours for a single case. This makes the use DES analysis impractical for motorsports applications for sure and may be for the passenger vehicle industries as well.…”

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

“…The additional terms À ru i u j in equation (2) are known as the Reynolds stress terms which are due to the fluctuating velocity field. Using the Reynolds decomposition, a time-dependent quantityã(t) can be decomposed as the sum of its time-averaged value A and a fluctuating component a(t), that is,ã(t) = A + a(t).…”

“…As a result, it has become a commonplace to execute scale-resolved (SRS) and scale-averaged (SAS) CFD simulations using over 100 million cells. 1,2 However, existing literature suggests that, in many engineering applications, transient SRS, such as the detached eddy simulation (DES) for short, are viewed by many as the preferred modeling approach over the steady-state Reynolds-averaged Navier–Stokes (RANS) simulations due to its perceived success in providing a better-correlated flow field predictions. These studies found that DES approaches generally perform better than RANS approaches, in terms of predicting integral flow quantities, like force and moment coefficients, and as well as the pressure and velocity fields, especially in the wake region.…”

Computational Analyses of the Effects of Wind Tunnel Ground Simulation and Blockage Ratio on the Aerodynamic Prediction of Flow over a Passenger Vehicle

“…Although a significant number of works have been published concerning CFD and wind tunnel correlations [33][34][35][36][37][38][39][40], very few of these studies investigated the comparison among wind tunnel tests with both static floor, rolling-road, and track testing from a numerical investigation point of view. A numerical comparison between different wind tunnel test sections, viz.…”