Fine Tuning the SST k − ω Turbulence Model Closure Coefficients for Improved NASCAR Cup Racecar Aerodynamic Predictions

Abstract

<div class="section abstract"><div class="htmlview paragraph">Faster turn-around times and cost-effectiveness make the Reynolds Averaged Navier-Stokes (RANS) simulation approach still a widely utilized tool in racecar aerodynamic development, an industry where a large volume of simulations and short development cycles are constantly demanded. However, a well-known flaw of the RANS methodology is its inability to properly characterize the separated and wake flow associated with complex automotive geometries using the existing turbulence models. Experience suggests that this limitation cannot be overcome by simply refining the meshing schemes alone. Some earlier researches have shown that the closure coefficients involved in the RANS turbulence modeling transport equations most times influence the simulation prediction results. The current study explores the possibility of improving the performance of the SST <i>k</i> − <i>ω</i> turbulence model, one of the most popular turbulence models in motorsports aerodynamic applications, by re-evaluating the values of certain model closure constants. A detailed full-scale current generation NASCAR Cup racecar was used for the investigation. The simulations were run using a commercial CFD package STAR-CCM+ (version 13.04.010). Five different closure coefficients in the SST <i>k</i> − <i>ω</i> model, <i>σ</i><sub><i>k</i>1</sub>, <i>σ</i><sub><i>k</i>2</sub>, <i>σ</i><sub><i>ω</i>1</sub>, <i>σ</i><sub><i>ω</i>2</sub> and <i>β</i><sup>∗</sup>, were examined. The investigation suggests the influence of each closure coefficient on the simulation prediction results are significantly different. <i>β</i><sup>∗</sup> appeared to be the most sensitive closure coefficient whereas both <i>σ</i><sub><i>k</i>1</sub> and <i>σ</i><sub><i>k</i>2</sub> had almost no effect on the NASCAR Cup racecar aerodynamic predictions. This study proposes a new set of SST <i>k</i> − <i>ω</i> turbulence model closure coefficients which has the potential of providing better-correlated aerodynamic predictions of a NASCAR Cup racecar under a range of different operating conditions.</div></div>

A Detailed Numerical Study on Aerodynamic Interactions of Tandem Wheels on a Generic Vehicle

“…A higher resolution may be required for the wheelhouse as a significantly stronger interaction between the wheel and wheelhouse boundary flow is suspected, given that the non-dimensional distance between the two surfaces is 0.065 d. The k-ω SST model predicted the ∆C D within 10% of the experiment for the 1.37 ratio; however, the trend of the smaller and larger wheelhouse diameter did not produce comparable results. Despite the ability of k-ω SST to predict adverse pressure gradients in the boundary layer [45], RKE has superior performance in predicting rotating shear flows [40]. The RKE model demonstrated its suitability for this study by accurately predicting drag forces, which were also demonstrated in other studies [15,40,46], and producing a comparable trend.…”

Design and Analysis of Solar Car Chassis for WSC 2023

“…SAS approaches, particularly steady-state Reynolds-Averaged Navier-Stokes (RANS) simulations, retain favor due to their balance between prediction accuracy and reduced computational costs. The critical elements for achieving accurate results lie in the judicious selection of turbulence models and solver parameters [5,6,7]. Beyond the intricacies of aerodynamics, the suspension system emerges as a paramount factor in ensuring passenger safety and comfort.…”

Modeling Water and Thermal Transients Inside Proton Exchange Membrane for Vehicle Application Fuel Cells

“…However, problems related to water and thermal management still need to be resolved before fuel cell power vehicles can be extensively commericialized 2 . The transient variations of water and thermal during dynamic operation, such as deceleration, acceleration, and start-up, are the main causes of fuel cell degradation [3][4] . For example, liquid saturation will increase during fuel cell acceleration, which can lead to water flooding and thus air starvation at the cathode, inducing concentration polarization loss [5][6][7] .…”

“…Mass transport transient was found to be the major cause of the current density profile. Wu et al 3 investigated the local transient characteristics of temperature and ice saturation during cold start processes by a quasi-two-dimensional model, and three hydrogen supply modes were compared and analyzed. However, most of these researches focus on the cell transients during start-up or load step changes.…”