On Fine Tuning the SST K–ω Turbulence Model Closure Coefficients for Improved Prediction of Automotive External Flows

Zhang, Chunhui; Uddin, Mesbah; Selent, Christian

doi:10.1115/imece2018-88328

Cited by 3 publications

(1 citation statement)

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“…The analysis presented includes closure coefficients sensitivity of drag and lift forces, and overall flow fields with a goal to provide general guidance on how to formulate a combination of the SST model coefficients that result in an improved CFD prediction of automotive flows. It is noted that this work is an extension of an earlier conference paper by Zhang et al 25 ; this conference paper is based off of PhD dissertation of Zhang. 26…”

Section: Introductionmentioning

confidence: 96%

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

Bounds

Zhang

Uddin

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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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.

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Section: Introductionmentioning

confidence: 96%

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

Bounds

Zhang

Uddin

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

Self Cite

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On the Effects of Parallelization on the Flow Prediction around a Fastback DrivAer Model at Different Attitudes

Misar¹,

Bounds²,

Ahani³

et al. 2021

SAE Technical Paper Series

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Tuning of Turbulence Model Closure Coefficients Using an Explainability Based Machine Learning Algorithm

Bounds¹,

Uddin²,

Desai³

2023

SAE Technical Paper Series

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<div class="section abstract"><div class="htmlview paragraph">This article discusses an application of Machine Learning (ML) tools to improve the prediction accuracy of Computational Fluid Dynamics (CFD) for external aerodynamic workflows. The Reynolds Averaged Navier-Stokes (RANS) approach to CFD has proved to be one of the most popular simulation methodologies due to its quick turnaround times and acceptable level of accuracy for most applications. However, in many cases the accuracy for the RANS models can prove to be suboptimal that can be significantly improved with model closure coefficient tuning. During the original turbulence model creation, these closure coefficients were chosen by somewhat ad hoc methods using simple canonical flows that do not transfer well to flows involving more complex objects, like the automotive bodies used in this work. This work presents a novel method of applying ML tools to CFD to optimize the turbulence closure coefficients by using model explainability tools such as Shapley Values, Shapley Additive exPlanations (SHAP), and ML surrogate models. The 25-degree slant Ahmed body model was used to obtain sampling data to tune closure coefficient in the Menter Shear Stress Transport (SST) turbulence model implemented in the open source CFD code, OpenFOAM v2012. Shapley additive values were then calculated using the samples which showed that β∗ has the strongest influence over the model predictions of lift and drag. ML surrogate models were then applied alongside SHAP providing a better overall sampling efficiency with Shapley additive values and more complete explanations of the model. The SHAP explanations showed that β∗ had the most influence on the force predictions followed by σω2, while σω1, σk1, and σk2 were shown to have little impact. The surrogate model was then used along with its explanations to provide optimized coefficients that reduced the error in the drag and lift predictions to -3.67% and -2.49% respectively, from -9.67% and -75.8%.</div></div>

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On Fine Tuning the SST K–ω Turbulence Model Closure Coefficients for Improved Prediction of Automotive External Flows

Cited by 3 publications

References 0 publications

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

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

On the Effects of Parallelization on the Flow Prediction around a Fastback DrivAer Model at Different Attitudes

Tuning of Turbulence Model Closure Coefficients Using an Explainability Based Machine Learning Algorithm

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