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 k − ω 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.

中文翻译：

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使用修改后的湍流模型闭合系数改进了对经过简化和真实汽车车身的流动的 CFD 预测

尽管存在缺点，但更快的周转时间和成本效益使雷诺平均 Navier-Stokes 建模方法在许多工业应用中仍然是一种流行且广泛使用的方法，包括一般的汽车行业，但特别是赛车行业。现有文献表明，所有雷诺平均 Navier-Stokes 模型通常无法以合理的精度预测压力和速度流场，尤其是在车辆尾流区域。最近的数值工作表明，当使用二方程涡粘性湍流模型时，仅通过网格细化或在湍流传输方程中添加额外的校正项是无法实现实验和计算流体动力学之间改进的相关性的，并且需要额外的努力才能更好地进行预测。在此背景下，本文采用的预测改进策略基于以下认识：湍流模型闭合系数通常指定为常数，它们的值由单个观察或假设的简单函数形式确定，并且这些系数为被构造和/或被约束在极其有限的情况下正确行事。随后，本研究通过分析使用不同于常用默认值的湍流模型闭合系数值运行的模拟，研究了一些选定的湍流模型闭合系数对计算流体动力学预测准确性的影响。这是通过首先研究每个模型参数对预测准确性的个体影响来完成的，然后制定模型闭合系数值的组合以获得具有最佳实验相关性的预测。该程序应用于四个不同的测试对象，包括 12° 攻角的 NACA 4412 翼型、25° 和 35° 倾斜角的 Ahmed 车身以及一辆全尺寸轿车型乘用车。剪应力传输 k − ω 被选为湍流模型，因为它在汽车应用中很受欢迎。观察到，无论测试模型如何，计算流体动力学预测在某种程度上独立于某些闭合系数，而预测显示出对某些模型常数值的强烈依赖性，尤其是耗散方程中的那些。本研究表明，通过使用一组修改后的闭合系数值，可以进行非常相关的计算流体动力学预测。这项研究的结果只是重申了 Stephen Pope 20 年前的结论，即模型的闭合系数不是静态和通用的，而是动态的，必须针对特定的流动情况进行校准。