Abstract This article discusses the use of numerical optimization procedures to aid in the calibration of turbulence model coefficients. Such methods would increase the rigor and repeatability of the calibration procedure by requiring clearly defined and objective optimization metrics, and could be used to identify unique combinations of coefficient values for specific flow problems. The approach is applied to the re-calibration of an explicit algebraic Reynolds stress model for the incompressible planar mixing layer using the Nelder–Mead simplex algorithm and a micro-genetic algorithm with minimally imposed constraints. Three composite fitness functions, each based upon the error in the mixing layer growth rate and the normal and shear components of the Reynolds stresses, are investigated. The results demonstrate a significant improvement in the target objectives through the adjustment of three pressure-strain coefficients. Adjustments of additional coefficients provide little further benefit. Issues regarding the effectiveness of the fitness functions and the efficiency of the optimization algorithms are also discussed.

中文翻译：

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湍流模型标定优化技术的应用

摘要 本文讨论了使用数值优化程序来帮助校准湍流模型系数。通过要求明确定义的客观优化指标，此类方法将提高校准程序的严格性和可重复性，并可用于识别特定流量问题的系数值的独特组合。该方法用于使用 Nelder-Mead 单纯形算法和具有最小施加约束的微遗传算法重新校准不可压缩平面混合层的显式代数雷诺应力模型。研究了三个复合适应度函数，每个函数都基于混合层生长速率的误差以及雷诺应力的法向和剪切分量。结果表明，通过调整三个压力应变系数可以显着改善目标。附加系数的调整几乎没有进一步的好处。还讨论了有关适应度函数的有效性和优化算法的效率的问题。