以各向异性强迫均质湍流为基础的雷诺应力衰减模型

Ty Homan, Omkar B. Shende, Ali Mani
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引用次数: 0

摘要

用于求解雷诺平均纳维-斯托克斯方程的模型是预测复杂湍流的常用工具,因为这些模型的计算成本低廉,而且能够提供或估算工程所需的量。然而,结果取决于对非封闭项的正确处理,这需要在模型形式的开发和评估方面取得进展。在本研究中,我们将雷诺应力传输方程视为第二时刻湍流闭合建模的框架。我们特别关注负责雷诺应力衰减的项,这些项可以在同质湍流的典型设置中与其他项分离并单独评估。我们证明,通过使用动量方程的各向异性强迫,我们可以获得传统上在单周期域中无法探测到的湍流状态。与现有文献相比,所获得的数据涵盖了广泛的各向异性湍流行为。我们考虑了各种模型形式,这些数据使我们能够对模型系数进行稳健的选择,并选出一个最佳模型,该模型在用主坐标雷诺兹应力表示时扩展到立方项。然后,我们将所选衰减模型的性能与模拟数据和文献中的流行模型进行了比较,结果表明所建立的模型具有更高的准确性,进而证明了该框架在模型选择和调整方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reynolds stress decay modeling informed by anisotropically forced homogeneous turbulence
Models for solving the Reynolds-averaged Navier-Stokes equations are popular tools for predicting complex turbulent flows due to their computational affordability and ability to provide or estimate quantities of engineering interest. However, results depend on a proper treatment of unclosed terms, which require progress in the development and assessment of model forms. In this study, we consider the Reynolds stress transport equations as a framework for second-moment turbulence closure modeling. We specifically focus on the terms responsible for decay of the Reynolds stresses, which can be isolated and evaluated separately from other terms in a canonical setup of homogeneous turbulence. We show that by using anisotropic forcing of the momentum equation, we can access states of turbulence traditionally not probed in a triply-periodic domain. The resulting data span a wide range of anisotropic turbulent behavior in a more comprehensive manner than extant literature. We then consider a variety of model forms for which these data allow us to perform a robust selection of model coefficients and select an optimal model that extends to cubic terms when expressed in terms of the principal coordinate Reynolds stresses. Performance of the selected decay model is then examined relative to the simulation data and popular models from the literature, demonstrating the superior accuracy of the developed model and, in turn, the efficacy of this framework for model selection and tuning.
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