In computational fluid dynamics simulations of industrial flows, models based on the Reynolds-averaged Navier–Stokes (RANS) equations are expected to play an important role in decades to come. However, model uncertainties are still a major obstacle for the predictive capability of RANS simulations. This review examines both the parametric and structural uncertainties in turbulence models. We review recent literature on data-free (uncertainty propagation) and data-driven (statistical inference) approaches for quantifying and reducing model uncertainties in RANS simulations. Moreover, the fundamentals of uncertainty propagation and Bayesian inference are introduced in the context of RANS model uncertainty quantification. Finally, the literature on uncertainties in scale-resolving simulations is briefly reviewed with particular emphasis on large eddy simulations.

中文翻译：

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RANS 模拟中模型不确定性的量化：综述

在工业流动的计算流体动力学模拟中，基于雷诺平均纳维 - 斯托克斯 (RANS) 方程的模型预计将在未来几十年发挥重要作用。然而，模型的不确定性仍然是 RANS 模拟预测能力的主要障碍。本综述考察了湍流模型中的参数和结构不确定性。我们回顾了最近关于在 RANS 模拟中量化和减少模型不确定性的无数据（不确定性传播）和数据驱动（统计推断）方法的文献。此外，在 RANS 模型不确定性量化的背景下，介绍了不确定性传播和贝叶斯推理的基本原理。最后，