Quantification of Model Uncertainty in RANS Simulations: A Review

H. Xiao, P. Cinnella · 2019 · Prog. Aerosp. Sci.（DOI 10.1016/j.paerosci.2018.10.001）

| 编号 QC072 | 主题 经典 CFD 基础 | 链接 arXiv:1806.10434 |

摘要

摘要保留原文（英文）

摘要取自 arXiv / 出版方元数据，未作翻译，以保证与原文一致。

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.

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