Turbulence Modeling in the Age of Data

K. Duraisamy, G. Iaccarino, H. Xiao · 2019 · Annu. Rev. Fluid Mech. 51, 357–377

| 编号 QC077 | 主题 经典 CFD 基础 | 链接 arXiv:1804.00183 |

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。

Data from experiments and direct simulations of turbulence have historically been used to calibrate simple engineering models such as those based on the Reynolds-averaged Navier–Stokes (RANS) equations. In the past few years, with the availability of large and diverse datasets, researchers have begun to explore methods to systematically inform turbulence models with data, with the goal of quantifying and reducing model uncertainties. This review surveys recent developments in bounding uncertainties in RANS models via physical constraints, in adopting statistical inference to characterize model coefficients and estimate discrepancy, and in using machine learning to improve turbulence models. Key principles, achievements and challenges are discussed. A central perspective advocated in this review is that by exploiting foundational knowledge in turbulence modeling and physical constraints, data-driven approaches can yield useful predictive models.

本站词条在引用该文献时，会以 wikilink（sources/QC077）链接到本页；右侧「反向链接」面板自动列出所有引用它的词条。