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首页> 外文期刊>Journal of Computational Physics >Customized data-driven RANS closures for bi-fidelity LES-RANS optimization
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Customized data-driven RANS closures for bi-fidelity LES-RANS optimization

机译：自定义数据驱动的Bi-Fidelity Les-Rans优化的rans封闭

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Multi-fidelity optimization methods promise a high-fidelity optimum at a cost only slightly greater than a low-fidelity optimization. This promise is seldom achieved in practice, due to the requirement that low-and high-fidelity models correlate well. In this article, we propose an efficient bi-fidelity shape optimization method for turbulent fluid-flow applications with Large-Eddy Simulation (LES) and Reynolds-averaged Navier-Stokes (RANS) as the high-and low-fidelity models within a hierarchical-Kriging surrogate modelling framework. Since the LES-RANS correlation is often poor, we use the full LES flow-field at a single point in the design space to derive a custom-tailored RANS closure model that reproduces the LES at that point. This is achieved with machine-learning techniques, specifically sparse regression to obtain high corrections of the turbulence anisotropy tensor and the production of turbulence kinetic energy as functions of the RANS mean flow. The LES-RANS correlation is dramatically improved throughout the design-space. We demonstrate the effectivity and efficiency of our method in a proof-of-concept shape optimization of the well-known periodic-hill case. Standard RANS models perform poorly in this case, whereas our method converges to the LES-optimum with only two LES samples. (C) 2021 The Author(s). Published by Elsevier Inc.
机译：多保真度优化方法承诺以略高于低保真度优化的成本实现高保真优化。由于要求低保真度模型和高保真度模型具有良好的相关性，这一承诺在实践中很少实现。在本文中，我们提出了一种有效的双保真度形状优化方法，用于在分层克里金代理建模框架内，以大涡模拟（LES）和雷诺平均Navier-Stokes（RANS）作为高保真和低保真模型的湍流流应用。由于LES-RANS相关性通常很差，我们使用设计空间中单个点的完整LES流场来推导定制的RANS闭合模型，该模型再现了该点的LES。这是通过机器学习技术实现的，特别是稀疏回归，以获得湍流各向异性张量的高校正，以及作为RANS平均流函数的湍流动能的产生。在整个设计空间中，LES-RANS相关性得到了显著改善。我们在著名的周期希尔情形的概念证明形状优化中证明了我们的方法的有效性和效率。在这种情况下，标准的RANS模型表现不佳，而我们的方法在只有两个LES样本的情况下收敛到LES最优。（c）2021作者。爱思唯尔公司出版。

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