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一种基于动态混合RANS-LES的大涡模拟近壁方法。

A Near-Wall Methodology for Large-Eddy Simulation Based on Dynamic Hybrid RANS-LES.

作者信息

Tullis Michael, Walters D Keith

机构信息

Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR 72701, USA.

出版信息

Entropy (Basel). 2024 Dec 14;26(12):1095. doi: 10.3390/e26121095.

Abstract

Attempts to mitigate the computational cost of fully resolved large-eddy simulation (LES) in the near-wall region include both the hybrid Reynolds-averaged Navier-Stokes/LES (HRL) and wall-modeled LES (WMLES) approaches. This paper presents an LES wall treatment method that combines key attributes of the two, in which the boundary layer mesh is sized in the streamwise and spanwise directions comparable to WMLES, and the wall-normal mesh is comparable to a RANS simulation without wall functions. A mixing length model is used to prescribe an eddy viscosity in the near-wall region, with the mixing length scale limited based on local mesh size. The RANS and LES regions are smoothly blended using the dynamic hybrid RANS-LES (DHRL) framework. The results are presented for the turbulent channel flow at two Reynolds numbers, and comparison to the DNS results shows that the mean and fluctuating quantities are reasonably well predicted with no apparent log-layer mismatch. A detailed near-wall meshing strategy for the proposed method is presented, and estimates indicate that it can be implemented with approximately twice the number of grid points as traditional WMLES, while avoiding the difficulties associated with analytical or numerical wall functions and modified wall boundary conditions.

摘要

减轻近壁区域完全解析大涡模拟(LES)计算成本的尝试包括混合雷诺平均纳维-斯托克斯/LES(HRL)和壁面模型LES(WMLES)方法。本文提出了一种结合这两种方法关键属性的LES壁面处理方法,其中边界层网格在流向和展向方向上的尺寸与WMLES相当,而壁法向网格与没有壁面函数的雷诺平均纳维-斯托克斯模拟相当。使用混合长度模型规定近壁区域的涡粘性,混合长度尺度基于局部网格尺寸进行限制。使用动态混合雷诺平均纳维-斯托克斯-LES(DHRL)框架将雷诺平均纳维-斯托克斯和LES区域平滑混合。给出了两个雷诺数下湍流槽道流的结果,与直接数值模拟结果的比较表明,平均量和脉动分量得到了合理的预测,没有明显的对数层不匹配。给出了所提方法的详细近壁网格划分策略,估计表明它可以用比传统WMLES大约两倍的网格点数来实现,同时避免了与解析或数值壁面函数以及修正壁面边界条件相关的困难。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e142/11675691/073967a41241/entropy-26-01095-g001.jpg

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