Wall-modeled large eddy simulation of 90° bent pipe flows with/without particles: A comparative study

雷诺平均Navier-Stokes方程 机械 湍流 大涡模拟 雷诺数 物理 直接数值模拟 流量(数学) 颗粒流 湍流模型 压缩性 分离涡模拟
作者
Meysam Fazeli,Homayoun Emdad,Mohammad Mehdi Alishahi,Saleh Rezaeiravesh
出处
期刊:International Journal of Heat and Fluid Flow [Elsevier BV]
卷期号:105: 109268-109268
标识
DOI:10.1016/j.ijheatfluidflow.2023.109268
摘要

Wall-modeled large eddy simulation (WMLES) has been proven to be a cost-effective approach capable of resolving turbulence up to certain resolutions. Among the simplest wall models used are the equilibrium wall models, assuming the pressure gradient and convective terms balance out in the momentum equations. There is a lack of studies to assess the performance of these standard wall models in internal turbulent flows including separation regions with/without particles. Regarding this research gap, we have conducted WMLES of incompressible turbulent flows, to the authors’ knowledge, for the first time, in 90° bent pipes with and without particles using an algebraic equilibrium wall model (Spalding’s function). A pipe flow simulation was conducted to confirm the simulation setup and assess the sensitivity with respect to the modeling parameters. In each case, comparisons are made with experiment or direct numerical simulation (DNS), and depending on the case, with other existing simulation methods in the literature: WMLES, standard (wall-resolving) LES, and Reynolds stress model (RSM) for Reynolds-averaged Navier-Stokes (RANS) simulations. Despite the controversy on the performance of equilibrium wall models in nonequilibrium flows, our results show acceptable accuracy of this type of wall models. Specifically in the bent pipe flow with particles, WMLES succeeded in predicting particle deposition efficiency at Stokes numbers greater than 0.5, but obtained less accurate results for smaller Stokes numbers. The WMLES errors were, however, on par with those of the standard LES employed with a tenfold higher grid cell count. Improved results would be expected if combined with auxiliary mechanisms such as stochastic models.
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