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Large Eddy Simulation based on the Residual-based Variational Multiscale Method and Lagrangian Dynamic Smagorinsky Model

机译:基于残差变分多尺度方法和拉格朗日动态Smagorinsky模型的大型涡模拟

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In this work, we focus on large eddy simulation (LES) using the finite element method. The use of finite elements is beneficial due to their flexibility for complex problems (e.g., complex geometry, high-order discretizations, adaptivity, parallelization). In order to achieve this, we combine aspects of the residual-based variational multiscale (RBVMS) approach, which provides the basis for stabilized finite element methods, and the dynamic Smagorinsky eddy-viscosity model. Far the Smagorinsky model, we employ two dynamic procedures: one that employs spatial averaging over homogeneous directions (which is applicable to turbulent flows with statistical homogeneity) and the other is based on Lagrangian, or fluid pathline, averaging (which is applicable to inhomogeneous turbulent flows). In this study, we consider three models. One model is solely based on the RBVMS approach. The other two models combine the cross-stress terms due to the RBVMS model with the dynamic Smagorinsky eddy-viscosity model for the Reynolds stress terms. In the two combined models, we employ a different type of averaging in each model. We present results using these models on two cases: turbulent channel flow (Re_t= 590) and flow over a cylinder (Re_D = 3,900). For the turbulent channel flow, all models provide similar predictions while for the flow over a cylinder, the combined model provides a better prediction.
机译:在这项工作中,我们专注于使用有限元方法的大型涡模拟(LES)。有限元的使用是有益的,因为它们对于复杂的问题(例如,复杂的几何形状,高阶离散化,适应性,并行化)具有灵活性。为了实现这一目标,我们结合了基于残差的可变多尺度(RBVMS)方法的各个方面,该方法为稳定有限元方法和动态Smagorinsky涡粘性模型提供了基础。在Smagorinsky模型的远处,我们采用了两种动态过程:一种在同构方向上进行空间平均(适用于具有统计均匀性的湍流),另一种基于Lagrangian或流体路径平均(适用于非均匀湍流)流量)。在这项研究中,我们考虑了三种模型。一种模型仅基于RBVMS方法。另外两个模型将RBVMS模型产生的交叉应力项与雷诺应力项的动态Smagorinsky涡粘性模型结合在一起。在两个组合模型中,我们在每个模型中采用不同类型的平均。我们在以下两种情况下使用这些模型给出了结果:湍流通道流量(Re_t = 590)和流经圆柱的流量(Re_D = 3,900)。对于湍流通道流,所有模型都提供相似的预测,而对于圆柱体上的流,组合模型则提供更好的预测。

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