Robust dynamic adaptation of the Smagorinsky model based on a sub-grid activity sensor
Robust dynamic adaptation of the Smagorinsky model based on a sub-grid activity sensor
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DOI：
10.1063/5.0032117
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发表时间：
2021-01
影响因子：
4.6
通讯作者：
J. Hasslberger;L. Engelmann;Achim Kempf;M. Klein
中科院分区：
文献类型：
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作者：
J. Hasslberger;L. Engelmann;Achim Kempf;M. Klein
本研究涉及的应用程序的亚网格活动传感器的涡粘性类型的基础模式的背景下，大涡模拟（LES）。相干结构函数被用来建立这种传感器与显式测试滤波相结合。所提出的传感器具有两个主要优点：第一，它衰减过渡流的基础模型的亚网格尺度耗散。根据当地条件，传感器基本上是在零和标准Smagorinsky模型产生的值之间混合涡流粘度。其次，传感器纠正了标准Smagorinsky模型的不正确的近壁缩放。传感器的应用不需要平均（在均匀方向上），并且易于实现，并且额外的计算成本微不足道。为了评估模型，三种不同的配置已被检查：层流到湍流过渡的泰勒-格林涡，壁占主导地位的通道流，和自由平面射流，包括被动标量混合。基于后验LES，已经发现，新的传感器增强的Smagorinsky模型往往优于文献中建立的涡粘性模型，如标准Smagorinsky模型和sigma模型，以及没有显式子网格模型的LES。
The present study deals with the application of a sub-grid activity sensor to an eddy viscosity type base model in the context of Large Eddy Simulation (LES). The coherent structure function is used to build this sensor in combination with explicit test filtering. The proposed sensor features two main advantages: First, it attenuates the sub-grid scale dissipation of the base model for transitional flows. Depending on local conditions, the sensor is essentially blending the eddy viscosity between zero and the value resulting from the standard Smagorinsky model. Second, the sensor rectifies the incorrect near-wall scaling of the standard Smagorinsky model. Application of the sensor requires no averaging (in the homogeneous direction) and is easy to implement, and the additional computational cost is insignificant. In order to assess the model, three different configurations have been examined: laminar-to-turbulent transition in the Taylor–Green vortex, wall-dominated channel flows, and a free planar jet flow including passive scalar mixing. Based on a posteriori LES, it has been found that the new sensor-enhanced Smagorinsky model often outperforms established eddy viscosity models from the literature, such as the standard Smagorinsky model and the sigma model, as well as the LES without the explicit sub-grid model.