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首页> 外文期刊>International Journal for Numerical Methods in Fluids >Extension of the local domain-free discretization method to large eddy simulation of turbulent flows
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Extension of the local domain-free discretization method to large eddy simulation of turbulent flows

机译:局部域无离散化方法扩展到湍流流动的大涡模拟

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摘要

In this work, an immersed boundary method, called the local domain-free discretization (DFD) method, is extended to large eddy simulation (LES) of turbulent flows. The discrete form of partial differential equations at an interior node may involve some nodes outside the solution domain. The flow variables at these exterior dependent nodes are evaluated via linear extrapolation along the direction normal to the wall. To alleviate the requirement of mesh resolution in the near-wall region, a wall model based on the turbulence boundary layer equations is introduced. The wall shear stress yielded by the wall model and the no-penetration condition are enforced at the immersed boundary to evaluate the velocity components at an exterior dependent node. For turbulence closure, a dynamic subgrid scale (SGS) model is adopted and the Lagrangian averaging procedure is used to compute the model coefficient. The SGS eddy viscosity at an exterior dependent node is set to be equal to that at the outer layer. To maintain the mass conservation near the immersed boundary, a mass source/sink term is added into the continuity equation. Numerical experiments on relatively coarse meshes with stationary or moving solid boundaries have been conducted to verify the ability of the present LES-DFD method. The predicted results agree well with the published experimental or numerical data.
机译:在这项工作中,浸入的边界方法称为本地无域的离散化(DFD)方法(DFD)方法延伸到湍流的大涡流模拟(LES)。内部节点处的局部微分方程的离散形式可以涉及解决方案域之外的一些节点。这些外部依赖节点处的流量变量通过沿垂直于墙壁的方向的线性推断来评估。为了减轻近壁区域中网格分辨率的要求,引入了基于湍流边界层方程的壁模型。由墙体模型和无渗透条件产生的壁剪切应力在浸没边界处强制执行,以评估外部依赖节点处的速度分量。对于湍流闭合,采用动态底图(SGS)模型,并使用拉格朗日平均过程来计算模型系数。外部依赖节点处的SGS涡粘度设定为等于外层的粘度。为了保持浸没边界附近的质量保护,将质量源/沉降项添加到连续性方程中。已经进行了具有固定或移动固体边界的相对粗糙网格的数值实验以验证本发明的LES-DFD方法的能力。预测结果与已发表的实验或数值数据一致。

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