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An adaptive-gridding lattice Boltzmann method with linked-list data structure for two-dimensional viscous flows

机译:具有链表数据结构的二维粘性流的自适应网格格子Boltzmann方法

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An adaptive mesh refinement technique for lattice Boltzmann method (LBM) is proposed in this paper. It combines hierarchical linked-list data structure and the LBM calculation. Based on uniform meshes, the adaptive algorithm refines the meshes by constructing the linked-lists of nodes, cells and levels for mesh levels refined. To guarantee the stability of numerical scheme, quadratic bubble function for the nodal momentum is used to interpolate in the LBM calculation of different mesh levels. For the flows of relatively higher Re, large Eddy simulation (LES) is adopted to solve turbulence problems. Because of the use of adaptive technique, the computational time can be cut and accurate flow field information can be captured. OpenMP parallel for linked-list data structure is used to improve computational efficiency. To verify the present method, flows over circular cylinder at Re = 40, 300, 500, 1,000 and 3,900 and NACA0012 airfoil at Re = 105 for AOA = 4° are simulated.
机译:提出了一种适用于格子玻尔兹曼法(LBM)的自适应网格细化技术。它结合了分层链表数据结构和LBM计算。基于统一网格,自适应算法通过构造节点,像元和级别的链接列表来细化网格级别,从而对网格进行细化。为了保证数值格式的稳定性,在不同网格级别的LBM计算中使用了节点动量的二次气泡函数进行插值。对于相对较高Re的流动,采用大涡模拟(LES)解决湍流问题。由于使用了自适应技术,因此可以减少计算时间,并且可以捕获准确的流场信息。链表数据结构的OpenMP并行用于提高计算效率。为了验证本方法,模拟了在Re = 40、300、500、1,000和3,900时在圆柱上的流动以及对于AOA = 4°在Re = 105时的NACA0012翼型。

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