A sharp boundary ghost-node method (GNM) is developed to solve the moving boundary fluid flow in a meshless local radial basis function (LRBF) framework. The background Euler fluid node is the mesh-less scattered node based on LRBF rather than the conventional Cartesian grid or unstructured mesh. The present approach (LRBF-GNM) can flexibly treat the steady boundary with the body-fitted nodes and tackle the moving boundary using the ghost-node method. The key idea of GNM is to project the information of fluid nodes into the ghost nodes by considering the boundary conditions of immersed boundary on Lagrangian nodes, and the influence of immersed boundary on the fluid can be explicitly added during the projection. There are some free parameters that should be determined before computing the virtual forcing source term acted only on the ghost nodes. The distribution of the ghost nodes, the projection strategy and the distribution of the image nodes should be treated carefully to balance the penetrability of streamlines over the immersed boundary, the numerical stability and the thickness of the diffusion boundary. A definition of sharp boundary is introduced to estimate the influences of the above free parameters on the immersed boundary. According to the numerical tests for a static boundary, the optimal parameters for GNM to precisely treat the immersed boundary are summarized. The fluid flow over steady and moving rigid boundaries and the vortex-induced vibration (VIV) are conducted and their solutions agree well with published numerical solutions and experimental measurements.

中文翻译：

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一种通用的锐边界鬼节点方法，用于移动具有无网格节点分布的刚性边界流体流

开发了锐边界鬼节点法（GNM）来求解无网格局部径向基函数（LRBF）框架中的移动边界流体流动。背景欧拉流体节点是基于LRBF的无网格离散节点，而不是传统的笛卡尔网格或非结构化网格。本方法（LRBF-GNM）可以灵活地使用贴体节点处理稳定边界，并使用鬼节点方法处理移动边界。 GNM的核心思想是考虑拉格朗日节点上浸没边界的边界条件，将流体节点的信息投影到鬼节点中，并且在投影过程中可以显式地添加浸没边界对流体的影响。在计算仅作用于幽灵节点的虚拟力源项之前，应确定一些自由参数。应仔细处理鬼节点的分布、投影策略和图像节点的分布，以平衡浸没边界上流线的穿透性、数值稳定性和扩散边界的厚度。引入尖锐边界的定义来估计上述自由参数对浸入边界的影响。根据静态边界的数值试验，总结出GNM精确处理浸没边界的最佳参数。进行了稳定和移动刚性边界上的流体流动以及涡激振动 (VIV)，其解与已发表的数值解和实验测量结果非常吻合。