Cell-based hybrid adaptive mesh refinement algorithm for immersed boundary method

This article proposes a hybrid adaptive mesh refinement (AMR) algorithm for the immersed boundary method (IBM) combination, and the AMR code is split into mesh and physics codes to optimize each part individually. The uniform parent grid solver is used for AMR grids by constructing hanging cells for the high-order extension. The restrictive spatial refinement in a fully threaded tree (FTT) data structure is explored, and a simplified stencil search algorithm for hanging cells construction is introduced, including current and following child AMR level cells, if any. The proposed AMR method was applied to IBM, which offers flexibility in treating complex geometries in the Cartesian grid, leading to algorithmic simplicity and computational efficiency. Local near immersed boundary refinement is proposed to avoid complex and computationally expensive IBM and AMR algorithms near the solid bodies. Finally, a high-order flux scheme extension at AMR level transition cells and the proposed method's applicability for steady and transient flows are demonstrated. Besides state and flux variables storage, the proposed hybrid AMR method's additional cost is 4.5 words/cell instead of 3.5 words/cell for 2D and 3.75 words/cell instead of 2.75 words/cell for 3D compared to the conventional FTT-based AMR method. By comparing the uniform grid, the AMR final grid distribution shows that the optimal cell distribution based on flow physics reduces the cells required by more than 40% to resolve the complex flow features in less computational time. The enhanced performance and accuracy of the proposed AMR method in resolving different scale flow features are validated through benchmark problems.