A high-fidelity simulation method for moving bodies in flow fields based on dynamic Cartesian grids

With increasing demands for high-fidelity simulations of objects undergoing large-amplitude frequent motions in flow fields, traditional grid-based algorithms face challenges of insufficient computational efficiency and numerical instability. Although non-conformal Cartesian grids combined with immersed boundary methods (IBM) offer automation advantages, their results are still affected by spurious pressure oscillations during the simulation of such motions. To address this issue, we develop a mature dynamic Cartesian grid framework incorporating geometric-adaptive surface tracking and solution-adaptive mesh refinement algorithm. By employing an optimized IBM via the efficient high-fidelity ghost cell approach, integrated with fresh cells treatment for moving objects swept regions, automated high-efficiency simulations of flow fields with moving bodies are achieved. We conducted validations including static and dynamic cylinder flow tests, classic low-Mach number motions, and large-amplitude periodic movements. Through these comparisons with experimental data and high-fidelity computational solutions, we demonstrated that our proposed method significantly outperforms bilinear interpolation in both boundary reconstruction accuracy and overall robustness. This approach enables large-scale simulations of body moving-boundary problems and provides robust support for the rapid design of aerospace vehicles.