Adaptive mesh refinement method based investigation of the interaction between shock wave, boundary layer, and tip vortex in a transonic compressor

Shock wave and tip leakage are important flow features at small length scales. These flow phenomena and their interactions play important roles in the performance of modern transonic fans and compressors. In most numerical predictions of these features, mesh convergence studies are conducted using overall performance data as criteria. However, less effort is made in assessing the quality of the predicted small-scale features using a mesh that yields a fairly accurate overall performance. In this work, this problem is addressed using the adaptive mesh refinement (AMR) method, which automatically refines the local mesh and provides very high resolution for the small-scale flow feature, at much less cost compared with globally refining the mesh. An accurate and robust AMR system suitable for turbomachinery applications is developed in this work and the widely studied NASA Rotor-37 case is investigated using the current AMR method. The complex interactions between the shock wave and the boundary layer, as well as those between the shock wave and the tip vortex, are accurately captured by AMR with a very high local grid resolution, and the flow mechanisms are analyzed in detail. The baseline mesh, which is considered to be acceptable according to the commonly used mesh convergence study, is unable to capture the detailed interaction between the shock wave and the boundary layer. Moreover, it falsely predicts the tip leakage vortex breakdown, which is a consequence of inadequate resolution in the tip region. Current work highlights the importance of a careful check of the mesh convergence, if small-scale features are the primary concern. The AMR method developed in this work successfully captures the flow details in the transonic compressor in an automatic fashion, and has been verified to be efficient compared with the globally mesh refinement or manually mesh regeneration.