An alternative Vorticity based Adaptive Mesh Refinement (V-AMR) technique for tip vortex cavitation modelling of propellers using CFD methods

This study focuses on the investigation of cavitating flow around the benchmark INSEAN E779A model propeller with the main aim of further improving the computational efficiency of the tip vortex cavitation (TVC) modelling by using a commercial CFD solver. Also, the effects of various key computational parameters including, numerical modelling, grid size, timestep, water quality and boundary layer resolution, on the TVC formation and its extension in the propeller slipstream are investigated systematically. The numerical simulations are conducted in uniform and open water conditions using RANS, DES and LES solvers implemented in the commercial CFD code, Start CCM+. In order to achieve the aim of the study, an alternative and new Vorticity-based Adaptive Mesh Refinement (V-AMR) technique is introduced for enhanced modelling of the TVC on the blades and downstream. For the CFD modelling of cavitation, the Schneer Sauer cavitation model based on the reduced Rayleigh Plesset equation is used for the sheet, tip and hub vortex cavitation. The hydrodynamic results and cavity patterns are validated with the experimental data. The results show that the application of the V-AMR technique further improves the representation of the TVC with minimal increase in computational cost. However, the eddy viscosity at the propeller blade tips increases with applying the V-AMR technique using the RANS solver due to its inherent modelling errors for the solution of the flow inside the tip vortex. This consequently results in an insufficient extension of TVC in the propeller slipstream compared to the predictions by the DES and LES based numerical solvers. Also, the evolution of the TVC is found to be sensitive to the boundary layer resolution when the standard RANS solver is used. The study will help to widen further applications of the CFD methods involving TVC, particularly for propeller induced underwater noise prediction and analysis.