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EN
The propeller cavitation not only affects the propulsive efficiency of a ship but also can cause vibration and noise. Accurate predictions of propeller cavitation are crucial at the design stage. This paper investigates the feasibility of the Reynolds-averaged Navier–Stokes (RANS) method in predicting propeller cavitation in behind-hull conditions, focusing on four aspects: (i) grid sensitivity; (ii) the time step effect; (iii) the turbulence model effect; and (iv) ability to rank two slightly different propellers. The Schnerr-Sauer model is adopted as the cavitation model. A model test is conducted to validate the numerical results. Good agreement on the cavitation pattern is obtained between the model test and computational fluid dynamics. Two propellers are computed, which have similar geometry but slightly different pitch ratios. The results show that RANS is capable of correctly differentiating the cavitation patterns between the two propellers in terms of the occurrence of face cavitation and the extent of sheet cavitation; moreover, time step size is found to slightly affect sheet cavitation and has a significant impact on the survival of the tip vortex cavitation. It is also observed that grid refinement is crucial for capturing tip vortex cavitation and the two-equation turbulence models used – realizable k-ε and shear stress transport (SST) k-ω – yield similar cavitation results.
EN
This paper focuses on the effects of grid schemes and turbulence models on the CFD modelling of stirred tanks. The economical grid was determined by examining the dimensionless wall distance and the skewness of elements. The grid independency study ensured that the independency of numerical predictions. Also, three categories of turbulence models were compared for prediction of flow pattern. The grid sensitivity study highlighted that the quality of control volumes in the bulk and near the wall regions are significant for obtaining the consistent solutions. It was also found that for the prediction of velocity components and the turbulent quantity the RANS based models are more efficient.
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