<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Feasibility Study of Rans in Predicting Propeller Cavitation in Behind-Hull Conditions

Polish Maritime Research

This work is licensed under the Creative Commons Attribution 4.0 International License.

The propeller cavitation not only affects the propulsive efficiency of a ship but also can cause vibration and noise. Accurate predictions of propeller...

Feasibility Study of Rans in Predicting Propeller Cavitation in Behind-Hull Conditions

Publié en ligne: 24 déc. 2020

Pages: 26 - 35

DOI: https://doi.org/10.2478/pomr-2020-0063

Mots clés
RANS, Computational Fluid Dynamics (CFD), propeller cavitation, turbulence model, grid sensitivity

© 2020 Yuxin Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Feasibility Study of Rans in Predicting Propeller Cavitation in Behind-Hull Conditions

Publié en ligne: 24 déc. 2020

Pages: 26 - 35

DOI: https://doi.org/10.2478/pomr-2020-0063

Mots clésRANS, Computational Fluid Dynamics (CFD), propeller cavitation, turbulence model, grid sensitivity

© 2020 Yuxin Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
RANS, Computational Fluid Dynamics (CFD), propeller cavitation, turbulence model, grid sensitivity