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Research Papers: Flows in Complex Systems

On the Applicability of Cavitation Erosion Risk Models With a URANS Solver

[+] Author and Article Information
Themistoklis Melissaris

Department of Propulsion Performance,
Wartsila Netherlands BV,
Drunen 5151 DM, The Netherlands;
Department of Maritime and Transport
Technology,
Delft University of Technology,
Delft 6700 AA, The Netherlands
e-mail: themis.melissaris@wartsila.com

Norbert Bulten

Wartsila Netherlands BV,
Drunen 5151 DM, The Netherlands
e-mail: norbert.bulten@wartsila.com

Tom J. C. van Terwisga

Department of Maritime and Transport
Technology,
Delft University of Technology,
Delft 6700 AA, The Netherlands;
Resistance & Propulsion,
Maritime Research Institute
Netherlands (MARIN),
Wageningen 6700 AA, The Netherlands
e-mail: t.v.terwisga@marin.nl

1Corresponding author.

Contributed by the Fluids Engineering Division of ASME for publication in the JOURNAL OF FLUIDS ENGINEERING. Manuscript received November 27, 2018; final manuscript received March 12, 2019; published online April 25, 2019. Assoc. Editor: Matevz Dular.

J. Fluids Eng 141(10), 101104 (Apr 25, 2019) (15 pages) Paper No: FE-18-1799; doi: 10.1115/1.4043169 History: Received November 27, 2018; Revised March 12, 2019

In the maritime industry, cavitation erosion prediction becomes more and more critical, as the requirements for more efficient propellers increase. Model testing is yet the most typical way a propeller designer can, nowadays, get an estimation of the erosion risk on the propeller blades. However, cavitation erosion prediction using computational fluid dynamics (CFD) can possibly provide more information than a model test. In the present work, we review erosion risk models that can be used in conjunction with a multiphase unsteady Reynolds‐averaged Navier–Stokes (URANS) solver. Three different approaches have been evaluated, and we conclude that the energy balance approach, where it is assumed that the potential energy contained in a vapor structure is proportional to the volume of the structure, and the pressure difference between the surrounding pressure and the pressure within the structure, provides the best framework for erosion risk assessment. Based on this framework, the model used in this study is tested on the Delft Twist 11 hydrofoil, using a URANS method, and is validated against experimental observations. The predicted impact distribution agrees well with the damage pattern obtained from paint test. The model shows great potential for future use. Nevertheless, it should further be validated against full scale data, followed by an extended investigation on the effect of the driving pressure that leads to the collapse.

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Figures

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Fig. 1

Energy balance approach showing the transition of the energy contained in the initial vapor cavity to the material surface [30]

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Fig. 2

Geometry of the Delft Twist 11 hydrofoil

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Fig. 10

Time history of the total vapor volume (top) and its spectral analysis (bottom)

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Fig. 11

Time history of the pressure at the observation point P1 (top) and its spectral analysis (bottom)

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Fig. 3

Description of the computational domain and the boundary conditions

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Fig. 4

Grid refinement levels around the foil

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Fig. 5

Convergence of the lift force with the grid refinement ratio. Impression of the numerical uncertainty estimates.

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Fig. 6

The different planes where the pressure distribution is compared with experimental data

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Fig. 7

Pressure distribution at the 50% of the span

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Fig. 8

Pressure distribution at the 40% of the span

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Fig. 9

Pressure distribution at the 30% of the span

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Fig. 12

Observation point P1, close to the surface

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Fig. 13

TST results for the total vapor volume, after removing a section at the beginning

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Fig. 14

TST results for the pressure signal at the observation point P1, after removing a section at the beginning

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Fig. 15

Convergence of the shedding frequency with the grid refinement ratio. Impression of the numerical uncertainty estimates for different reference courant numbers.

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Fig. 16

Convergence of the lift force with the grid refinement ratio. Impression of the numerical uncertainty estimates for different reference courant numbers.

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Fig. 17

Convergence of the drag force with the grid refinement ratio. Impression of the numerical uncertainty estimates for different reference courant numbers.

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Fig. 18

Time-averaged pressure distribution at the midspan for different grid densities. Comparison with experimental measurements.

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Fig. 19

Qualitative comparison between the particle image velocimetry imaging and the simulated shedding cycle, visualized as an isosurface of aν=0.01

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Fig. 20

Flow aggressiveness potential power density, as a function of the flow velocity

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Fig. 21

Pressure signal and the time-averaged pressure evolution at the observation point

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Fig. 22

Time-averaged pressure field pt¯ after it has converged in each volume cell, showing a distinct pressure recovery region

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Fig. 23

Instantaneous pressure field pi for a random instant of time

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Fig. 24

Accumulated energy on the surface (a) for the instantaneous pressure field pi and (b) the time-averaged pressure fieldpt¯

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Fig. 25

Surface impact distribution for different values of the parameter n, for each aggressiveness indicator ⟨e˙S⟩eS′ (top) and ⟨e˙S⟩f′ (bottom)

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Fig. 26

Comparison between the surface impact distributions obtained from (a) 3 h of paint test, (b) the indicator ⟨e˙S⟩eS′, and (c) the indicator ⟨e˙S⟩f′. The CFD solution, n = 6 for both indicators, has been plotted on the actual surface to compare the eroded regions with the paint test.

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