A Method to Predict Cavitation Inception Using Large-Eddy Simulation and its Application to the Flow Past a Square Cylinder

[+] Author and Article Information
W. Wienken

Institute of Fluid Mechanics,Technische Universität Dresdenwienken@ism.mw.tu-dresden.de

J. Stiller

Institute for Aerospace Engineering, Technische Universität Dresden, D-01062 Dresden, Germany

A. Keller

 Technische Universität München, Versuchsanstalt für Wasserbau und Wasserwirtschaft, D-82432 Obernach/Walchensee, Germany

J. Fluids Eng 128(2), 316-325 (Mar 01, 2006) (10 pages) doi:10.1115/1.2170132 History:

A new method to predict traveling bubble cavitation inception is devised. The crux of the method consists in combining the enhanced predictive capabilities of large-eddy-simulation (LES) for flow computation with a simple but carefully designed stability criterion for the cavitation nuclei. For LES a second-order accurate finite element model based on the Galerkin/least-squares method with Runge-Kutta time integration is applied. The incoming nucleus’ spectrum is approximated by a Weibull distribution. Moreover, it is shown that under typical conditions the stability of the nuclei can be evaluated with an algebraic criterion emerging from the Rayleigh-Plesset equation. This criterion can be expressed as modified critical Thoma number and fits well into the LES approach. The method was applied to study cavitation inception in a flow past a square cylinder. A good agreement with experimental results was achieved. Furthermore, the principal advantage over statistical (time-averaged) methods could be clearly demonstrated, even though the spatial resolution and application of the LES were restricted by limited computational resources. As the latter keep on growing, a wider range of applications will become accessible methods for cavitation prediction based on algebraic stability criteria combined with LES.

Copyright © 2006 by American Society of Mechanical Engineers
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Figure 1

Turbulent channel flow at Re=180

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Figure 2

Cavitation inception

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Figure 3

Nucleus distribution and its approximation by the Weibull and Normal distribution

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Figure 4

Computational domain for the LES

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Figure 5

Section of the grid used for the unsteady RANS computation (Each fifth gridline is shown)

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Figure 6

Velocity and pressure distribution, k-ε model

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Figure 7

Velocity and pressure distribution, RSM model

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Figure 8

Velocity and pressure distribution, LES

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Figure 9

(a) Pressure probes on the cylinder surface (b) Mean pressure distribution on cylinder surface (▴; Experiment; –: LES; – –: RSM; – ∙ –: κ–ε)

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Figure 10

Velocity profiles (∎; Experiment; –: LES; ▴ RSM; ●: κ–ε)

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Figure 11

Isobar surface; LES

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Figure 12

Isobar surface; RSM



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