Twenty-Fourth Symposium on Naval Hydrodynamics (2003) / Chapter Skim
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Influence of Acoustic Interaction in Noise Generating Cavitation
Influence of Acoustic Interaction in Noise Generating Cavitation
Pages 881-897
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From page 881... ...
The numerical study concerns acoustic interaction between two spherical cavities. Monte Carlo simulations with random input data were carried out.
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A key aspect was also the pressure can be the dominating pressure forcing the chance to make extensive Monte Carlo simulations collapse. using random parameter values for the model.
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The pressure generated by the cavities is calculated at the distance of ri from the centre of each caviW. Artificial damping To obtain reasonable pressure pulses, needed for a meaningful simulation of the interaction effects, some additional damping was needed.
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An effect of deformation is that it can be expected to result in more viscous dissipation and thus less volume acceleration than for spherical cavities. This latter effect is simulated in the computations by the artificial damping term, Eqn.
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This is demonstrated in Hallander (1999~. The time history of the externally applied pressure is also affected by the time variation of the inflow and, for an oscillating foil, also by the oscillation of the foil (Schoon 2000~.
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The average power density spectrum G69 for each example (in the Monte Carlo simulations) was estimated by the following process.
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These distribution parameters were then used to generate random input data to Monte Carlo simulations with the system of two spherical cavities, Section 6.2. An improved experimental study In cooperation with another project (Schoon 2000)
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An extreme example is that of downstream moving collapses at highly unsteady cavitation, see Fig. 7, where a fast collapsing glossy sheet generates a large number of surrounding cavities (Schoon and Bark 1998b)
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Examples of parameters varied included the space between the cavities, the initial gas pressure and the artificial damping. The initial gas pressure within the cavities was observed to strongly affect the behaviour of the cavities and the pressure pulses generated.
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In a new series of simulations (Hallander 1999) , distribution parameters estimated from two sets of new experimental conditions were used to produce 2000 sets of random input data.
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1 0.05 _ O ~ 2 4 6 ~ 0 Rn1 [mm] Figure 11: Distributions of random input data from simulations of unsteady cavitation.
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When comparing some time series of pressure signals, the simulated peak pressure amplitudes appear higher than measured ones. However, after decimation of the simulated signal (to a sampling frequency of similar size)
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Here' the energy spectral density of the sum of the pulses, when the interaction is taken into account, increases considerably at the highest frequencies, while the levels at low frequencies mostly remain relatively unchanged. In real cavitation processes, involving many cavities that behave partly randomly, the trends indicated above still apply, however the details are much influenced by the statistical properties of the cavitation processes.
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9 CONCLUSIONS 1. A main engineering conclusion drawn from the numerical simulations is that, provided the levels at the very highest frequencies are disregarded, the acoustic interaction between medium sized dominating structures does not make much difference.
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When an accurate estimate of the noise at the very highest frequencies is desired, the numerical simulations indicate that an accurate simulation of both the acoustic interaction and the statistical properties of cloud cavitation is required. As the time displacements between cavities are very critical, this problem is very difficult to solve in numerical simulations.
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& Bark, G "Some observations of violent collapses of sheet cavities and subsequent cloud cavitation on a foil in unsteady flow." ASME Fluids Engineering Division Summer Meeting, Washington, DC, USA.
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Since the high-speed film recordings were limited to 1.3 s, the number of samples used for the data fitting was small. The estimated distribution parameters were finally used to generate random input data to the Monte Carlo simulations, Figures 11 and 12.
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