Towards predicting cavitation noise using scale-resolving simulations: The importance of inflow turbulence

There is increasing attention for the effects of anthropogenic underwater radiated noise (URN) on marine fauna. This is expected to lead to regulations with respect to the maximum permitted sound emissions of ships. It is known that cavitating tip vortices, generated by ship propellers, are some of the key contributors to URN. Consequently, there is a need to evaluate propeller designs with respect to noise generation in a design stage. Computational fluid dynamics (CFD) has the potential to offer detailed insights into cavitating vortex dynamics and noise sources, at a reasonable cost. URN can be efficiently estimated using CFD in combination with an acoustic analogy. In order to use such predictions in a design process, it is essential to understand and quantify the errors associated with the numerical predictions of noise sources. This thesis investigates the reliability of such evaluations and aims to reduce occurring modelling errors.

To compute noise sources, it is necessary to simulate cavitation dynamics using scale-resolving simulations (SRS). Here, part of the turbulence kinetic energy spectrum is resolved in space and time, as opposed to being modelled using Reynolds averaged Navier-Stokes (RANS). The SRS method of choice in this work is the partially averaged Navier-Stokes (PANS) method. Bridging models, such as PANS, exhibit a smooth transition and absence of commutation errors between RANS and large eddy simulation (LES) zones, in contrast to hybrid models such as detached eddy simulation (DES). The formulation allows for a theoretical decoupling of the discretisation and modelling errors, thereby enabling verification and validation processes.

PANS allows the user to select the ratios of resolved-to-total turbulence kinetic energy and dissipation (rate). Appropriate settings and methods to estimate these settings a priori are investigated. Furthermore, a new PANS closure is developed, which offers improved convergence behaviour compared to more commonly used models, and is better suited to application for multiphase flows. It has been shown repeatedly in literature that SRS should be accompanied by physical inflow boundary conditions, where time-varying fluctuations, resembling turbulence, should be inserted upstream of the object of interest, to prevent laminar solutions. However, from literature it is clear that for maritime applications this is often neglected. To the knowledge of the author, there is no previous application of such an inflow in combination with cavitation. In this PhD thesis, a synthetic inflow turbulence generator (ITG) is implemented, and tested for several test cases in wetted and cavitating conditions. For these cases, the numerical errors, consisting of discretisation, iterative and statistical errors are evaluated.

Firstly, the results when using the ITG are compared against recycling flow results for a turbulent channel flow, using different SRS methods. It was shown that the ITG can deliver a resolved turbulent inflow at lower computational cost. Secondly, the effect of neglecting such an inflow was tested for the Delft Twist 11 hydrofoil, where it was shown that simulating such a flow with a low ratio of resolved-to-total turbulence kinetic energy can lead to flow separation at the wing leading edge. This is in contrast to experimentally observed behaviour. The inclusion of the ITG can reduce this modelling error, although the sheet cavity dynamics remain largely unaffected. Finally, an elliptical wing with a cavitating tip vortex is simulated. The observed vortex dynamics are compared against a semi-analytical model from literature. To obtain vortex dynamics, the ITG was shown to be necessary. The far-field noise generated by the vortex is quantified and related to the cavity dynamics.

Some of the main contributions of this research are improved insight in the use of SRS in cavitating conditions, in simulating cavity dynamics and in using an ITG to obtain flow fields representative of experimental conditions. In this way it has enhanced our understanding of the ability and limitations in the prediction of acoustic sources due to cavitation. To improve predictions of cavitation dynamics it is recommended to address the cavitation model and the method which describes the cavity interface, to reduce the discrepancy in average cavity size between simulations and experimental observations.