In many practical submerged objects, various types of cavitation such as bubble, sheet, and cloud cavitation occur according to flow conditions. In spite of numerous theoretical, numerical, and experimental studies, there are still many problems to be solved such as induced noise and damage potential due to cavitation. In this paper, an experimental investigation on coherent structures and induced noise characteristics of partial cavitation on a two-dimensional hydrofoil is presented. Experiments that focused on the dynamics of cavitation clouds were conducted in a cavitation tunnel. Using high-speed visualization, the series process consisting of inception, growth, and desinence of the partial cavity was investigated. The noise generated during the process was also measured, and the correlation with the cavity pattern was examined. The results show that the periodic behavior of cavitation clouds is directly reflected in the noise characteristics. In addition, the visualization of coherent structures within the sheet and cloud cavity provides a qualitative understanding of hairpin vortices and their packets, which play a dominant role in turbulent cavitating flows.

Model tests are performed in order to predict the noise level of a full ship and to control its noise signature. Localizing noise sources in the model test is therefore an important research subject along with measuring noise levels. In this paper, a noise localization method using a hydrophone array in a large cavitation tunnel is presented. The 45-channel hydrophone array was designed using a global optimization technique for noise measurement. A set of noise experiments was performed in the KRISO (Korea Research Institute of Ships & Ocean Engineering) large cavitation tunnel using scaled models, including a ship with a single propeller, a ship with twin propellers and an underwater vehicle. The incoherent broadband processors defined based on the Bartlett and the minimum variance (MV) processors were applied to the measured data. The results of data analysis and localization are presented in the paper. Finally, it is shown that the mechanical noise, as well as the propeller noise can be successfully localized using the proposed localization method.

The underwater acoustic noise radiated from the propellers of surface and underwater vehicles is characterized as high-frequency broadband noise modulated by low-frequency narrowband noise. Since the modulation is affected by propeller rotation speed, blade rate, and inception of cavitation, the measured propeller noise can be used to extract the information of propeller system and also to monitor its operating condition. In this research, we apply the cyclostationary signal processing technique to monitor vehicle propeller operation. In particular, we show that the rotation speed of a propeller can be estimated from cyclostationary characteristics such as the cyclic autocorrelation function and its Fourier counterpart - the cyclic spectrum. The cyclic spectrum shows the spectral support of the broadband modulating noise in addition to the modulation frequencies of the envelope signal. The effectiveness of the proposed technique is examined using two experimental data sets which are the scaled propeller model test data from a large cavitation tunnel and the ROV self-noise measurement data. The data analysis results show that the propeller rotation speed as well as the frequency band of the broadband noise can be identified using the cyclostationary statistics of acoustic noise data.