Cavitation phenomenon happens when a liquid is subjected to fast changes in local pressure and speed, and the local liquid pressure becomes lower than its saturated vapor pressure. In most situations, it is not favorable and damaged equipment, and also reduce system efficiency and make noises and vibrations. There are several cavitation regimes depending on the cavitation number such as incipient cavitation, shear cavitation, sheet/cloud cavitation, and supercavitation. Diagnosing different phenomena such as shedding and collapsing in different cavitation regimes can help to find out what is happening in the considered fluid. Signal processing is one of the most important ways to analyze the fluctuations and changes of unsteady systems or processes. Therefore, it can be useful in detection of cavitation phenomena.

Many different experimental and numerical investigations have been done in cavitation flow over various cases such as blunt body, disk and sphere. Achenbach (Achenbach, 1972) measured the total drag experimentally, local static pressure and local skin friction distribution of the flow past sphere in a broad range of Reynolds numbers (5×104 0.9 and two bi-modal regimes for 0.9>? >0.675 and 0.675>? >0.3. They found that at high cavitation numbers (? >0.9), where cavity lengths are small, the breakup is driven by small-scale instabilities in the overlying boundary layer. But in cavitation numbers below 0.9, greater cavity lengths allow large-scale shedding to develop driven by coupled re-entrant jet formation and shockwave propagation.

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As expressed in the literature, the small amount of research on the wavelet transform of cases with cavitation especially in the sphere are available. Most of signal processing research on sphere cavitation are experimentally, and there is no wavelet analysis of numerical investigation. The experimental data in this model types were analyzed by capturing an image or acoustic data of the model. In this work, the exact values of flow properties points are captured during the time in different cavitation numbers. The data is analyzed by wavelet transform, and it is discussed on regimes and phenomena in flow depending on its frequencies.?