Recent observations of the geometries of growing and collapsing bubbles over axisymmetric headforms have revealed the complexity of the "microfluidmechanics" associated with these flows (Hamilton et al., 1982, Briancon Marjollet and Franc, 1990, Ceccio and Brennen, 1991). Among the complex features observed were bubble to bubble interaction, cavitation noise generation and bubble interaction with the boundary layer which leads to the shearing of the underside of the bubble and alters the collapsing process. All of these previous tests were performed on small headform sizes. The focus of this research is to determine the dynamics governing the growth and collapse of traveling bubbles and to analyze the scaling effects due to variations in geometry size, Reynolds number and cavitation number. For this effect, cavitating flows over Schiebe headforms of different sizes (5.08cm, 25.4cm and 50.8cm in diameter) were studied in the David Taylor Large Cavitation Channel (LCC). This thesis presents the scaling effects captured on high-speed film and electrode sensors as well the noise signals generated during the collapse of the cavities. The influence of each of these parameters on the dynamics involved in the growth and collapse phases of the traveling bubble are presented, along with the acoustical impulse produced during the collapse of the bubble.
In order to model and analyze the dynamics of the three-dimensional bubble deformation in the presence of the pressure field around the Schiebe headform, an unsteady numerical code using traveling sources has been developed. This thesis presents calculations of the interaction between the irrotational flow outside the boundary layer of the headform and individual traveling bubbles. An error estimation of the method and comparisons with the LCC experiments are presented. This method is shown to predict some of the features of three-dimensional bubble growth and collapse dynamics remarkably well. Furthermore, analysis of these computations allow a better understanding bubble interaction and event rate prediction
