A Data Acquisition System to Detect Bubble Collapse Time and Pressure Losses in Water Cavitation

Introduction

The cavitation phenomenon constitutes a research field of increasing interest over the last years, as attested by several works (Abdel-Maksoud et al., in press; Arndt et al., 1999; Brennen, 1995; Payri et al., 2009; Schneider et al., 2007; Suh & Lee, 2008; Tseng & Shyy, 2010; Vortmann et al., 2003) dealing with it in the attempt to underline several aspects, problems and characteristics. This phenomenon interests a wide range of machines, from internal combustion engines to turbines and pumps of all sizes. It affects negatively the performance of hydraulic machines and may cause material erosion.

In hydraulic machines, cavitation may occur along stationary parts and along moving blades. Liquid fuel and oxidizer turbo-pumps for rocket engines are usually operated under cavitating condition with quite high rotational speed to attain high-pressure performance with their minimum size and weight. Under such conditions, inducers easily fall into unstable operations with severe instabilities such as cavitation surge and rotating cavitation.

Cavitation occurs when, due to a decrease in local pressure up to the vapor pressure, cavities filled with water vapor are formed; once the vapor bubbles reach higher pressure regions on their paths they collapse by an implosion. When cavitation bubbles collapse near the surface of a solid in contact with the liquid, tiny pits or craters damage the solid surface. A problem related to the transport and manipulation of multiphase fluids, as cavitating flow, concerns the individuation of the different characteristic flow patterns and its strong influence on important operation parameters of the machinery.

Over the past few years many researchers have made an effort to carry out data acquisition systems to observe, to display and monitoring the cavitating flow in order to understand its behavior, formation mechanisms of cloud cavitation, and stability of cavitation characteristics. Although prediction based on phenomenological models is fairly well developed, on-line monitoring of cavitating flow patterns is still a limiting technological problem. Flow visualization is an important tool in fluid dynamics research and it has been used extensively in the different fields as engineering, physics, medical science, etc. Visual observations and photographic recordings have been performed in many practical situations as experimental means to provide a ‘‘measure’’ of the cavitation phenomenon. Cavitation structures behave stochastically both in time and space, with a very rapid change within microseconds. In (Ezddin et al., 2008) a visualization of a highly submerged cavitating water jet was done by high-speed camera photography in order to study and understand the jet structure and the behavior of cloud cavitation within time and space. Periodical shedding and discharging of cavitation clouds have been analyzed and the corresponding frequency was determined by cloud shape analysis. Also Soyama et al. (1995) attempted to clarify the jet structure and the behavior of severely erosive cavitation clouds around a high-speed submerged water jet, using a high-speed movie camera with a framing rate of ten thousand frames.