Pattern recognition of two-phase liquid–gas flow by discriminant analysis applied to accelerometric signals

Abstract

Optimizing heat transfer in the evaporator tubes is essential for obtaining optimal coefficients. Deviation from ideal nucleated boiling configurations can lead to rapid transitions to film boiling conditions, compromising the exchange coefficients. Detecting critical bubble flow conditions is invaluable in preventing thermal overloads and safeguarding operational integrity. The aim is to detect the type of bubble motion established in the ducts using non-intrusive techniques. To achieve this aim, accelerometric devices detect the vibrations induced by the passage of bubbles. The bubble's movement is also recorded contextually by a fast video camera capable of detecting images through a vertical transparent quartz duct. The accelerometric data, processed using discriminant analysis theory and then associated with the recorded images, give a good relationship, more than 95%, between the motion of the bubbles and their behavior characteristics. The latter in this work is represented on the plane of the phases.