An efficient method for processing high-speed infrared images of nucleate boiling on thin heaters at low heat flux

Abstract

In recent years, there have been many studies on boiling with high-speed infrared cameras. The infrared image contains not only the information of temperature field, but also the information of bubble dynamics. The identification of different heat transfer modes is fundamental to the identification of bubbles. Although many studies have proposed their own methods, they differ greatly from each other and require laborious and time-consuming manual trial and error processes. The research community is in dire need of an efficient method for infrared image processing. This article proposes a self-adaptive method based on statistics to identify different heat transfer modes and nucleation sites which can be executed by computer automatically and rapidly. A series of pool nucleate boiling experiments using thin titanium foil heaters are carried out, and the temperature and heat flux distributions are analyzed. Based on distinguishing outliers from heat flux data, this method can identify different heat transfer modes (evaporation, convection and transient conduction), and then nucleation sites and bubble dynamics parameters. The experimental data and other open-access data from literatures are used to for validation. A number of evaluation parameters are considered and relatively satisfactory performance are achieved.