Estimation of bubble size distribution based on power spectrum

Abstract

A bubble size distribution gives relevant insight into mixing processes where gas-liquid phases are present. The distribution estimation is challenging since accurate bubble detection from images captured from industrial processes is a complicated task due to varying lighting conditions which change the appearance of bubbles considerably. In this paper, we propose a new method for estimating the bubble size distribution based on the image power spectrum. The method works by calculating the power spectrum for a number of frequency bins and learning the linear relationship between the power spectrum and the bubble size distribution. Since the detection of individual bubbles is not needed, the proposed method is remarkably faster than the traditional approaches. The method was compared to a geometry-based bubble detection method with both synthetic and industrial image data. The proposed method outperformed the bubble detection based approach especially in the cases where bubbles were small and the number of bubbles high.