Typical bubble shape estimation in two-phase flow using inverse problem techniques

Two-phase flow models predict different arrangements of gas and liquid, which include the formation of bubbles, slugs and aerated areas. Thus, flow visualization plays a central role in development and refinement of models. Recently, high-speed cameras have enabled the recording of these flows and the challenge is often to extract relevant information from large amounts of data.This paper analyzes and proposes some schemes to estimate a typical bubble based on flow images. The key idea is to find a bubble image that represents and characterizes the flow conditions. As we show, using simple mean as estimator does not yield meaningful images and can be successfully replaced by median. Finally, observing the power spectral density of individual bubble images, we propose an inverse problem regularizer combined with total variation penalization, which significantly improves image quality and sharpens bubble boundaries.