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The focus of this paper is on the measurement and calculation model of void fraction for the vertical upward co-current air–water slug flow in a circular tube of 15 mm inner diameter. High-speed photography and optical probes were utilized, with water superficial velocity ranging from 0.089–0.65 m∙s–1 and gas superficial velocity ranging from 0.049–0.65 m∙s–1. A new void fraction model based on the local parameters was proposed, disposing the slug flow as a combination of Taylor bubbles and liquid slugs. In the Taylor bubble region, correction factors of liquid film thickness Cδ and nose shape CZ* were proposed to calculate αTB. In the liquid slug region, the radial void fraction distribution profiles were obtained to calculate αLS, by employing the image processing technique based on supervised machine learning. Results showed that the void fraction proportion in Taylor bubbles occupied crucial contribution to the overall void fraction. Multiple types of void fraction predictive correlations were assessed using the present data. The performance of the Schmidt model was optimal, while some models for slug flow performed not outstanding. Additionally, a predictive correlation was correlated between the central local void fraction and the cross-sectional averaged void fraction, as a straightforward form of the void fraction calculation model. The predictive correlation showed a good agreement with the present experimental data, as well as the data of Olerni et al., indicating that the new model was effective and applicable under the slug flow conditions.

Experimental investigation on vertical upward co-current air–water slug flow was carried out at atmospheric pressure, employing optical probes and high-speed photography. Experiments were performed with water superficial velocity ranging from 0.089 to 1.52 m/s and gas superficial velocity ranging from 0.049 to 1.06 m/s in a polymethyl methacrylate tube of 15 mm inner diameter. Taking advantage of the time-domain characteristics of optical probes, slug flow has been disposed as a combination of Taylor bubbles and liquid slugs. Slug flow identification, characteristic parameters measurement and analysis were conducted in the present study. Firstly, combined with subjective observation, a developed methodology of flow pattern identification based on machine learning was proposed, with emphasis on slug flow. Results showed that the proposed method exhibited an outstanding advantage in the identification in the flow pattern transition region. More importantly, extensive data on local void fraction in liquid slugs (αl,LS) and in continuous slug units (αl), gas-phase velocity of Taylor bubble (UTB) and liquid slug (ULS), length of Taylor bubble (LTB) and liquid slug (LLS), were obtained. Through comprehensive analysis, it was found that turbulence intensity and flow pattern transitions were the dominant factors in affecting the characteristics of slug flow parameters.