Integrating machine learning and image processing for void fraction estimation in two-phase flow through corrugated channels

There is a substantial amount of information embedded in images of two-phase flow captured through highspeed video (HSV) or high -resolution photography. However, accurate image segmentation is necessary to unlock a meaningful analysis of the data. In this study, we discuss how to estimate the flow void fraction in chevron -type corrugated channels typical of compact plate heat exchangers (CPHE) from back -lit front -view HSV images, using machine learning (ML) algorithms and data processing techniques. A U -Net neural network was employed for image segmentation, demonstrating robust performance with evaluation metrics consistently exceeding 0.9. The binary masks (indicating gas or liquid phases) derived from segmentation were processed in MATLAB (R) to estimate void fraction through a 3D reconstruction algorithm of the gas cluster's volume. In contrast to conventional void fraction estimates based on the area ratio of binary masks, this algorithm models the curvature of the liquid -vapor interface through the corrugated channel. When compared to other methods, our algorithm predicts very similar void fraction contour maps. However, the average discrepancy between our algorithm and the area -ratio approach can be as high as 80%, underscoring the importance of the processing method in analyzing the data and developing correlations. Finally, a drift flux model was introduced to predict the void fraction distribution using a two-part equation accommodating the dependency of the distribution coefficient C 0 on the liquid flow rate for a corrugation Froude number Fr similar to larger than 1. The proposed model can predict the void fraction dataset with a mean average percentage error of 8.17%. In summary, U-Net's pixel -level accuracy facilitates deep and precise post -processing of HSV images, enabling meaningful void fraction measurements. Thanks to its generality and minimal training effort requirements, the discussed methodology can be applied to estimate void fractions in various two-phase flow experiments and operating conditions.