Real-Time Air-Water Volume Fraction Prediction Using Deep Learning and High-Speed Imaging
Conference
Sharifi, A, Zanje, SR, Mahyawansi, P et al. (2025). Real-Time Air-Water Volume Fraction Prediction Using Deep Learning and High-Speed Imaging
. 471-485. 10.1061/9780784486184.043
Sharifi, A, Zanje, SR, Mahyawansi, P et al. (2025). Real-Time Air-Water Volume Fraction Prediction Using Deep Learning and High-Speed Imaging
. 471-485. 10.1061/9780784486184.043
Accurate real-time measurement of gas volume fractions is essential for analyzing multiphase flows, especially in pipe systems where understanding gas-liquid dynamics is critical for optimizing fluid transport and identifying potential issues like pressure surges or blockages. This study used a non-intrusive high-speed imaging to capture a transient water-air interactions within a transparent circular pipe, capturing a high-resolution visual data of a multiphase flow regimes. However, limited visual access in the turbulent flows with a gas-liquid interfaces restricts the visualization of interfacial dynamics. To address this, we developed a method to process video frames using a deep learning framework, combining the segment anything model (SAM) with the K-means clustering for an image segmentation. A Resnet-based neural network was trained to predict the water-air volume fractions across. This method enables real-time multiphase flow analysis with an application in the fluid transport optimization and a pipeline monitoring. The volume fraction correction was performed by comparing the predicted air-water volume fractions against low-intrusive WMS measurements, achieving an accuracy of 92.67%. This ensured accurate predictions and allowed for the assessment of uncertainties in the image processing methodology.
