Details

Autor(en) / Beteiligte

• Han, Bangbang
• Ge, Bin
• Wang, Fan
• Gao, Qixin
• Li, Zhixuan
• Fang, Lide

Titel

Void fraction detection technology of gas-liquid two-phase bubbly flow based on convolutional neural network

Ist Teil von

• Experimental thermal and fluid science, 2023-04, Vol.142, p.110804, Article 110804

Ort / Verlag

Elsevier Inc

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •A detection method based on Faster R-CNN for real-time bubble recognition and feature extraction, is developed.•Void fraction of gas–liquid two-phase bubbly flow is studied.•The uncertainty of the method is 10 % and average error of this method is 7.07 %, which has high accuracy. Gas-liquid two-phase bubbly flow widely exists in the field of natural gas extraction. The identification and feature extraction of bubbles are becoming more and more important to the pipe system. This study develops a detection method based on Faster R-CNN for real-time bubble recognition, feature extraction, and void fraction calculation. The method detects ellipsoidal large bubbles in gas–liquid two-phase bubbly flow in a vertical closed pipeline with a high void fraction. The uncertainty of the method is 10 %. Then compared to the gas volume holdup from standard flowmeter, average error of this method is 7.07 %, which has high accuracy. This new detection method finds a new way for feature extraction of two-phase flow.