Spatio-temporal void fraction visualization in air-water two-phase flow regime transitions by combination of convolutional neural network and long short-term memory implemented into multiple current-voltage (MCV-CNN_LSTM)

• Published in: Flow measurement and instrumentation Vol. 97; p. 102593
• Main Authors: Saito, Daisuke, Prayitno, Yosephus Ardean Kurnianto, Sejati, Prima Asmara, Miwa, Shuichiro, Takei, Masahiro
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-07-2024
• Subjects: CNN; Flow regime transition; LSTM; Multiple current-voltage; Spatio-temporal; Void fraction; Spatio-temporal; Flow regime transition; CNN; Void fraction; LSTM; Multiple current-voltage
• ISSN: 0955-5986; 1873-6998
• DOI: 10.1016/j.flowmeasinst.2024.102593

Spatio-temporal void fraction in air-water two-phase flow regime transitions has been visualized by combination of convolutional neural network and long short-term memory implemented into multiple -current-voltage (MCV-CNN_LSTM). The MCV-ML is composed of two components, which are MCV-CNN_LSTM training and evaluation. In the first component, four steps are carried out as 1) true void fraction α measurement was conducted by wire-mesh sensor (WMS) as objective variables, 2) simulated MCV voltage U calculation by simulation of MCV measurement for explanatory variable, 3) CNN_LSTM training instance generation, and 4) CNN_LSTM model training and testing. In the second component, two additional steps are experimentally conducted which are 5) actual MCV voltage V measurement by MCV for input of trained ML, and 6) spatio-temporal void fraction α prediction. For the dataset generation, the experiment was conducted on air-water two phase flow regime transitions in vertical pipe with an inner diameter of 25 mm and a length of 350 mm from the elbow. The superficial velocity of liquid and gas phase are varied to present the flow regime transition of bubbly to slug flow. As a result, the estimated spatio-temporal void fraction αˆ by MCV-CNN_LSTM enables the visualization of detailed gas distribution in vertical upward two-phase flow. The spatial averaged instantaneous void fraction measured by WMS and estimated by MCV-CNN_LSTM show qualitative agreement in normalized cross correlation (INCC) of 0.364 on its temporal variation.
•Spatio-temporal void fraction visualization in air-water two-phase flow regime transition.•Combination of convolutional neural network and long short-term memory (CNN_LSTM).•Multiple current-voltage with embedded CNN_LSTM (MCV-CNN_LSTM).•True void fraction measured by wire-mesh sensor (WMS).•Comparison of spatial-averaged instantanious void fraction between MCV-CN_LSTM and WMS.