Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then...

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Bibliographic Details
Main Authors: Shanying Lin, Jialu Xu, Shengnan Liu, Muk Chen Ong, Wenhua Li
Format: Article
Language:English
Published: MDPI AG 2023-09-01
Series:Applied Sciences
Subjects:
convolutional neural network
feature fusion
jumper
gas–liquid two-phase flow
flow regime identification
Online Access:https://www.mdpi.com/2076-3417/13/18/10512

Internet

https://www.mdpi.com/2076-3417/13/18/10512

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