Prediction of two-phase flow patterns in upward inclined pipes via deep learning

Zi Lin, Xiaolei Liu, Liyun Lao, Hengxu Liu

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

The industrial process involving gas liquid flows is one of the most frequently encountered phenomena in the energy sectors. However, traditional methods are practically unable to reliably identify flow patterns if additional independent variables/parameters are to be considered rather than gas and liquid superficial velocities. In this paper, we reported an approach to predict flow pattern along upward inclined pipes (0–90°) via deep learning neural networks, using accessible parameters as inputs, namely, superficial velocities of individual phase and inclination angles. The developed approach is equipped with deep learning neural network for flow pattern identification by experimental datasets that were reported in the literature. The predictive model was further validated by comparing its performance with well-established flow regime forecasting methods based on conventional flow regime maps. Besides, the intensity of key features in flow pattern prediction was identified by the deep learning algorithm, which is difficult to be captured by commonly used correlation approaches.
Original languageEnglish
Article number118541
JournalEnergy
Volume210
Early online date15 Aug 2020
DOIs
Publication statusPublished - 1 Nov 2020

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