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

Lin, Z., Liu, X. , Lao, L. and Liu, H. (2020) Prediction of two-phase flow patterns in upward inclined pipes via deep learning. Energy, 210, 118541. (doi: 10.1016/j.energy.2020.118541)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Dr Xiaolei

Creator Roles

Liu, X.: Conceptualization, Software, Investigation, Data curation, Writing – review and editing, Supervision

• Authors: Lin, Z., Liu, X., Lao, L., and Liu, H.
• Subjects: T Technology > TA Engineering (General). Civil engineering (General)
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Energy
• Publisher: Elsevier
• ISSN: 0360-5442
• ISSN (Online): 1873-6785
• Published Online: 15 August 2020
• Copyright Holders: Copyright © 2020 Elsevier Ltd.
• First Published: First published in Energy 210:118541
• Publisher Policy: Reproduced in accordance with the publisher copyright policy