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Fluids
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Advances in Multiphase Flow Measurement and Simulation
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Advances in Multiphase Flow Measurement and Simulation

A Special Issue of Fluids (ISSN 2311-5521) belonging to the section "Flow of Multi-Phase Fluids and Granular Materials".

Deadline for manuscript submissions: 25 December 2026 | Viewed by 733

Editors

Dr. Xuelong Yang

E-Mail Website
Guest Editor
College of Metrology Measurement and Instrument, China Jiliang University, Hangzhou 310018, China
Interests: moisture separation; liquid–gas jet pump; ejector; multiphase flow
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Youmin Hou

E-Mail Website
Guest Editor
School of Power and Mechanical Engineering, Wuhan University, Wuhan 430072, China
Interests: multiphase flow; heat and mass transfer

Special Issue Information

Dear Colleagues,

Multiphase flow is the core flow form in the fields of energy, chemical engineering, petroleum, metallurgy, and new energy. Its complex interactions between phases, dynamic evolution of interfaces, and coupling of multiple physical field strengths have long posed key technical bottlenecks for high-precision measurement and efficient numerical simulation.

This Special Issue focuses on the cutting-edge developments in multiphase flow measurement and simulation. The system includes advanced testing technologies such as non-invasive online detection, synchronous measurement of multiple physical fields, intelligent identification of flow patterns, as well as research results on CFD numerical modeling, cross-scale simulation, multi-method coupling simulation and AI-driven numerical optimization. Intended to promote the collaborative improvement of multiphase flow measurement accuracy and simulation efficiency, promote theoretical innovation, technological breakthroughs and deep integration of engineering applications and provide academic support and reference for technological upgrades in related industrial fields.

Dr. Xuelong Yang
Prof. Dr. Youmin Hou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fluids is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multiphase flow
  • non-invasive measurement
  • flow pattern identification
  • DEM-CFD coupling
  • cross-scale simulation
  • population balance model
  • oil and gas engineering
  • new energy
  • chemical process

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

47 pages, 11717 KB  
Article
Hybrid Convolutional, Transformer and Physics-Encoding Networks for Multiphase Flow Pattern Identification in Vertical Pipelines
by Eric Thompson Brantson, Mukhtar Abdulkadir, Ransford Yeboah, Ebenezer Kobina Abakah, Edzie William Otubuah and Martin Luther Afirim
Fluids 2026, 11(9), 210; https://doi.org/10.3390/fluids11090210 - 24 Aug 2026
Viewed by 216
Abstract
Accurate identification of multiphase flow patterns in vertical pipelines is critical for operational safety and efficiency in the oil and gas industry. Yet, conventional methods struggle with subjectivity and transitional regimes. This study develops and integrates three neural network architectures: a convolutional neural [...] Read more.
Accurate identification of multiphase flow patterns in vertical pipelines is critical for operational safety and efficiency in the oil and gas industry. Yet, conventional methods struggle with subjectivity and transitional regimes. This study develops and integrates three neural network architectures: a convolutional neural network (CNN) for spatial features, a transformer neural network (TNN) for long-range dependencies, and a physics-encoding network (PEN) for embedding physical constraints. These are combined into a hybrid framework trained on an experimental dataset of 2131 images from a wire mesh sensor, annotated using a semi-automated pipeline. Results show the hybrid model achieved 95.91% test accuracy with a macro F1-score of 0.96, the highest of the four models evaluated, with its main advantage in transitional regimes. A multi-seed ablation shows that the convolutional branch provides the dominant discriminative signal, while the transformer and physics-inspired branches added complementary improvements that are consistent across runs. This hybridisation mitigates individual model weaknesses, with the physics-inspired branch acting as a spatial regulariser that improves interpretability, providing a robust and objective tool for reliable pipeline monitoring. Full article
(This article belongs to the Special Issue Advances in Multiphase Flow Measurement and Simulation)
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