Investigation of the Gas–Liquid Flow and Separation Behaviors in Pipelines
A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Chemical Processes and Systems".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 2091
Editors
Interests: gas-liquid flow; flow regime identification; flow visualization; flow assurance; deep learning
Interests: heat and mass transfer; gas-liquid two-phase flow; multiphase flow parameter measurement
Interests: multi-view learning; label distribution learning
Interests: efficient transportation of unconventional oil; multiphase flow and flow assurance in pipes or wellbores; safety of oil and gas storage and transportation engineering.
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The safe and efficient transport of fluids through pipelines has long been a cornerstone of modern industry, underpinning global energy supply chains, chemical manufacturing, and environmental management. Gas–liquid flow dynamics and separation behaviors in pipeline systems represent a critical area in a wide range of industries, including oil and gas, chemical engineering, and energy systems. These flows are inherently complex due to the strong interactions between phases under varying pressure, pipeline structure, temperature, and flow conditions. A deeper understanding of multiphase flow behavior is essential for the design, optimization, and sustainable operation of pipeline systems, leading to improved efficiency, safety, and performance.
This Special Issue, “Investigation of the Gas-Liquid Flow and Separation Behaviors in Pipelines”, seeks high-quality works focusing on novel advances in experimental, theoretical, and computational investigations into flow regime transitions, pressure drop prediction, flow visualization and measurement, and innovative separation technologies.
Topics of interest include, but are not limited to, the following:
- Advanced experimental techniques for flow visualization and measurement;
- Development of models for flow regime transitions, pressure drops, and separation efficiency;
- Novel separator designs and performance evaluation;
- Computational fluid dynamics (CFD) modeling of multiphase flows;
- Data-driven/machine learning methods for flow prediction and optimization;
- Case studies addressing real-world industrial challenges.
I hope you consider participating in this Special Issue.
Dr. Yingjie Chang
Dr. Xuebo Zheng
Dr. Qinghai Zheng
Dr. Jie Sun
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly 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 2400 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
- gas-liquid flow
- pipeline systems
- flow dynamics
- flow visualization
- separator design
- machine learning
- deep learning
- flow regime identification
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