Characteristics and Flow Measurement of Gas-Liquid Two-Phase Counter-Current Flow in Vertical Annulus

Characteristics and Flow Measurement of Gas-Liquid Two-Phase Counter-Current Flow in Vertical Annulus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Characteristics and Flow Measurement of Gas-Liquid Two-Phase Counter-Current Flow in Vertical Annulus Feng Cao, Anzhao Ji, Zhanjun Chen, Xuefen Liu, Peng Zhang, Fengfeng Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9250438/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Gas-liquid two-phase counter-current flow in vertical annulus is involved in multiple industrial fields, due to the influence of the inner pipe, there are difficulties in measuring gas-liquid flow rates. To solve this problem, this paper proposes a new method for two-phase flow measurement based on differential pressure signals and machine learning models. Experiments of gas-liquid two-phase flow were conducted on a vertical annulus pipe with adjustable eccentricity, and the relationships between the probability density function and power spectral density function of two types of differential pressure signals, gas and liquid superficial velocities, and pipe eccentricity were analyzed. An unsupervised classification algorithm based on local density was used to identify the flow patterns. A gas-liquid flow rate prediction model was constructed based on the artificial neural network model and hyper-parameter optimization was performed, achieving an average absolute percentage error of 28.85% for liquid and 8.56% for gas. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Physics Gas-liquid two-phase flow measurement Vertical annulus Two-phase counter-current flow Differential pressure signals Artificial Neural Network Regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 01 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviews received at journal 11 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviewers agreed at journal 04 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor invited by journal 01 Apr, 2026 Editor assigned by journal 29 Mar, 2026 Submission checks completed at journal 29 Mar, 2026 First submitted to journal 28 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9250438","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617844828,"identity":"8b4d2d3d-5c52-42f9-8265-61eb8d25b7f4","order_by":0,"name":"Feng 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