Artificial Neural Networks for Gas‐Liquid Flow Regime Classification in Small Channels

Abstract: The reliable design of multiphase micro‐structured apparatus requires a precise knowledge of the internal flow regime. Previous research indicated that classifiers based on artificial neural networks (ANN) are relatively simple to develop and provide a reasonable accuracy when trained with data for specific inlet designs. This paper introduces advanced ANN classifiers capable of predicting all relevant flow regimes regardless of the inlet design with a recall of 94 % and above for Taylor, churn, dispersed, rivulet, and parallel flows, between 89 % and 94 % for annular and bubbly flows, and 83 % for Taylor‐annular flow. These classifiers were trained and validated by using more than 13,000 experimental data points extracted from 97 flow maps.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Artificial Neural Networks for Gas‐Liquid Flow Regime Classification in Small Channels ; day:25 ; month:04 ; year:2024 ; extent:10 ; Chemie - Ingenieur - Technik ; (25.04.2024) (gesamt 10)

Contributors, Places and Time

• Creator: Haase, Stefan ; May, Henry ; Hiller, Andreas ; Schubert, Markus

Further information

• DOI: 10.1002/cite.202300214
• URN: urn:nbn:de:101:1-2404261406398.340286662595
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:55 AM CEST