Visual identification of oscillatory two-phase flow with complex flow patterns
In: ISSN: 0263-2241, 2021
Online
academicJournal
Zugriff:
International audience ; We present an approach based on computer vision and machine learning methods to identify two-phase flow with complex flow patterns in oscillatory conditions. A visualization experiment bench was designed, constructed, and used to simulate the actual reciprocating motion of the cooling gallery inside the piston of low-speed diesel engines. The results of our proposed approach show that the feature vectors extracted from the optical flow images provides a valuable reference for the velocity vectors in two-phase flow. We show that it is possible to identify oscillatory two-phase flow videos with respect to Reynolds numbers from 10568 to 31704 using a Bayesian Network classifier, with the best accuracy of 94%. The approach purposed in this paper can not only be used to present the validating sources for numerical simulation results, but also be widely applied in the visualization of multiphase flow, which is a key area to be developed on the basic research of heat transfer systems.
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Visual identification of oscillatory two-phase flow with complex flow patterns
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Autor/in / Beteiligte Person: | Huang, Yuqi ; Li, Dominique ; Niu, Haoyi ; Conte, Donatello ; University, Zhejiang ; Laboratoire d'Informatique Fondamentale et Appliquée de Tours (LIFAT) ; Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL) ; Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS) ; Université de Tours (UT) ; Bases de données et traitement des langues naturelles (BDTLN) ; Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université de Tours (UT)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL) |
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Zeitschrift: | ISSN: 0263-2241, 2021 |
Veröffentlichung: | HAL CCSD ; Elsevier, 2021 |
Medientyp: | academicJournal |
DOI: | 10.1016/j.measurement.2021.110148 |
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