Intelligent image-based gas-liquid two-phase flow regime recognition / Soheil Ghanbarzadeh in Transactions of the ASME . Journal of fluids engineering, Vol. 134 N° 6 (Juin 2012)  

Public ISBD [article]  inTransactions of the ASME . Journal of fluids engineering > Vol. 134 N° 6 (Juin 2012) . - 10 p. Titre : Intelligent image-based gas-liquid two-phase flow regime recognition Type de document : texte imprimé Auteurs : Soheil Ghanbarzadeh, Auteur ; Pedram Hanafizadeh, Auteur ; Mohammad Hassan Saidi, Auteur Année de publication : 2012 Article en page(s) : 10 p. Note générale : fluids engineering Langues : Anglais (eng) Mots-clés : two-phase flow  flow regime  image processing  fuzzy logic  neural network Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : Identification of different flow regimes in industrial systems operating under two-phase flow conditions is necessary in order to safely design and optimize their performance. In the present work, experiments on two-phase flow have been performed in a large scale test facility with the length of 6 m and diameter of 5 cm. Four main flow regimes have been observed in vertical air-water two-phase flow at moderate superficial velocities of gas and water namely: Bubbly, Slug, Churn, and Annular. An image processing technique was used to extract information from each picture. This information includes the number of bubbles or objects, area, perimeter, as well as the height and width of objects (second phase). In addition, a texture feature extraction procedure was applied to images of different regimes. Some features which were adequate for regime identification were extracted such as contrast, energy, entropy, etc. To identify flow regimes, a fuzzy interface was introduced using characteristic of second phase in picture. Furthermore, an Adaptive Neuro Fuzzy (ANFIS) was used to identify flow patterns using textural features of images. The experimental results show that these methods can accurately identify the flow patterns in a vertical pipe. DEWEY : 620.1 ISSN : 0098-2202 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JFEGA4000134000006 [...] [article] Intelligent image-based gas-liquid two-phase flow regime recognition [texte imprimé] / Soheil Ghanbarzadeh, Auteur ; Pedram Hanafizadeh, Auteur ; Mohammad Hassan Saidi, Auteur . - 2012 . - 10 p. fluids engineering Langues : Anglais (eng) in Transactions of the ASME . Journal of fluids engineering > Vol. 134 N° 6 (Juin 2012) . - 10 p. Mots-clés : two-phase flow  flow regime  image processing  fuzzy logic  neural network Index. décimale : 620.1 Essais des matériaux. Défauts des matériaux. Protection des matériaux Résumé : Identification of different flow regimes in industrial systems operating under two-phase flow conditions is necessary in order to safely design and optimize their performance. In the present work, experiments on two-phase flow have been performed in a large scale test facility with the length of 6 m and diameter of 5 cm. Four main flow regimes have been observed in vertical air-water two-phase flow at moderate superficial velocities of gas and water namely: Bubbly, Slug, Churn, and Annular. An image processing technique was used to extract information from each picture. This information includes the number of bubbles or objects, area, perimeter, as well as the height and width of objects (second phase). In addition, a texture feature extraction procedure was applied to images of different regimes. Some features which were adequate for regime identification were extracted such as contrast, energy, entropy, etc. To identify flow regimes, a fuzzy interface was introduced using characteristic of second phase in picture. Furthermore, an Adaptive Neuro Fuzzy (ANFIS) was used to identify flow patterns using textural features of images. The experimental results show that these methods can accurately identify the flow patterns in a vertical pipe. DEWEY : 620.1 ISSN : 0098-2202 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JFEGA4000134000006 [...]

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