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contributor authorSoheil Ghanbarzadeh
contributor authorPedram Hanafizadeh
contributor authorMohammad Hassan Saidi
date accessioned2017-05-09T00:51:19Z
date available2017-05-09T00:51:19Z
date copyrightJune, 2012
date issued2012
identifier issn0098-2202
identifier otherJFEGA4-27535#061302_1.pdf
identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/149132
description abstractIdentification 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.
publisherThe American Society of Mechanical Engineers (ASME)
titleIntelligent Image-Based Gas-Liquid Two-Phase Flow Regime Recognition
typeJournal Paper
journal volume134
journal issue6
journal titleJournal of Fluids Engineering
identifier doi10.1115/1.4006613
journal fristpage61302
identifier eissn1528-901X
keywordsFlow (Dynamics)
keywordsFuzzy logic
keywordsTwo-phase flow
keywordsImage processing
keywordsSlug
keywordsModeling AND Feature extraction
treeJournal of Fluids Engineering:;2012:;volume( 134 ):;issue: 006
contenttypeFulltext


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