Please use this identifier to cite or link to this item:
Title: Estimation of Multiphase Flow Properties using Computational Intelligence Models
Authors: Özbayoğlu, Ahmet Murat
Yüksel, H. Ertan
Keywords: two phase flow
multiphase flow
image processing
computational intelligence
artificial neural networks
decision trees
Issue Date: 2011
Publisher: Elsevier Science Bv
Source: Conference of the Complex Adaptive Systems on Responding to Continuous Global Change in Systems Needs -- OCT 30-NOV 02, 2011 -- Chicago, IL
Series/Report no.: Procedia Computer Science
Abstract: Estimation of flow properties is essential in terms of the efficient usage of resources in drilling operations. Meanwhile, hydraulic characteristics of two phase fluids in annular geometries are not studied thoroughly. In this study, the flow patterns and liquid holdup characteristics of liquid-gas flow is analyzed using experimental data obtained from an eccentric pipe configuration. A high speed digital camera is used for recording the flow; in addition liquid holdup values are calculated using digital image processing techniques instead of empirical correlations or mechanistic measurements. At the same time through the acquired images, corresponding flow patterns are observed. Using the acquired images, estimation models are developed for air-water flow in horizontal eccentric annulus. This is conducted by using computational intelligence rather than conventional mechanistic models. The chosen models are nearest neighbor, backpropagation, decision trees and SVM. Input attributes are superficial Reynolds numbers for both liquid and gas phase. The output is the classified flow pattern and the liquid holdup value. SVM model turned out to he the hest estimator for flow pattern identification process (%92.49 success rate for classifying 7 different flow patterns) whereas regression decision tree had the best performance for liquid holdup determination (RMSE of 0.0777). (C) 2011 Published by Elsevier B.V.
ISSN: 1877-0509
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Show full item record

CORE Recommender


checked on Sep 23, 2022


checked on Sep 24, 2022

Page view(s)

checked on Dec 26, 2022

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.