Title: Prediction of flow regime using ANN for air-water flow through small diameter tubes in horizontal plane

Authors: Nirjhar Bar; Manindra Nath Biswas; Sudip Kumar Das

Addresses: Chemical Engineering Department, University of Calcutta, 92, A.P.C. Road, Kolkata 700-009, India ' Government College of Engineering and Leather Technology, LB Block, Sector 3, Salt Lake City, Kolkata 700-098, India ' Chemical Engineering Department, University of Calcutta, 92, A.P.C. Road, Kolkata 700-009, India

Abstract: Artificial neural network (ANN) modelling for the classifications of flow regimes in air-water flow through 1 mm to 5 mm tubes is presented. Two hundred eighteen data points based on the experimental investigation in 3 and 4 mm tubes and 2,114 data points from various experimental results collected from the literature for air-water two-phase flow through tubes having small diameter have been used. Five different artificial neural network training algorithms have been used to predict the flow regime. The ANN model based on radial basis function gives slightly better predictability over the other networks.

Keywords: artificial neural networks; ANNs; flow regime prediction; multilayer perceptron; backpropagation; Levenberg-Marquardt; radial basis function; RBF; support vector machines; SVM; principal component analysis; PCA; transfer function; air-water flow; small diameter tubes; horizontal plane; modelling; two-phase flow.

DOI: 10.1504/IJCONVC.2016.082022

International Journal of Convergence Computing, 2016 Vol.2 No.2, pp.107 - 129

Accepted: 14 Aug 2016
Published online: 01 Feb 2017 *

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