Prediction of entrance length for low Reynolds number flow in pipe using neuro-fuzzy inference system

Abstract

This paper proposes an adaptive network fuzzy inference system (ANFIS) for the prediction of entrance length in pipe for low Reynolds number flow. After using the computational fluid dynamics (CFD) technique to establish the basic database under various working conditions, an efficient rule database and optimal distribution of membership function is constructed from the hybrid-learning algorithm of ANFIS. An experimental data set is obtained with Reynolds number, diameter of the pipe, and inlet velocity as input parameters and entrance length as output parameter. The input–output data set is used for training and validation of the proposed techniques. After validation, they are forwarded for the prediction of entrance length. The entrance length estimation results obtained by the model are compared with existing predictive models and are presented. The model performed quite satisfactory results with the actual and predicted entrance length values. The model can also be used for estimating entrance length on-line but the accuracy of the model depends upon the proper training and selection of data points.