Experimental and neural network modelling of polymer drag reduction in 180° bends

Abstract

This research investigates the effect of small amounts of drag-reducing polymer (DRP), (Partially hydrolysed polyacrylamide) on single phase water flow in 180° bends using both experimental and artificial neural network (ANN) modelling approach. The results show that, in both straight pipes and U-bend, drag reduction (DR) generally increased with polymer concentration until an optimum concentration is reached beyond which further increase in concentration resulted in increased drag. It was observed that using different concentrations of master solution had negligible effect on the computed DR as long as there is sufficient mixing to allow for homogeneous polymer dispersion. The results further revealed that DR in straight pipes are significantly higher than that in bends of equivalent length and the highest DR reported were 65%, 42% and 45% in straight, horizontal U-bend and down-flow U-bend pipe configurations respectively. It was also reported here that DR increased with Reynolds number for all flow configurations investigated. This study was also extended to ANN modelling of the various output parameters of interest such as friction factor and bend friction coefficient. Some test cases showed that one neuron was sufficient in predicting the output parameters. This suggests that classical models would also be adequate in modelling single-phase flow drag reduction in and around bends. Model predictions were excellent with cross correlation of nearly 100% (99.99%). In this work, the ANN model coded using Levenberg–Marquardt LM algorithm and logsigmoid transfer function gave the best Neural Network model performance as indicated by its lowest value of X2 (=2.03E-04). The proposed ANN model can be used with confidence for the prediction of friction factor, bends coefficient and drag reduction DR in straight pipes and return bends.