Two-phase flow pressure drop modelling in horizontal pipes with different diameters

The two-phase frictional pressure drop has a dominant effect in many industrial applications associated with the multiphase flow. This study investigated accuracy of several available methods for predicting different pipe diameters using 4124 experimental data points. It is observed that performance existing poor wide range operating conditions. Then, Artificial Neural Network models were proposed, including six multilayer perceptron (MLP) and one Radial Basis Function (RBF) same sets. weights biases ANNs optimized Levenberg-Marquardt (LM), Bayesian Regularization (BR), Scaled Conjugate Gradient (SCG), Resilient Backpropagation (RB), Particle Swarm Optimization (PSO) Genetic Algorithm (GA). Statistical error analysis indicates neural network incorporated (MLP-GA) predicts entire set Root Mean Square Error 0.525 an Average Absolute Relative percentage 6.722. Finally, sensitivity was carried out, indicating mass flux (G) highest direct impact on drop.