Artificial neural network analysis of the Nusselt number and friction factor of hydrocarbon fuel under supercritical pressure - Kaihang Tao; Jianqin Zhu; Zeyuan Cheng; Dike Li (Propulsion and Power Research, 2022)
This paper presents the Nusselt number and friction factor model for hydrocarbon fuel under supercritical pressure in horizontal circular tubes using an artificial neural network (ANN) analysis on the basis of the back propagation algorithm. The derivation of the proposed model relies on a large number of experimental data obtained from the tests performed with the platform of supercritical flow and heat transfer. Different topology structures, training algorithms and transfer functions are employed in model optimization. The performance of the optimal ANN model is evaluated with the mean relative error, the determination coefficient, the number of iterations and the convergence time. It is demonstrated that the model has high prediction accuracy when the tansig transfer function, the Levenberg-Marquardt training algorithm and the three-layer topology of 4-9-1 are selected. In addition, the accuracy of the ANN model is observed to be the highest compared with other classic empirical correlations. Mean relative error values of 4.4% and 3.4% have been achieved for modeling of the Nusselt number and friction factor respectively over the whole experimental data set. The ANN model established in this paper is shown to have an excellent performance in learning ability and generalization for characterizing the flow and heat transfer law of hydrocarbon fuel, which can provide an alternative approach for the future study of supercritical fluid characteristics and the associated engineering applications.