Second Law Analysis of Fully Developed Convection in a Helical Coiled Tube Under Constant Wall Temperature Using a CFD-ANN Approach

Abstract

The present paper analyses the second law of thermodynamics in a fully developed forced convection in the horizontal helical coiled tube under constant wall temperature. The influence of non-dimensional parameters such as Reynolds number (Re), coil-to-tube ratio (δ) and coil pitch (λ) are inspected on the entropy generation. According to the literature, the coil pitch has a minor effect on the entropy generation compared with Re and δ. Using a CFD tool is a common classical method to find the optimal Reynolds Number and coil-to-tube ratio (δ) based on the entropy generation minimization principal. This approach requires lots of time and resources while the innovative implementation of an Artificial Neural Network (ANN) reduces the simulation time considerably. The data pool generated by the CFD tool is used to train the ANN. As less data is needed to train the ANN in comparison to classical CFD based method, the performance of ANN-CFD optimization approach enhances. As entropy generation minimization principal is applied during the optimization, Nusselt number and friction factor are required to evaluate the entropy generation; these parameters are obtained through a numerical simulation and then are used to train the ANN. The ANN can predict these parameters as a function of different Re numbers and coil-to-tube ratios during optimization. Several different architectures of ANNs were evaluated and parametric studies were performed to optimize network design for the best prediction of the variables. The results obtained from the ANN are compared with the available experimental data to show the network reasonable accuracy.