Turbulent Heat Transfer Characteristics of Supercritical Carbon Dioxide for a Vertically Upward Flow in a Pipe Using Computational Fluid Dynamics and Artificial Neural Network

Abstract

Modeling of turbulence heat transfer for supercritical fluids, using computational fluid dynamics (CFD) software, is always challenging due to the drastic property variations near the critical point. The use of artificial neural networks (ANNs) along with numerical methods has shown promising results in predicting heat transfer coefficients of heat exchangers. In this study, the accuracy of four different turbulent models available in the commercial CFD software—ansysfluent is investigated against the available experimental results. The k–ε Re-normalization group (RNG) model, with enhanced wall treatment, is found to be the best-suited turbulence model. Further, K–ε RNG turbulence model is used in CFD for parametric analysis to generate the data for ANN studies. A total of 1,34,698 data samples was generated and fed into the ANN program to develop an equation that can predict the heat transfer coefficient. It was found that, for the considered range of values, the absolute average relative deviation is 3.49%.