Adaptive neuro-fuzzy modeling of convection heat transfer of turbulent supercritical carbon dioxide flow in a vertical circular tube

Heat transfer of supercritical fluids has been the subject of many investigations; however, since the analysis of heat transfer in these fluids established by a mathematical model based on the planning parameters is complicated, this study attempts to provide a model for convection heat transfer of turbulent supercritical carbon dioxide flow in a vertical circular tube with a hydraulic diameter of 7.8 mm in inlet bulk temperature of 15 °C and a 8 MPa constant pressure by empirical results obtained by Kim et al.[1] and adaptive neuro-fuzzy inference system (ANFIS). At first, we considered Nux as a target parameter and qw, G, Bo* and x+ as input parameters. Then, we randomly divided 123 empirical data into train and test sections in order to accomplish modeling. We instructed ANFIS network by 75% of the empirical data. Twenty-five percent of primary data which had been considered for testing the appropriateness of the modeling were entered into the ANFIS model. Results were compared by two statistical criterions (R2 and RMSE) with empirical ones. Considering the results, it is obvious that our proposed modeling by ANFIS is efficient and valid and it can be expanded for more general states.