3D-CFD SIMULATION AND NEURAL NETWORK MODEL FOR THE j AND f FACTORS OF THE WAVY FIN-AND-FLAT TUBE HEAT EXCHANGERS

A three dimensional (3D) computational fluid dynamics (CFD) simulation and a neural network model are presented to estimate the behaviors of the Colburn factor (j) and the Fanning friction factor (f) for wavy fin-and-flat tube (WFFT) heat exchangers. Effects of the five geometrical factors of fin pitch, fin height, fin length, fin thickness, and wavy amplitude are investigated over a wide range of Reynolds number (600≤Re≤7000). The CFD simulation results express that the geometrical parameters of wavy fins have significant effects on the j and f factors as a function of Reynolds number. The computational results have an adequate accuracy when compared to experimental data. The accuracy of the calculations of the j and f factors are evaluated by the values of the absolute average relative deviation (AARD), being respectively 3.8% and 8.2% for the CFD simulation and 1.3% and 1% for the neural network model. Finally, new correlations are proposed to estimate the values of the j and f factors with 3.22% and 3.68% AARD respectively.