In this work, empirical correlations were proposed to accurately estimate the heat transfer and pressure drop for water flow in sinusoidal wavy channels. The artificial intelligence techniques such as artificial neural network (ANN) and genetic algorithm (GA) were applied to develop the correlations. The experimental data related to the Nusselt number (Nu) and pressure drop (ΔP) in the wavy channels with various geometries were collected for the modeling. The independent variables are Reynolds number (Re) and main geometrical parameters including amplitude ratio (b/H), phase shift (φ), and number of waves (Nw). The results indicate that the ANN is a suitable technique to develop correlations for predicting thermal and flow characteristics in heat exchangers with wavy channels.
