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Application of ANN and GA for the Prediction and Optimization of Thermal and Flow Characteristics in a Rectangular Channel Fitted with Twisted Tape Vortex Generators

This study reports an application of the hybrid model, including back propagation network and genetic algorithm, for predicting the thermal and flow characteristics in a rectangular channel fitted with multiple twisted tape vortex generators (MT-VG). Dimensionless geometric parameters and Reynolds number were considered as network inputs, and Nusselt number and friction factor were the output variables. The performance of the developed neural networks was found to be superior in comparison with the empirical correlations. In addition, the proposed networks have been considered as two objective functions in order to obtain optimal operation conditions. Since mentioned objectives are conflicting, the multi-objective optimization using genetic algorithm was used for the optimization.