In the current study, the artificial neural network modeling of the Nusselt number and friction factor of a heat exchanger, equipped with the twisted tapes to enhance the heat transfer capacity, has been done. In the modeling procedure, alongside the Reynolds number, four geometrical characteristics of the twisted cut tapes, including the depth of cut, the area of cut, the perimeter of cut, and the pitch length of the twisted tapes, have been considered. The required data were gathered from both experiments and the CFD method. The results clarified that compared to the experimental procedures and numerical methods, the artificial neural network model has an outstanding performance in considerably less time and cost. Despite a variety of cuts, the present work introduced generalized and unique tools to design and control these systems. The work interestingly reveals that the model can forecast the friction factor and the heat transfer augmentation only by the specification parameter of cuts rather than the shape of cuts. Further sensitivity studies also proved that, if necessary, some variables could be removed from the input vectors, and the models predicting ability remains acceptable.

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