Modeling of heat transfer and fluid flow characteristics of helicoidal double-pipe heat exchangers using Adaptive Neuro-Fuzzy Inference System (ANFIS)

In this paper an Adaptive Neuro-Fuzzy Inference System (ANFIS) is used for modeling the effect of important parameters on heat transfer and fluid flow characteristics of helicoidal double-pipe heat exchangers using some numerically investigated and compared with those to experimental results for training and test data. In this way, overall heat transfer coefficient (Uo) and inner and annular pressure drop (ΔPin, ΔPan) are modeled with respect to the variation of inner and annular dean number (Dein, Dean), inner and annular Prandtl number (Prin, Pran) and pitch of coil (B) which are defined as input (design) variables. Then, we divided these data into train and test sections in order to accomplish modeling. We instructed ANFIS network by 75% of numerical-validated data. Twenty-five percent of primary data which had been considered for testing the appropriateness of the models was entered into ANFIS network models and results were compared by two statistical criterions (R2, RMSE). 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.