Energy and entropy generation analyses of a nanofluid-based helically coiled pipe under a constant magnetic field using smooth and micro-fin pipes: Experimental study and prediction via ANFIS model

The 1st aim of the present experimental study is on expanded energy and entropy generation analyses for a helically coiled pipe using micro-fin tube and magnetic field. To this end, the influence of various coil diameters, coil pitches, and test fluids (Fe3O4-water 1% wt. and distilled water) on the thermal performance are investigated. The 2nd aim is to predict the Nusselt number, friction factor and entropy generation using adaptive neuro fuzzy inference system (ANFIS) model. The results showed that the case with a diameter of 135 mm and a pitch of 30 mm represents a higher thermal efficiency in the water-based coil. For more accurate optimization from the operation and design standpoints, in addition to maximizing efficiency, the entropy generation number should be minimized. Consequently, the entropy generation number variations is assessed through four key parameters of Reynolds number, coil diameter, coil pitch, presence/absence of magnetic field. At a fixed pitch, the cases with diameters of 90 and 135 mm are the optimum cases in terms of exergy viewpoint among the other coils for distilled water and nanofluid, respectively. The AFNIS model in this study predicts the results with average relative errors of 0.67, 4.48 and 2.83% for Nusselt number, friction factor and entropy generation.

Automated summary

This experimental study focuses on expanded energy and entropy generation analyses for a helically coiled pipe using micro-fin tube and magnetic field, investigating the influence of various coil diameters, coil pitches, and test fluids. The ANFIS model accurately predicts Nusselt number, friction factor, and entropy generation, with the optimal cases identified in terms of exergy viewpoint. The average relative errors for the predicted results are found to be 0.67%, 4.48%, and 2.83% for Nusselt number, friction factor, and entropy generation, respectively.