Numerical study of the effects of twisted-tape inserts on heat transfer parameters and pressure drop across a tube carrying Graphene Oxide nanofluid: An optimization by implementation of Artificial Neural Network and Genetic Algorithm

A numerical study is undertaken to investigate the effects of twisted-tape inserts on the heat transfer and pressure drop across a horizontal tube carrying water-based Graphene-Oxide nanofluid. The flow of the nanofluid is considered for dissimilar volume fraction and Re. Twisted tapes of various aspect ratios are placed inside the tube. The results show that, at high volume fractions and Re levels, solid nanoparticles improve the heat transfer provided that the twist ratio is not very small. The highest heat transfer is achieved at twist ratio of 2.34 and nanoparticle volume fraction of 4% regardless of Re, and the highest PEC is corresponded to the twist ratio of 2.34, Re of 5000, and 4 % volume fraction. Then, the Artificial Neural Network is used for the estimation of the Nu, pressure drop, and PEC. Based on the results, Nu rises by increasing Re and the volume fraction, while the friction factor rises by reducing the Re and the twist pitch. Finally, an optimization is made using Genetic Algorithm. Based on the results, the optimal inputs are Re = 19,471, the twist pitch of 0.0376, and the volume fraction of 0.0383 producing Nu of 263.57 and a friction factor of 0.0725.

Automated summary

The study demonstrates that twisted-tape inserts have a significant impact on heat transfer at high volume fractions and Re. Artificial Neural Network and Genetic Algorithm can be used to accurately estimate the optimal parameter combination.