Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
