CFD Investigation and neutral network modeling of heat transfer and pressure drop of nanofluids in double pipe helically baffled heat exchanger with a 3-D fined tube. (5th May 2016)

• Record Type: Journal Article
• Authors: Saeedan, Mahdi; Solaimany Nazar, Ali Reza; Abbasi, Yaser; Karimi, Reza
• Is Part Of: Applied thermal engineering. Volume 100(2016:May)
• Page Start: 721
• Page End: 729
• Publication Date: 2016-05-05
• Subjects: Heat Transfer -- Heat Exchanger -- Helical Baffle -- Nanofluids -- Neutral Network Modeling; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2016.01.125
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Water based nanofluids of Cu, CuO, and CNT nanoparticles at different volume concentrations are considered. The effects of Reynolds number and volume concentration on heat transfer and pressure drop are evaluated. Models of Nusselt number and pressure gradient are obtained by applying the neural network. Abstract: Energy consumption can be economized by using nanofluids as heat transfer medium in heat exchangers. Thermal performance of a helically baffled heat exchanger combined with a 3D fined tube operated with nanofluids is investigated numerically. Water based nanofluids of Cu, CuO, and CNT nanoparticles at different volume concentrations are considered. The effects of Reynolds number and volume concentration on heat transfer and pressure drop are evaluated. An increase in the volume concentration and Reynolds number intensified both heat transfer and pressure drop. For CuO/water and Cu/water nanofluid, the Nu Number increased as the volume concentration increased, while for CNT/water, the Nu number decreased as the volume concentration increased. Models of Nusselt number and pressure gradient are obtained in the heat exchanger in terms of Reynolds number, volume concentration and physical properties of particles by applying the neural network. The neural network predicts the output variables with a great accuracy.