Turbulent flows in a spiral double-pipe heat exchanger Optimal performance conditions using an enhanced genetic algorithm

• Published in: International journal of numerical methods for heat & fluid flow Vol. 30; no. 1; pp. 39 - 53
• Main Authors: Tian, Zhe, Abdollahi, Ali, Shariati, Mahmoud, Amindoust, Atefeh, Arasteh, Hossein, Karimipour, Arash, Goodarzi, Marjan, Bach, Quang-Vu
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Group Publishing Limited 15.01.2020
• Subjects: Aluminum oxide; Computational fluid dynamics; Computer simulation; Copper; Finite volume method; Fluid dynamics; Fluid flow; Fluid motion; Fluids; Friction; Genetic algorithms; Heat conductivity; Heat exchangers; Heat transfer; Heat transfer coefficients; Inlet temperature; Inlets (waterways); Nanofluids; Nanoparticles; Neural networks; Physical properties; Pipes; Reynolds number; Silica; Silicon dioxide; Simulation; Software; Tube heat exchangers; Velocity; Viscosity
• ISSN: 0961-5539; 0961-5539; 1758-6585
• DOI: 10.1108/HFF-04-2019-0287

PurposeThis paper aims to study the fluid flow and heat transfer through a spiral double-pipe heat exchanger. Nowadays using spiral double-pipe heat exchangers has become popular in different industrial segments due to its complex and spiral structure, which causes an enhancement in heat transfer.Design/methodology/approachIn these heat exchangers, by converting the fluid motion to the secondary motion, the heat transfer coefficient is greater than that of the straight double-pipe heat exchangers and cause increased heat transfer between fluids.FindingsThe present study, by using the Fluent software and nanofluid heat transfer simulation in a spiral double-tube heat exchanger, investigates the effects of operating parameters including fluid inlet velocity, volume fraction of nanoparticles, type of nanoparticles and fluid inlet temperature on heat transfer efficiency.Originality/valueAfter presenting the results derived from the fluid numerical simulation and finding the optimal performance conditions using a genetic algorithm, it was found that water–Al2O3 and water–SiO2 nanofluids are the best choices for the Reynolds numbers ranging from 10,551 to 17,220 and 17,220 to 31,910, respectively.