Modelling, Analysis and Entropy Generation Minimization of Al₂O₃-Ethylene Glycol Nanofluid Convective Flow inside a Tube

Abstract

Entropy generation is always a matter of concern in a heat transfer system. It denotes the amount of energy lost as a result of irreversibility. As a result, it must be reduced. The present work considers an investigation on the turbulent forced convective heat transfer and entropy generation of Al2O3-Ethylene glycol (EG) nanofluid inside a circular tube subjected to constant wall temperature. The study is focused on the development of an analytical framework by using mathematical models to simulate the characteristics of nanofluids in the as-mentioned thermal system. The simulated result is validated using published data. Further, Genetic algorithm (GA) and DIRECT algorithm are implemented to determine the optimal condition which yields minimum entropy generation. According to the findings, heat transfer increases at a direct proportion to the mass flow, Reynolds number (Re), and volume concentration of nanoparticles. Furthermore, as Re increases, particle concentration should be decreased in order to reduce total entropy generation (TEG) and to improve heat transfer rate of any given particle size. A minimal concentration of nanoparticles is required to reduce TEG when Re is maintained constant. The highest increase in TEG with nanofluids was 2.93 times that of basefluid. The optimum condition for minimum entropy generation is Re = 4000, nanoparticle size = 65 nm, volume concentration = 0.2% and mass flow rate = 0.54 kg/s.

Keywords

• nanofluid
• entropy generation
• optimization
• genetic algorithm
• DIRECT algorithm