Optimising Al2O3–water nanofluid

Abstract

Abstract Background This study explores how nanofluids can be optimised to improve heat transfer in various applications. A genetic algorithm that finds the optimal parameter configuration to achieve the best performance is studied and applied. The research focuses on the critical factors of heat transfer coefficient and pressure drop, which determine the efficiency of nanofluid-based systems. The main body of the abstract The methodology involves artificial intelligence and multi-objective optimisation techniques. Results show that pressure drop and heat transfer coefficient have an inverse relationship. The study provides a range of optimal values for nanofluid temperature, particle size, and volume fraction. Results The results show that the temperature, particle size, and volume fraction should be high. Another variation will be small particle size and small volume fractions with fluid temperature around 80 °C. The analysis yielded the following configuration with the optimal PEC. Temperature (oC), particle size (nm), volume fraction (%), heat transfer coefficient (kW/m2K), pressure drop (Pas), and PEC were 82.6 °C, 175.26 nm, 4.75%, 792.49 kW/m2K, 29.94 Pas, and 5.01. Conclusions The research highlights the potential of Al2O3–water nanofluids to maintain pressure drop and enhance heat transfer. It contributes to understanding nanofluid optimisation and provides practical insights for designing and selecting nanofluid systems that enhance heat transfer.