Energy and exergy analysis of ternary nanofluid for electric vehicle coolant through invasive weed optimisation algorithm—a numerical study

Abstract

The present work explores a novel water-ethylene glycol-based ternary hybrid nanofluid, comprising aluminium oxide (Al₂O₃), zinc oxide (ZnO), and reduced graphene oxide (rGO) for electric vehicle traction system radiator coolant. A multi-objective hybrid optimisation technique is used to determine the optimal composition of nanofluid and operating conditions of the radiator. Design of experiments based on response surface methodology is used to develop regression models for performance parameters such as Peclet number, normalised Nusselt number, Bejan number, and second law efficiency. Further, optimised operating conditions and composition of nanofluid were derived using invasive weed optimisation (IWO) algorithm for the best performance of radiator. A multiphase numerical analysis is performed using Ansys® Fluent’s k-ɛ turbulence model to evaluate the performance parameters. Additionally, an experimental study is conducted to understand the degree of consensus with the developed numerical model. Based on optimisation, a ternary hybrid nanofluid concentration of 1.91% with 3.33: 3.94: 3.65 proportion of Al₂O₃: ZnO: rGO is identified to be significant at coolant and air inlet conditions of 98.61 °C & 13.96 LPM and 33.27 °C & 1108.6 CFM, respectively. The optimised performance metrics achieved through IWO indicate a Peclet number of 3511, normalised Nusselt number of 1.85 with Bejan number of 0.82 and a second law efficiency of 0.73.