Experimental Analysis of Al2O3-SiO2-TiO2 Nanocoolant in Engine Cooling Systems and Performance Optimisation Via Machine Learning Approaches

Abstract

The present study investigates the effect of hybrid nanoparticle-added nanocoolant containing Al2O3-SiO2-titanium dioxide (TiO2) at different ratios on engine cooling system performance. The investigation involved the addition of hybrid nanoparticles to water and anti-freeze water at varying ratios, with the objective of examining their impact on the performance of engine cooling systems in terms of heat transfer. The optimal mixing ratio was then ascertained through the utilisation of a model constructed with artificial intelligence. The optimal outcome derived from the sample code of NF3 has demonstrated a substantial enhancement in the performance of an engine load of 8.8 kW, exhibiting an enhancement rate of up to 59.9% in comparison with water. The most effective mixture of Al2O3, SiO2 and TiO2 was determined through the utilisation of machine learning (ML) algorithms, resulting in an enhancement of 33% in operational conditions, with the incorporation of 0.24% Al2O3, 0.26% SiO2 and 0.25% TiO2 nanoparticle additives. In conclusion, the enhancement in heat transfer and engine fuel consumption was evaluated within the framework of the determined criteria and economic analysis. The payback (PB) period was determined to be 13 months.