Utilizing machine learning algorithms for prediction of the rheological behavior of ZnO (50%)-MWCNTs (50%)/ Ethylene glycol (20%)-water (80%) nano-refrigerant

Abstract

This paper aims to explore the utilization of machine learning techniques for the accurate prediction of rheological properties in a specific nanofluid system, ZnO(50 %)-MWCNTs (50 %)/Ethylene glycol (20 %)-water (80 %), designed for nano-refrigeration applications. The effective manipulation of the rheological behavior of nanofluids is pivotal for enhancing their heat transfer efficiency and overall performance. By harnessing the predictive power of machine learning, this study endeavors to unravel the intricate relationships governing the rheological characteristics of the nano-refrigerant, ultimately contributing to the development of advanced cooling solutions. The obtained results show that pnf of ZnO(50%)-MWCNTs (50%)/ Ethylene glycol(20%)-water (80%) nano-refrigerant is little affected by T, and even when T varies, this result does not alter much. Also, the lowest pnf occurs when it has the highest temperature and the lowest gamma and m. Finally, it was concluded that the best algorithm in terms of the Taylor diagram for pnf output is the MPR algorithm and the worst is the ECR algorithm and the pattern of gamma changes shows that the ideal value of gamma is the biggest when pnf levels fall in tandem with their growth.