Rheological Behavior of SAE50 Oil–SnO2–CeO2 Hybrid Nanofluid: Experimental Investigation and Modeling Utilizing Response Surface Method and Machine Learning Techniques

Nanoscale Research Letters

Research

Mahdi Malekiyan¹  Mohammad Lotfalipour¹  Mojtaba Sepehrnia²  Mahsa Karimi³  Somayeh Davoodabadi Farahani⁴ 

[1] Department of Mechanical Engineering, Shahabdanesh University, Qom, Iran;Department of Mechanical Engineering, Shahabdanesh University, Qom, Iran;Department of Mechanical Engineering, Technical and Vocational University, Qom, Iran;Faculty of Mechanical Engineering, University of Kashan, Kashan, Iran;School of Mechanical Engineering, Arak University of Technology, Arak, Iran;

关键词: Experimental study;    Hybrid nanofluid;    Cerium oxide–tin oxide;    RSM;    ANFIS;    Machine learning;   

DOI  :  10.1186/s11671-022-03756-7

 received in 2022-08-21, accepted in 2022-11-25,  发布年份 2022

来源: Springer

【 摘 要 】

In this study, for the first time, the effects of temperature and nanopowder volume fraction (NPSVF) on the viscosity and the rheological behavior of SAE50–SnO2–CeO2 hybrid nanofluid have been studied experimentally. Nanofluids in NPSVFs of 0.25% to 1.5% have been made by a two-step method. Experiments have been performed at temperatures of 25 to 67 °C and shear rates (SRs) of 1333 to 2932.6 s−1. The results revealed that for base fluid and nanofluid, shear stress increases with increasing SR and decreasing temperature. By increasing the temperature to about 42 °C at a NPSVF of 1.5%, about 89.36% reduction in viscosity is observed. The viscosity increases with increasing NPSVF about 37.18% at 25 °C. In all states, a non-Newtonian pseudo-plastic behavior has been observed for the base fluid and nanofluid. The highest relative viscosity occurs for NPSVF = 1.5%, temperature = 25 °C and SR = 2932.6 s−1, which increases the viscosity by 37.18% compared to the base fluid. The sensitivity analysis indicated that the highest sensitivity is related to temperature and the lowest sensitivity is related to SR. Response surface method, curve fitting method, adaptive neuro-fuzzy inference system and Gaussian process regression (GPR) have been used to predict the dynamic viscosity. Based on the results, all four models can predict the dynamic viscosity. However, the GPR model has better performance than the other models.

【 授权许可】

CC BY   
© The Author(s) 2022

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