Abstract
Nanofluids (NFs) are another achievement of nanoscience that has received much attention in recent years. The properties of the base fluid are improved when MWCNT nanoparticles (NPs) are added, according to research. Due to their high efficiency, reliability, and flexibility, dynamic viscosity (μnf) is one of the crucial properties of NFs that affect heat transfer and surface tension. Due to their intelligent structure, artificial neural networks (ANNs), a novel modeling technique, compete with traditional statistical modeling. The correlation of μnf to three input parameters for MWCNT-ZnO/SAE 5W30 NF was studied, and input-output model was attained via modeling, and training an ANN. This ANN achieved R2 and MSE values of 0.9999 and 0.00010349, respectively, demonstrating the ANN's successful training. In this study, μnfshows a strong inverse effect with temperature and shear rate (SR). In other words, the temperature increases from 5 to 55 oC almost reduces μnffrom 370 cP to around 30 cP, and SR increases from 50 to 1000 rpm almost reduces μnffrom 400 cP to 25 cP. Additionally, the solid volume fraction (SVF) affected μnf directly although insignificantly.
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