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• W3162135500 abstract "This study challenges the capabilities of artificial neural network (ANN) as well as response surface methodology (RSM) in estimating the viscosity (μ) and thermal conductivity (k) of β − SiC/EG and β − SiC/PG nanofluids. The implementation of these methods is performed by defining statistical criteria with a great obsession to forecast viscosity ratio as well as thermal conductivity ratio with the least error. The nanoparticle of β − SiC has a bigger effect on μEG and μPG so that they increase up to 78.71% and 56%, while kEG and kPG improve under the best conditions up to 14.64% and 4.85%, respectively. Statistical calculations show that for RSM, R-square is 0.987forμβ−SiC/EGμEG, 0.975 μβ−SiC/PGμPG, 0.99 kβ−SiC/EGkEG and 0.987 kβ−SiC/PGkPG. This figure for ANN is 0.994, 0.992, 0.994 and 0.991. This implies that ANN ability is superior. Although β − SiC nanoparticles increase the viscosity by 78.7% (for EG) and 56% (for PG) which is not desirable, fortunately, fluid mechanics-based calculations reveal that in turbulent regime, the pumping power increases by only 17.2% and 13.5% under worst conditions." @default.
• W3162135500 created "2021-05-24" @default.
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• W3162135500 date "2021-09-01" @default.
• W3162135500 modified "2023-10-17" @default.
• W3162135500 title "Challenging ANN and RSM approaches to forecast β-SiC nanoparticles efficacy on performance of liquid ethylene glycol and propylene glycol" @default.
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• W3162135500 doi "https://doi.org/10.1016/j.powtec.2021.05.021" @default.
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