Applying artificial neural network and response surface method to forecast the rheological behavior of hybrid nano‐antifreeze containing graphene oxide and copper oxide nanomaterials

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dc.contributor.author Melaibari, Ammar A.
dc.contributor.author Khetib, Yacine
dc.contributor.author Alanazi, Abdullah K.
dc.contributor.author Sajadi, S. Mohammad
dc.contributor.author Sharifpur, Mohsen
dc.contributor.author Cheraghian, Goshtasp
dc.date.accessioned 2022-09-21T06:54:43Z
dc.date.available 2022-09-21T06:54:43Z
dc.date.issued 2021-10-18
dc.description.abstract In this study, the efficacy of loading graphene oxide and copper oxide nanoparticles into ethylene glycol-water on viscosity was assessed by applying two numerical techniques. The first technique employed the response surface methodology based on the design of experiments, while in the second technique, artificial intelligence algorithms were implemented to estimate the GO-CuO/water-EG hybrid nanofluid viscosity. The nanofluid sample’s behavior at 0.1, 0.2, and 0.4 vol.% is in agreement with the Newtonian behavior of the base fluid, but loading more nanoparticles conforms with the behavior of the fluid with non-Newtonian classification. Considering the possibility of non-Newtonian behavior of nanofluid temperature, shear rate and volume fraction were effective on the target variable and were defined in the implementation of both techniques. Considering two constraints (i.e., the maximum R-square value and the minimum mean square error), the best neural network and suitable polynomial were selected. Finally, a comparison was made between the two techniques to evaluate their potential in viscosity estimation. Statistical considerations proved that the R-squared for ANN and RSM techniques could reach 0.995 and 0.944, respectively, which is an indication of the superiority of the ANN technique to the RSM one. en_US
dc.description.department Mechanical and Aeronautical Engineering en_US
dc.description.librarian dm2022 en_US
dc.description.uri https://www.mdpi.com/journal/sustainability en_US
dc.identifier.citation Melaibari, A.A.; Khetib, Y.; Alanazi, A.K.; Sajadi, S.M.; Sharifpur, M.; Cheraghian, G. Applying Artificial Neural Network and Response Surface Method to Forecast the Rheological Behavior of Hybrid Nano-Antifreeze Containing Graphene Oxide and Copper Oxide Nanomaterials. Sustainability 2021, 13, 11505. https://doi.org/10.3390/su132011505. en_US
dc.identifier.issn 2071-1050 (online)
dc.identifier.other 10.3390/su132011505
dc.identifier.uri https://repository.up.ac.za/handle/2263/87249
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.rights © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. en_US
dc.subject Hybrid nanofluid en_US
dc.subject Viscosity en_US
dc.subject Artificial neural network (ANN) en_US
dc.subject Response surface methodology (RSM) en_US
dc.subject.other Engineering, built environment and information technology articles SDG-04
dc.subject.other
dc.subject.other Engineering, built environment and information technology articles SDG-07
dc.subject.other SDG-04: Quality education
dc.subject.other Engineering, built environment and information technology articles SDG-09
dc.subject.other SDG-09: Industry, innovation and infrastructure
dc.subject.other Engineering, built environment and information technology articles SDG-12
dc.subject.other SDG-12: Responsible consumption and production
dc.subject.other Engineering, built environment and information technology articles SDG-13
dc.subject.other SDG-13: Climate action
dc.title Applying artificial neural network and response surface method to forecast the rheological behavior of hybrid nano‐antifreeze containing graphene oxide and copper oxide nanomaterials en_US
dc.type Article en_US


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