Modelling the effects of mixing ratio and temperature on the thermal conductivity of GNP-Alumina hybrid nanofluids: A comparison of ANN, RSM, and linear regression methods

Modelling the effects of mixing ratio and temperature on the thermal conductivity of GNP-Alumina hybrid nanofluids: A comparison of ANN, RSM, and linear regression methods

This research aimed to evaluate and compare the efficacy of three distinct methods for forecasting the thermal conductivity of GNP-Alumina hybrid nanofluids. The methods under consideration were artificial neural network (ANN), response surface methodology (RSM), and linear regression (LR). The pred...

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Bibliographic Details
Main Authors: Adeola Borode, Peter Olubambi
Format: Article
Language:English
Published: Elsevier 2023-08-01
Series:Heliyon
Subjects:
Hybrid nanofluid
Graphene nanoplatelet
Alumina: artificial neural network
Response surface methodology
Regression
Thermal conductivity
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844023064368

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http://www.sciencedirect.com/science/article/pii/S2405844023064368

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