Staff View: An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM)

An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM)

Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanopartic...

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Main Authors:	Yaw C.T., Koh S.P., Sandhya M., Ramasamy D., Kadirgama K., Benedict F., Ali K., Tiong S.K., Abdalla A.N., Chong K.H.
Other Authors:	36560884300
Format:	Article
Published:	MDPI 2024
Subjects:	central composite design CNC coefficients correlation energy glycol-based graphene nanoplatelets heat transfer hybrid nanofluid response surface methodology
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spelling	my.uniten.dspace-341692024-10-14T11:18:15Z An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM) Yaw C.T. Koh S.P. Sandhya M. Ramasamy D. Kadirgama K. Benedict F. Ali K. Tiong S.K. Abdalla A.N. Chong K.H. 36560884300 22951210700 57211782885 26325891500 12761486500 57194591957 36130958600 15128307800 25646071000 36994481200 central composite design CNC coefficients correlation energy glycol-based graphene nanoplatelets heat transfer hybrid nanofluid response surface methodology Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 �C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions. � 2023 by the authors. Final 2024-10-14T03:18:15Z 2024-10-14T03:18:15Z 2023 Article 10.3390/nano13101596 2-s2.0-85160574875 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160574875&doi=10.3390%2fnano13101596&partnerID=40&md5=0e60639c58131138f5353e476a865c29 https://irepository.uniten.edu.my/handle/123456789/34169 13 10 1596 All Open Access Gold Open Access MDPI Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	central composite design CNC coefficients correlation energy glycol-based graphene nanoplatelets heat transfer hybrid nanofluid response surface methodology
spellingShingle	central composite design CNC coefficients correlation energy glycol-based graphene nanoplatelets heat transfer hybrid nanofluid response surface methodology Yaw C.T. Koh S.P. Sandhya M. Ramasamy D. Kadirgama K. Benedict F. Ali K. Tiong S.K. Abdalla A.N. Chong K.H. An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM)
description	Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 �C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions. � 2023 by the authors.
author2	36560884300
author_facet	36560884300 Yaw C.T. Koh S.P. Sandhya M. Ramasamy D. Kadirgama K. Benedict F. Ali K. Tiong S.K. Abdalla A.N. Chong K.H.
format	Article
author	Yaw C.T. Koh S.P. Sandhya M. Ramasamy D. Kadirgama K. Benedict F. Ali K. Tiong S.K. Abdalla A.N. Chong K.H.
author_sort	Yaw C.T.
title	An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM)
title_short	An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM)
title_full	An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM)
title_fullStr	An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM)
title_full_unstemmed	An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM)
title_sort	approach for the optimization of thermal conductivity and viscosity of hybrid (graphene nanoplatelets, gnps: cellulose nanocrystal, cnc) nanofluids using response surface methodology (rsm)
publisher	MDPI
publishDate	2024
_version_	1814061169070047232
score	13.222552

An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM)

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