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Article: Optimization of laminar convective heat transfer of oil-in-water nanoemulsion fluids in a toroidal duct

TitleOptimization of laminar convective heat transfer of oil-in-water nanoemulsion fluids in a toroidal duct
Authors
KeywordsNanoemulsions
Convective heat transfer
Two-phase approach
Optimization
Toroidal duct
Issue Date2020
PublisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/ijhmt
Citation
International Journal of Heat and Mass Transfer, 2020, v. 150, p. article no. 119332 How to Cite?
AbstractThis study presents numerical simulations, back propagation artificial neural networks and genetic algorithms for optimizing laminar convective heat transfer of oil-in-water nanoemulsion fluids having non-Fourier heat conduction characteristics. Firstly, a numerical study has been conducted on laminar flow and forced convective heat transfer of oil-in-water nanoemulsion fluids in toroidal ducts using Eulerian-Lagrangian two-phase approach. New correlations of drag coefficient, effective thermal conductivity and effective viscosity were adopted to improve the accuracy of simulation. Numerical results show that convective heat transfer can be enhanced by oil nanodroplets with thermal conductivity lower than that of the base fluid. Then regression models and artificial neural network models were developed based on simulation results for predicting convective heat transfer performances of nanoemulsions, considering effects of cross-sectional aspect ratio, Reynolds number, oil nanodroplet diameter and concentration. Artificial neural network models can predict mean Nusselt number and pressure drop better than the regression model. Finally, genetic algorithms was used to optimize convective heat transfer of nanoemulsions considering droplet migration. It can be found that low cross-sectional aspect ratio of width to height is beneficial for thermal performance factor. For single-objective optimization, mean Nusselt number reaches the maximum 32.3 at aspect ratio of 0.9677 and thermal performance factor reaches the maximum 1.305 at aspect ratio of 0.3935 under certain conditions. Pareto optimal set was obtained for two-objective optimization. This study would be useful for the optimal design of convective heat transfer of emulsions in toroidal ducts.
Persistent Identifierhttp://hdl.handle.net/10722/293680
ISSN
2019 Impact Factor: 4.947
2015 SCImago Journal Rankings: 1.749

 

DC FieldValueLanguage
dc.contributor.authorLiu, F-
dc.contributor.authorSUN, HH-
dc.contributor.authorZHANG, DX-
dc.contributor.authorChen, Q-
dc.contributor.authorZhao, J-
dc.contributor.authorWang, L-
dc.date.accessioned2020-11-23T08:20:16Z-
dc.date.available2020-11-23T08:20:16Z-
dc.date.issued2020-
dc.identifier.citationInternational Journal of Heat and Mass Transfer, 2020, v. 150, p. article no. 119332-
dc.identifier.issn0017-9310-
dc.identifier.urihttp://hdl.handle.net/10722/293680-
dc.description.abstractThis study presents numerical simulations, back propagation artificial neural networks and genetic algorithms for optimizing laminar convective heat transfer of oil-in-water nanoemulsion fluids having non-Fourier heat conduction characteristics. Firstly, a numerical study has been conducted on laminar flow and forced convective heat transfer of oil-in-water nanoemulsion fluids in toroidal ducts using Eulerian-Lagrangian two-phase approach. New correlations of drag coefficient, effective thermal conductivity and effective viscosity were adopted to improve the accuracy of simulation. Numerical results show that convective heat transfer can be enhanced by oil nanodroplets with thermal conductivity lower than that of the base fluid. Then regression models and artificial neural network models were developed based on simulation results for predicting convective heat transfer performances of nanoemulsions, considering effects of cross-sectional aspect ratio, Reynolds number, oil nanodroplet diameter and concentration. Artificial neural network models can predict mean Nusselt number and pressure drop better than the regression model. Finally, genetic algorithms was used to optimize convective heat transfer of nanoemulsions considering droplet migration. It can be found that low cross-sectional aspect ratio of width to height is beneficial for thermal performance factor. For single-objective optimization, mean Nusselt number reaches the maximum 32.3 at aspect ratio of 0.9677 and thermal performance factor reaches the maximum 1.305 at aspect ratio of 0.3935 under certain conditions. Pareto optimal set was obtained for two-objective optimization. This study would be useful for the optimal design of convective heat transfer of emulsions in toroidal ducts.-
dc.languageeng-
dc.publisherPergamon. The Journal's web site is located at http://www.elsevier.com/locate/ijhmt-
dc.relation.ispartofInternational Journal of Heat and Mass Transfer-
dc.subjectNanoemulsions-
dc.subjectConvective heat transfer-
dc.subjectTwo-phase approach-
dc.subjectOptimization-
dc.subjectToroidal duct-
dc.titleOptimization of laminar convective heat transfer of oil-in-water nanoemulsion fluids in a toroidal duct-
dc.typeArticle-
dc.identifier.emailWang, L: lqwang@hku.hk-
dc.identifier.authorityWang, L=rp00184-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.ijheatmasstransfer.2020.119332-
dc.identifier.scopuseid_2-s2.0-85078050696-
dc.identifier.hkuros319534-
dc.identifier.volume150-
dc.identifier.spagearticle no. 119332-
dc.identifier.epagearticle no. 119332-
dc.publisher.placeUnited Kingdom-

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