Design of neuro-computing paradigms for nonlinear nanofluidic systems of MHD Jeffery–Hamel flow

Mehmood, A; Haq, NU; Zameer, A; Ling, SH; Raja, MAZ

Design of neuro-computing paradigms for nonlinear nanofluidic systems of MHD Jeffery–Hamel flow

Mehmood, A Haq, NU Zameer, A Ling, SH

Raja, MAZ

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Publication Type:: Journal Article
Citation:: Journal of the Taiwan Institute of Chemical Engineers, 2018, 91 pp. 57 - 85
Issue Date:: 2018-10-01

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Field	Value	Language
dc.contributor.author	Mehmood, A	en_US
dc.contributor.author	Haq, NU	en_US
dc.contributor.author	Zameer, A	en_US
dc.contributor.author	Ling, SH https://orcid.org/0000-0003-0849-5098	en_US
dc.contributor.author	Raja, MAZ	en_US
dc.date.issued	2018-10-01	en_US
dc.identifier.citation	Journal of the Taiwan Institute of Chemical Engineers, 2018, 91 pp. 57 - 85	en_US
dc.identifier.issn	1876-1070	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131384
dc.description.abstract	© 2018 Taiwan Institute of Chemical Engineers In this paper, a neuro-heuristic technique by incorporating artificial neural network models (NNMs) optimized with sequential quadratic programming (SQP) is proposed to solve the dynamics of nanofluidics system based on magneto-hydrodynamic (MHD) Jeffery–Hamel (JHF) problem involving nano-meterials. Original partial differential equations associated with MHD–JHF are transformed into third order ordinary differential equations based model. Furthermore, the transformed system has been implemented by the differential equation NNMs (DE-NNMs) which are constructed by a defined error function using log-sigmoid, radial basis and tan-sigmoid windowing kernels. The parameters of DE-NNM of nanofluidics system are optimized with SQP algorithm. To illustrate the performance of the proposed system, MHD–JHF models with base-fluid water mixed with alumina, silver and copper nanoparticles for different Hartman numbers, Reynolds numbers, angles of the channel and volume fractions with three different proposed DE-NNMs are designed to evaluate. For comparison purpose, the proposed results with reference numerical solutions of Adams solver illustrate their worth. Statistical inferences through different performance indices are given to demostrate the accuracy, stability and robustness of the stochastic solvers.	en_US
dc.relation.ispartof	Journal of the Taiwan Institute of Chemical Engineers	en_US
dc.relation.isbasedon	10.1016/j.jtice.2018.05.046	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Chemical Engineering	en_US
dc.title	Design of neuro-computing paradigms for nonlinear nanofluidic systems of MHD Jeffery–Hamel flow	en_US
dc.type	Journal Article
utslib.citation.volume	91	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0904 Chemical Engineering	en_US
utslib.for	0905 Civil Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	91	en_US

Abstract:

© 2018 Taiwan Institute of Chemical Engineers In this paper, a neuro-heuristic technique by incorporating artificial neural network models (NNMs) optimized with sequential quadratic programming (SQP) is proposed to solve the dynamics of nanofluidics system based on magneto-hydrodynamic (MHD) Jeffery–Hamel (JHF) problem involving nano-meterials. Original partial differential equations associated with MHD–JHF are transformed into third order ordinary differential equations based model. Furthermore, the transformed system has been implemented by the differential equation NNMs (DE-NNMs) which are constructed by a defined error function using log-sigmoid, radial basis and tan-sigmoid windowing kernels. The parameters of DE-NNM of nanofluidics system are optimized with SQP algorithm. To illustrate the performance of the proposed system, MHD–JHF models with base-fluid water mixed with alumina, silver and copper nanoparticles for different Hartman numbers, Reynolds numbers, angles of the channel and volume fractions with three different proposed DE-NNMs are designed to evaluate. For comparison purpose, the proposed results with reference numerical solutions of Adams solver illustrate their worth. Statistical inferences through different performance indices are given to demostrate the accuracy, stability and robustness of the stochastic solvers.

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http://hdl.handle.net/10453/131384