Bayesian regularization networks for micropolar ternary hybrid nanofluid flow of blood with homogeneous and heterogeneous reactions: Entropy generation optimization
This study aims to analyze a Bayesian regularization backpropagation algorithm for micropolar ternary hybrid nanofluid flow over curved surfaces with homogeneous and heterogeneous reactions, Joule heating and viscous dissipation. The ternary hybrid nanofluid consists of nanoparticles of titanium oxi...
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| Vydané v: | Alexandria engineering journal Ročník 77; s. 127 - 148 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
15.08.2023
Elsevier |
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| ISSN: | 1110-0168 |
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| Abstract | This study aims to analyze a Bayesian regularization backpropagation algorithm for micropolar ternary hybrid nanofluid flow over curved surfaces with homogeneous and heterogeneous reactions, Joule heating and viscous dissipation. The ternary hybrid nanofluid consists of nanoparticles of titanium oxide (TiO2), copper oxide (CuO), and silicon oxide (SiO2), with blood as the base fluid. The governing partial differential equations for the fluid flow are converted into ordinary differential equations using a group of self-similar transformations. The ordinary differential equations are solved using an appropriate shooting algorithm in MATLAB. The effects of physical parameters including curvature, micro-polar, radiation, magnetic, Prandtl, Eckert, Schmidt, and homogeneous and heterogeneous chemical reaction parameters are analyzed for velocity, micro rotational, temperature, and concentration profile. Physical quantities of engineering interest like heat transfer rate, mass transfer rate, skin friction coefficient, couple stress coefficient, and entropy generation are also discussed in this study. A Bayesian regularization backpropagation algorithm is also designed for the solution of the ordinary differential equations. The obtained network is analyzed using training state, performance, error histograms, model response, Error autocorrelation, and input-error correlation plots. It is observed that the entropy generation and the Bejan number increase for enhancing Brinkman and radiation parameter. Clinical researchers and biologists may use the results of this computational study to forecast endothelial cell damage and plaque deposition in curved arteries, by which the severity of these conditions can be reduced. |
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| AbstractList | This study aims to analyze a Bayesian regularization backpropagation algorithm for micropolar ternary hybrid nanofluid flow over curved surfaces with homogeneous and heterogeneous reactions, Joule heating and viscous dissipation. The ternary hybrid nanofluid consists of nanoparticles of titanium oxide (TiO2), copper oxide (CuO), and silicon oxide (SiO2), with blood as the base fluid. The governing partial differential equations for the fluid flow are converted into ordinary differential equations using a group of self-similar transformations. The ordinary differential equations are solved using an appropriate shooting algorithm in MATLAB. The effects of physical parameters including curvature, micro-polar, radiation, magnetic, Prandtl, Eckert, Schmidt, and homogeneous and heterogeneous chemical reaction parameters are analyzed for velocity, micro rotational, temperature, and concentration profile. Physical quantities of engineering interest like heat transfer rate, mass transfer rate, skin friction coefficient, couple stress coefficient, and entropy generation are also discussed in this study. A Bayesian regularization backpropagation algorithm is also designed for the solution of the ordinary differential equations. The obtained network is analyzed using training state, performance, error histograms, model response, Error autocorrelation, and input-error correlation plots. It is observed that the entropy generation and the Bejan number increase for enhancing Brinkman and radiation parameter. Clinical researchers and biologists may use the results of this computational study to forecast endothelial cell damage and plaque deposition in curved arteries, by which the severity of these conditions can be reduced. |
| Author | Sharma, Parikshit Fernandez-Gamiz, Unai Sharma, Bhupendra K. Mishra, Nidhish K. Noeiaghdam, Samad |
| Author_xml | – sequence: 1 givenname: Bhupendra K. surname: Sharma fullname: Sharma, Bhupendra K. email: bhupen_1402@yahoo.co.in organization: Department of Mathematics, Birla Institute of Technology and Science, Pilani, India – sequence: 2 givenname: Parikshit orcidid: 0000-0001-5471-6547 surname: Sharma fullname: Sharma, Parikshit email: parikshitsharma2001@gmail.com organization: Department of Mathematics, Birla Institute of Technology and Science, Pilani, India – sequence: 3 givenname: Nidhish K. surname: Mishra fullname: Mishra, Nidhish K. email: n.kumar@seu.edu.sa organization: Department of Basic Sciences, College of Sciences and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi Arabia – sequence: 4 givenname: Samad orcidid: 0000-0002-2307-0891 surname: Noeiaghdam fullname: Noeiaghdam, Samad email: snoei@istu.edu organization: Industrial Mathematics Laboratory, Baikal School of BRICS, Irkutsk National Research Technical University, Irkutsk 664074, Russia – sequence: 5 givenname: Unai orcidid: 0000-0001-9194-2009 surname: Fernandez-Gamiz fullname: Fernandez-Gamiz, Unai email: unai.fernandez@ehu.eus organization: Nuclear Engineering and Fluid Mechanics Department, University of the Basque Country UPV/EHU, Nieves Cano 12, 01006 Vitoria-Gasteiz, Spain |
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| Keywords | Curved artery Bayesian regularization backpropagation algorithm Ternary hybrid nanofluid Homogeneous and heterogeneous chemical reactions |
| Language | English |
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| Title | Bayesian regularization networks for micropolar ternary hybrid nanofluid flow of blood with homogeneous and heterogeneous reactions: Entropy generation optimization |
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