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Intelligent computing for MHD radiative Von Kármán Casson nanofluid along Darcy-Fochheimer medium with activation energy

The impact of activation energy in chemical processes, heat radiations, and temperature gradients on non-Darcian steady MHD convective Casson nanofluid flows (NMHD-CCNF) over a radial elongated circular cylinder is investigated in this study. The network of partial differential equations (PDEs) for...

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Autores principales: Raja, Muhammad Asif Zahoor, Nisar, Kottakkaran Sooppy, Shoaib, Muhammad, Abukhaled, Marwan, Riaz, Aqsa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10622628/
https://www.ncbi.nlm.nih.gov/pubmed/37928395
http://dx.doi.org/10.1016/j.heliyon.2023.e20911
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author Raja, Muhammad Asif Zahoor
Nisar, Kottakkaran Sooppy
Shoaib, Muhammad
Abukhaled, Marwan
Riaz, Aqsa
author_facet Raja, Muhammad Asif Zahoor
Nisar, Kottakkaran Sooppy
Shoaib, Muhammad
Abukhaled, Marwan
Riaz, Aqsa
author_sort Raja, Muhammad Asif Zahoor
collection PubMed
description The impact of activation energy in chemical processes, heat radiations, and temperature gradients on non-Darcian steady MHD convective Casson nanofluid flows (NMHD-CCNF) over a radial elongated circular cylinder is investigated in this study. The network of partial differential equations (PDEs) for NMHD-CCNF is developed using the modified Buongiorno framework, and the network of controlling PDEs is then transformed into ordinary differential equations (ODEs) utilizing the Von Karman method. Finally, the resulting non-linear ODEs are computed using the ND-solve approach to produce sets of data to assess the proposed model's skills, which can then be handled using the Bayesian Regularization technique of artificial neural networks (BRT-ANN). A novel stochastic computing-based application is being developed to evaluate the importance of NMHD-CCNF across a spinning disc that is radially stretched. The novelty and significance of results for better understanding, clarity, and highlighting the innovative contributions and significance of the proposed scheme. Further, to check the validity of the defined results for NMHD-CCNF, error charts, validation, and mean squared error suggestions are employed. The impact of multiple physical parameters on concentration, radial and tangential velocities, and temperature profiles is shown via tables and figures. Additionally, the results demonstrate that as the Forchheimer number, Casson nanofluid parameter, magnetic parameter, and porosity parameter are strengthened, the radial and rotational nanofluid mobility drops dramatically. The stretching parameter, on the other hand, has a parallel developmental trend. The heat generation parameter, the thermophoresis process, the thermal radiation parameter, and the Brownian motion of nanoparticles can all be increased to give thermal enhancement. On the other side, with larger estimates in thermophoresis parameters and the activation energy, there is a noticeable increase in the concentration profile.
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spelling pubmed-106226282023-11-04 Intelligent computing for MHD radiative Von Kármán Casson nanofluid along Darcy-Fochheimer medium with activation energy Raja, Muhammad Asif Zahoor Nisar, Kottakkaran Sooppy Shoaib, Muhammad Abukhaled, Marwan Riaz, Aqsa Heliyon Research Article The impact of activation energy in chemical processes, heat radiations, and temperature gradients on non-Darcian steady MHD convective Casson nanofluid flows (NMHD-CCNF) over a radial elongated circular cylinder is investigated in this study. The network of partial differential equations (PDEs) for NMHD-CCNF is developed using the modified Buongiorno framework, and the network of controlling PDEs is then transformed into ordinary differential equations (ODEs) utilizing the Von Karman method. Finally, the resulting non-linear ODEs are computed using the ND-solve approach to produce sets of data to assess the proposed model's skills, which can then be handled using the Bayesian Regularization technique of artificial neural networks (BRT-ANN). A novel stochastic computing-based application is being developed to evaluate the importance of NMHD-CCNF across a spinning disc that is radially stretched. The novelty and significance of results for better understanding, clarity, and highlighting the innovative contributions and significance of the proposed scheme. Further, to check the validity of the defined results for NMHD-CCNF, error charts, validation, and mean squared error suggestions are employed. The impact of multiple physical parameters on concentration, radial and tangential velocities, and temperature profiles is shown via tables and figures. Additionally, the results demonstrate that as the Forchheimer number, Casson nanofluid parameter, magnetic parameter, and porosity parameter are strengthened, the radial and rotational nanofluid mobility drops dramatically. The stretching parameter, on the other hand, has a parallel developmental trend. The heat generation parameter, the thermophoresis process, the thermal radiation parameter, and the Brownian motion of nanoparticles can all be increased to give thermal enhancement. On the other side, with larger estimates in thermophoresis parameters and the activation energy, there is a noticeable increase in the concentration profile. Elsevier 2023-10-13 /pmc/articles/PMC10622628/ /pubmed/37928395 http://dx.doi.org/10.1016/j.heliyon.2023.e20911 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Raja, Muhammad Asif Zahoor
Nisar, Kottakkaran Sooppy
Shoaib, Muhammad
Abukhaled, Marwan
Riaz, Aqsa
Intelligent computing for MHD radiative Von Kármán Casson nanofluid along Darcy-Fochheimer medium with activation energy
title Intelligent computing for MHD radiative Von Kármán Casson nanofluid along Darcy-Fochheimer medium with activation energy
title_full Intelligent computing for MHD radiative Von Kármán Casson nanofluid along Darcy-Fochheimer medium with activation energy
title_fullStr Intelligent computing for MHD radiative Von Kármán Casson nanofluid along Darcy-Fochheimer medium with activation energy
title_full_unstemmed Intelligent computing for MHD radiative Von Kármán Casson nanofluid along Darcy-Fochheimer medium with activation energy
title_short Intelligent computing for MHD radiative Von Kármán Casson nanofluid along Darcy-Fochheimer medium with activation energy
title_sort intelligent computing for mhd radiative von kármán casson nanofluid along darcy-fochheimer medium with activation energy
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10622628/
https://www.ncbi.nlm.nih.gov/pubmed/37928395
http://dx.doi.org/10.1016/j.heliyon.2023.e20911
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