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

Muhammad Asif Zahoor Raja; Kottakkaran Sooppy Nisar; Muhammad Shoaib; Marwan Abukhaled; Aqsa Riaz

doi:10.1016/j.heliyon.2023.e20911

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

Heliyon. 2023 Oct 13;9(10):e20911. doi: 10.1016/j.heliyon.2023.e20911. eCollection 2023 Oct.

Authors

Muhammad Asif Zahoor Raja¹, Kottakkaran Sooppy Nisar^{2

3}, Muhammad Shoaib⁴, Marwan Abukhaled⁵, Aqsa Riaz⁶

Affiliations

¹ Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section .3, Douliou, Yunlin, 64002, Taiwan, ROC.
² Department of Mathematics, College of Science and Humanities in Alkharj, Prince Sattam bin Abdulaziz University, 11942, Saudi Arabia.
³ School of Technology, Woxsen University, Hyderabad, 502345, Telangana State, India.
⁴ Yuan Ze University, AI Center, Taoyuan, 320, Taiwan, ROC.
⁵ Department of Mathematics and Statistics, American University of Sharjah, Sharjah, United Arab Emirates.
⁶ Department of Mathematics, University of Wah, Pakistan.

Abstract

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.

Keywords: Activation energy; Bayesian Regularization technique; MHD convective Casson nanofluid; Radially extended rotating disc; Von Karman's assertion.