Soft computing paradigm for Ferrofluid by exponentially stretched surface in the presence of magnetic dipole and heat transfer

Alexandria Engineering Journal

卷:61

Poom Kumam¹  Saeed Islam²  Zahir Shah³  Imrana Farhat⁴  Muhammad Shoaib⁴  Muhammad Asif Zahoor Raja⁵ 

[1] Center of Excellence in Theoretical and Computational Science (TaCS-CoE) &

[2] KMUTTFixed Point Research Laboratory, Room SCL 802 Fixed Point Laboratory, Science Laboratory Building, Departments of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi (KMUTT), 126 Pracha-Uthit Road, Bang Mod, Thung Khru, Bangkok 10140, Thailand;

[3] Department of Mathematical Sciences, University of Lakki Marwat, Lakki Marwat 28420, Khyber Pakhtunkhwa, Pakistan;

[4] Department of Mathematics, COMSATS University Islamabad, Attock Campus, Pakistan;

[5] Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section .3, Douliou, Yunlin 64002, Taiwan, ROC;

关键词: Ferrofluid flow;    Magnetic dipole;    Numerical computation;    Backpropagated neural network;    Levenberg Marquardt Algorithm;   

DOI  :  

来源: DOAJ

【 摘 要 】

In the presented research article, the intelligence based numerical computation of artificial neural network backpropagated with Levenberg-Marquardt algorithm has been developed to analyze the novel ferrofluid flow model in the presence of magnetic dipole. Heat transfer effects are also incorporated along the horizontal. The designed fluid flow model initially represented by system of partial differential equations are converted into system of non-linear ordinary differential equations through suitable similarity transformations. The reference dataset of the possible outcomes is obtained from Adam numerical solver for the different scenarios of flow model by variation of co-efficient of the thermal expansion, Eckert number, suction parameter, magnetization and radiation parameter. The approximated solutions are interpreted for designed model by testing, training and validation process of backpropagated neural networks. Furthermore, the comparative studies and performance analysis of used algorithm is validated through regression analysis, histogram studies, correlation index and results of mean square error.

【 授权许可】

Unknown