Stochastic neuro-swarming intelligence paradigm for the analysis of magneto-hydrodynamic Prandtl-Eyring fluid flow with diffusive magnetic layers effect over an elongated surface  

作　　者：Zeeshan Ikram Butt Iftikhar Ahmad Muhammad Shoaib Syed Ibrar Hussain Hira Ilyas Muhammad Asif Zahoor Raja 

机构地区：[1]Department of Mathematics,University of Gujrat,Gujrat 50700,Pakistan [2]Yuan Ze University,AI Center,Taoyuan,Taiwan 320,China [3]Department of Mathematics and Computer Science,University of Palermo,Via Archirafi 34,Palermo 90123,Italy [4]Department of Mathematics,University of Houston,Houston,TX 77204,USA [5]Department of Physical Sciences,University of Chenab,Gujrat 50700,Pakistan [6]Future Technology Research Centre,National Yunlin University of Science and Technology,Yunlin,Taiwan 64002,China

出　　处：《Chinese Journal of Chemical Engineering》2024年第10期295-311,共17页中国化学工程学报（英文版）

摘　　要：In recent years,the integration of stochastic techniques,especially those based on artificial neural networks,has emerged as a pivotal advancement in the field of computational fluid dynamics.These techniques offer a powerful framework for the analysis of complex fluid flow phenomena and address the uncertainties inherent in fluid dynamics systems.Following this trend,the current investigation portrays the design and construction of an important technique named swarming optimized neuroheuristic intelligence with the competency of artificial neural networks to analyze nonlinear viscoelastic magneto-hydrodynamic Prandtl-Eyring fluid flow model,with diffusive magnetic layers effect along an extended sheet.The currently designed computational technique is established using inverse multiquadric radial basis activation function through the hybridization of a well-known global searching technique of particle swarm optimization and sequential quadratic programming,a technique capable of rapid convergence locally.The most appropriate scaling group involved transformations that are implemented on governing equations of the suggested fluidic model to convert it from a system of nonlinear partial differential equations into a dimensionless form of a third-order nonlinear ordinary differential equation.The transformed/reduced fluid flow model is solved for sundry variations of physical quantities using the designed scheme and outcomes are matched consistently with Adam's numerical technique with negligible magnitude of absolute errors and mean square errors.Moreover,it is revealed that the velocity of the fluid depreciates in the presence of a strong magnetic field effect.The efficacy of the designed solver is depicted evidently through rigorous statistical observations via exhaustive numerical experimentation of the fluidic problem.

关 键 词：PrandtleEyring fluid Particle swarm optimization HYDRODYNAMIC Neural networks Computational fluid dynamics 

分 类 号：TB126[理学—工程力学] TP18[理学—力学]