Regression analysis for thermal transport of fractional-order magnetohydrodynamic Maxwell fluid flow under the influence of chemical reaction using integrated machine learning approach

IF 5.3 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Waqar Ul Hassan, Khurram Shabbir, Ahmed Zeeshan, Rahmat Ellahi
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引用次数: 0

Abstract

An innovative idea of regression analysis based on machine learning technique for magnetohydrodynamic flow of Maxwell fluid within a cylinder is proposed. Mean Squared Error is used for the simulation of heat transfer and fluid flow. The governing flow equations involving a system of coupled, nonlinear fractional partial differential equations are solved by homotopic approach called HPM. The predicted solution is obtained with Python built-in code on Google-Colab. The effects of Atangana-Baleanu fractional time order derivative on the momentum, thermal, and concentration boundary layer are analyzed. It is observed that the momentum boundary layer gets higher and higher by increasing the values of Atangana-Baleanu fractional time order derivative. The thermal boundary layer shows improvement with the increasing value of the Peclet number. The concentration boundary layer thickness declines with the growing values of chemical reactions. The validation of results is examined by MSE, function fit, and correlation index.
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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