<p>The possible improvements of the Williamson fluid flow through a porous material using heat radiation and an angled magnetic field are in nuclear reactors,and solar power plants, blood flow, oil recovery, chemical reaction rates with heat transfer, and groundwater remediation with substance filtration, particularly, the main objective of this study, for understanding the parameters that enhance heat transfer in solar energy generation. The fluid model also includes velocity, temperature, and concentration slips, and the velocity is <InlineEquation ID="IEq1"> <EquationSource Format="TEX">${U}_{\text{w}}(x) = ax$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mi>U</mi> <mtext>w</mtext> </msub> <mrow> <mo stretchy="false">(</mo> <mi>x</mi> <mo stretchy="false">)</mo> </mrow> <mo>=</mo> <mi>a</mi> <mi>x</mi> </mrow> </math></EquationSource> </InlineEquation> accessible to an inclined magnetic field and the radiation effect at an angle of α over the stretching surface. Utilizing the similarity transformation modifies the partial differential equations to pertinent ordinary differential equations. The MATLAB package BVP4C algorithm is applied to determine these equations. The investigation findings demonstrated that when the velocity profile decreased as the magnetic parameters increased, the temperature distribution minimized for the Prandtl number values maximized, and the concentration profile dropped when the Lewis number levels rose. Additionally, the proposed novel work of a multiple linear regression established on machine learning facilitates the model relationship between the numerous independent physical factors of magnetic parameter, suction parameter, Weissenberg number, Prandtl number, Eckert number, slip factors, and the dependent physical quantities of skin friction, Nusselt number, and Sherwood number, with an accuracy of 95%. The sensitivity analysis study then determines which parameter has the highest influence, offering estimates for skin friction raised by 0.7%, the rate of heat transfer boosted by 0.5%, and the concentration intensification parameter to be improved by 1.8%. Finally, the accuracy and validity of the current outcome are confirmed and supported by a graph and tabular data, allowing for comparison with previous findings</p>

Multi-slip impact of Williamson fluid flow employing MLR prediction and sensitivity evaluation for solar panel installation

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发表日期：

2025年12月20日

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P. Priyadharshini,
V. Karpagam

摘要

The possible improvements of the Williamson fluid flow through a porous material using heat radiation and an angled magnetic field are in nuclear reactors,and solar power plants, blood flow, oil recovery, chemical reaction rates with heat transfer, and groundwater remediation with substance filtration, particularly, the main objective of this study, for understanding the parameters that enhance heat transfer in solar energy generation. The fluid model also includes velocity, temperature, and concentration slips, and the velocity is ${U}_{\text{w}}(x) = ax$ $U_{w} (x) = a x$ accessible to an inclined magnetic field and the radiation effect at an angle of α over the stretching surface. Utilizing the similarity transformation modifies the partial differential equations to pertinent ordinary differential equations. The MATLAB package BVP4C algorithm is applied to determine these equations. The investigation findings demonstrated that when the velocity profile decreased as the magnetic parameters increased, the temperature distribution minimized for the Prandtl number values maximized, and the concentration profile dropped when the Lewis number levels rose. Additionally, the proposed novel work of a multiple linear regression established on machine learning facilitates the model relationship between the numerous independent physical factors of magnetic parameter, suction parameter, Weissenberg number, Prandtl number, Eckert number, slip factors, and the dependent physical quantities of skin friction, Nusselt number, and Sherwood number, with an accuracy of 95%. The sensitivity analysis study then determines which parameter has the highest influence, offering estimates for skin friction raised by 0.7%, the rate of heat transfer boosted by 0.5%, and the concentration intensification parameter to be improved by 1.8%. Finally, the accuracy and validity of the current outcome are confirmed and supported by a graph and tabular data, allowing for comparison with previous findings