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Computational analysis of mixed convection Jeffrey fluid flow between rotating discs: incorporating magnetic field and thermal radiation via neural network modeling

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Abstract

Researchers are increasingly interested in utilizing intelligent computing infrastructures to explore different fields of science and engineering, providing enhanced versions of soft computing-based methodologies for problem-solving. In this present investigation, we employ an artificial neural network utilizing the Levenberg–Marquardt technique to tackle the mixed convection flow of a Jeffrey fluid between rotating discs, subjected to a robust magnetic field and thermal radiation. The governing equations representing the given problem are converted into ordinary differential equations through appropriate mathematical transformations. The dataset required for training the backpropagation artificial neural network-based Levenberg–Marquardt technique model is generated using the spectral quasi-linearization method. The effectiveness of the proposed methodology is validated through various assessment metrics, including Mean Squared Error computation, analysis of error histograms, and regression analysis. The study explores the impact of magnetic, thermal radiation, and Jeffrey fluid parameters on velocity and temperature, presented visually for better understanding.

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Authors and Affiliations

  1. Department of Mathematics, National Institute of Technology, Warangal, Telangana, 506004, India

    Ravi Mahla & K. Kaladhar

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  1. Ravi Mahla
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Correspondence to K. Kaladhar.

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Mahla, R., Kaladhar, K. Computational analysis of mixed convection Jeffrey fluid flow between rotating discs: incorporating magnetic field and thermal radiation via neural network modeling. Eur. Phys. J. Plus 139, 344 (2024). https://doi.org/10.1140/epjp/s13360-024-05128-9

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