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A numerical investigation of singularly perturbed 2D parabolic convection–diffusion problems of delayed type based on the theory of reproducing kernels

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Abstract

Studying convection–diffusion problems of delayed type in physics helps us to understand transport phenomena and has practical applications in various fields. The mathematical analysis of this model has practical applications in various fields, such as flow dynamics, material science, and environmental modeling. In this paper, the theory of reproducing kernel spaces (RKS) is utilized to solve singularly perturbed 2D parabolic convection–diffusion problems of delayed type. To this end, a series form for the solution is first constructed in reproducing kernel Hilbert space, and then, the approximate solution is given as an \(\mathcal {N}\)-term summation. The main contribution of the present research is that, for the first time, a novel formula is found for the homogenization of 2D initial-boundary-value problems. Furthermore, a semi-analytical RKS method is employed without employing the Gram–Schmidt orthogonalization algorithm. We derive theorems to reveal stability and convergence properties which are examined by numerical experiments. The technique is especially suited for problems having boundary-layer behavior. Numerical results are provided to demonstrate the efficiency, stability, and superiority of the proposed technique.

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Authors and Affiliations

  1. Department of Mathematics, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran

    Parisa Ahmadi Balootaki & Majid Tavassoli Kajani

  2. Department of Mathematics, Faculty of Mathematical Science, Shahrekord University, Shahrekord, Iran

    Reza Khoshsiar Ghaziani & Mojtaba Fardi

Authors
  1. Parisa Ahmadi Balootaki
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  2. Reza Khoshsiar Ghaziani
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  3. Mojtaba Fardi
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  4. Majid Tavassoli Kajani
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Correspondence to Mojtaba Fardi.

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Balootaki, P.A., Ghaziani, R.K., Fardi, M. et al. A numerical investigation of singularly perturbed 2D parabolic convection–diffusion problems of delayed type based on the theory of reproducing kernels. Soft Comput (2024). https://doi.org/10.1007/s00500-023-09573-z

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