239
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Numerical analysis of a fully discrete stabilized FEM for system of singularly perturbed parabolic IBVPs

ORCID Icon & ORCID Icon
Pages 1595-1616 | Received 06 Jun 2021, Accepted 09 Oct 2021, Published online: 11 Nov 2021
 

Abstract

In this article, a fully discrete numerical method is studied to solve the system of singularly perturbed parabolic convection–diffusion problem. The full discretization incorporates the backward-Euler method and the streamline-diffusion finite element method (SDFEM) for the temporal and spatial discretization, respectively. In each equation, the highest ordered spatial derivatives are multiplied by distinct parameters, hence the overlapping boundary layers phenomena displayed in the solution. To capture the singularity of the solution, a layer-adapted mesh has been used for the spatial domain discretization. The stability of the numerical method has been addressed by considering an appropriate stabilization parameter. A complete error analysis has shown the first order of accuracy of the fully discrete method in the discrete L2(0,T;SD)-norm. Lastly, few numerical experiments have been addressed to justify the theoretical estimates.

2020 AMS Subject Classifications:

Acknowledgments

The authors wish to acknowledge the referees for their valuable comments and suggestions, which helped to improve the presentation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,129.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.