143
Views
2
CrossRef citations to date
0
Altmetric
Research Articles

Numerical method for solving the fractional evolutionary model of bi-flux diffusion processes

, &
Pages 880-900 | Received 11 Oct 2022, Accepted 25 Dec 2022, Published online: 06 Jan 2023
 

Abstract

In this paper, based on the nonuniform time meshes, we proposed an efficient difference scheme for solving the time-fractional bi-flux diffusion equation. By the energy method, we then proved that the present scheme is unconditionally stable and convergent in L2 norm. Several numerical examples are given to verify the theoretical results. In addition, numerical simulations show the order of fractional derivative affects the diffusion rate of the particles.

2010 AMS SUBJECT CLASSIFICATIONS:

Acknowledgments

The authors are deeply grateful to the anonymous reviewers for their valuable comments and suggestions which greatly enhance the quality of this manuscript.

Disclosure statement

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

Additional information

Funding

Cui-cui Ji was partially supported by National Natural Science Foundation of China [grant number 12001307] and Natural Science Foundation of Shandong Province [grant numbers ZR2020QA033, ZR2021MA072]. Maosheng Jiang was partially supported by National Natural Science Foundation of China [grant number 12071046], Natural Science Foundation of Shandong Province [grant number ZR2021QA018] and China Postdoctoral Science Foundation [grant number 2022M721757].

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.