Analysis of a diffusive chemical reaction model in three space dimensions

Abstract

This article proposes an implicit operator splitting nonstandard finite difference (OS-NSFD) scheme for numerical treatment of two species in three space dimensions reaction-diffusion glycolysis model. Since, the unknown state variables exhibiting the concentrations of species in glycolysis models and they cannot be negative and obtaining their positive solutions is a challenging task. The established theoretical result ensures that our proposed OS-NSFD scheme is unconditionally convergent at equilibrium point and fulfills the condition of positivity of solutions on contrary to other methods. Further, we analyze the existence and uniqueness of the solution obtained for the underlying system. To highlight the effectiveness of OS-NSFD scheme we compare the simulation results of OS-NSFD scheme with three well-known existing operators splitting finite difference (FD) schemes, namely, forward Euler explicit, backward Euler implicit and Crank Nicolson splitting schemes. Many existing techniques provide with the restricted positive solutions which do not work always. These techniques are only applicable if certain conditions on the discretized parameters are considered otherwise; they produce negative solutions, which is not the physical feature of the real system. The current work bridges this gap by catering the unconditional positive solutions to the reaction diffusion models.

Keywords:

• Glycolysis model
• nonstandard finite difference scheme
• operator splitting
• positive solutions
• reaction-diffusion system

Acknowledgments

The authors extend their appreciation to Taif University, Saudi Arabia, for supporting this work through project number (TU-DSPP-2024-47).

Author contributions

conceptualization, N.A. ; methodology, M.A.R; software, N.A., A. A., and I.K.; validation, J.A., I.K.; formal analysis, N.A., J. A. and A.A; investigation, M.A.R; writing--original draft preparation, N.A. J.A.; writing--review and editing, J. A., M. A. and I.K.; visualization, N.A. and M.R. and M.A.R.; supervision, N.A., I.K. and M.A.R; Y.S.H and A.F.A; revision.

Disclosure statement

The authors declare no conflict of interest.

Additional information

Funding

This research was funded by Taif University, Saudi Arabia, Project No. (TU-DSPP-2024-47).