Spatiotemporal pattern formation in nonlinear coupled reaction-diffusion systems with a mixed-type modal discontinuous Galerkin approach

TL;DR

This paper introduces a novel mixed-type modal discontinuous Galerkin method for solving nonlinear coupled reaction-diffusion systems, effectively capturing complex spatiotemporal patterns in various scientific models.

Contribution

The study develops a new DG scheme with a mixed formulation and a novel reaction term treatment, improving accuracy and stability for NCRD systems.

Findings

Successfully captures complex patterns like spots, stripes, and hexagons.

Produces results comparable to existing literature.

Framework adaptable to large multi-dimensional problems.

Abstract

The nonlinear coupled reaction-diffusion (NCRD) systems are important in the formation of spatiotemporal patterns in many scientific and engineering fields, including physical and chemical processes, biology, electrochemical processes, fractals, viscoelastic materials, porous media, and many others. In this study, a mixed-type modal discontinuous Galerkin approach is developed for one- and two- dimensional NCRD systems, including linear, Gray-Scott, Brusselator, isothermal chemical, and Schnakenberg models to yield the spatiotemporal patterns. These models essentially represent a variety of complicated natural spatiotemporal patterns such as spots, spot replication, stripes, hexagons, and so on. In this approach, a mixed-type formulation is presented to address the second-order derivatives emerging in the diffusion terms. For spatial discretization, hierarchical modal basis functions premised on the orthogonal scaled Legendre polynomials are used. Moreover, a novel reaction term treatment is proposed for the NCRD systems, demonstrating an intrinsic feature of the new DG scheme and preventing erroneous solutions due to extremely nonlinear reaction terms. The proposed approach reduces the NCRD systems into a framework of ordinary differential equations in time, which are addressed by an explicit third-order TVD Runge-Kutta algorithm. The spatiotemporal patterns generated with the present approach are very comparable to those found in the literature. This approach can readily be expanded to handle large multi-dimensional problems that come up as model equations in developed biological and chemical applications.