Stochastic simulations of the Schnakenberg model with spatial inhomogeneities using reactive multiparticle collision dynamics

A numerically efficient globally averaged number density approach is used to simulate a reaction-diffusion system using a particle-based stochastic simulation algorithm called reactive multiparticle collision (RMPC) dynamics. Constant diffusivity of the particles is achieved through a time-varying r...

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Veröffentlicht in:AIMS mathematics Jg. 4; H. 6; S. 1805 - 1823
Hauptverfasser: Sayyidmousavi, Alireza, Rohlf, Katrin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: AIMS Press 01.01.2019
Schlagworte:
reaction-diffusion
reactive multiparticle collision dynamics
schnakenberg model
stochastic simulations
ISSN:2473-6988, 2473-6988
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Zusammenfassung:A numerically efficient globally averaged number density approach is used to simulate a reaction-diffusion system using a particle-based stochastic simulation algorithm called reactive multiparticle collision (RMPC) dynamics. Constant diffusivity of the particles is achieved through a time-varying rotation angle (also called collision angle). Variation in the diffusion coefficient between two different chemical species is achieved in one of two ways: (i) using a different kBT/m value for one species compared to the other, or (ii) using the same kBT/m value for both species, but using a different probability to free-stream for one species compared to another. For smaller diffusivities and larger spatial inhomogeneities, bath particles were necessary for the model to agree with the PDE solution. The latter approach was further used without a bath, and shown to be capable of producing Turing patterns after long simulation times. The significance of our work is that RMPC can serve as a feasible simulation tool for both short and long-term simulations, can handle spatial inhomogeneities, can model a fairly large range of diffusivities in a reaction-diffusion scenario, and is capable of producing Turing patterns. An advantage of this method includes more detailed system information in feasible simulation times.
ISSN:2473-6988
2473-6988
DOI:10.3934/math.2019.6.1805