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Chemical patterns in translating vortices: inter- and intra-cellular mixing effects
Antoine Vallatos1, Rhys Evans, Barnaby W Thompson
1School of Chemistry, University of Birmingham, Birmingham B15 2TT, United Kingdom.
Chaos (Woodbury, N.Y.)
|July 5, 2013
Summary
Stationary chemical patterns in the Belousov-Zhabotinsky reaction transition between flow distributed oscillations (FDOs) and absolute instabilities (AI). Mixing parameters in a modified Oregonator model control these pattern formation modes.
Area of Science:
- Chemical kinetics
- Nonlinear dynamics
- Fluid dynamics
Background:
- The Belousov-Zhabotinsky (BZ) reaction exhibits complex spatiotemporal patterns.
- Coupling the BZ reaction with vortex flow in a Vortex Flow Reactor generates stationary chemical patterns known as flow distributed oscillations (FDOs).
- Under certain conditions, these FDOs can become unstable, leading to pattern disappearance or complex formations.
Purpose of the Study:
- To investigate the transitions between different chemical pattern formation modes.
- To understand the mechanisms underlying the instability of flow distributed oscillations.
- To reproduce and analyze these transitions using a computational model.
Main Methods:
- Experimental study of the Belousov-Zhabotinsky reaction in a translating vortex flow.
- Development and application of a modified Oregonator model with two-zone cells connected in series.
- Systematic variation of inter-cellular and intra-cellular mixing parameters within the model.
Main Results:
- The modified Oregonator model successfully reproduced the observed transitions between pattern formation modes.
- Increased mixing between the outer zones of the model cells induced a transition from FDOs to absolute instabilities (AI).
- Increased mixing within cells, between core and outer zones, reversed this transition, shifting from AI back to FDOs.
Conclusions:
- The study demonstrates that mixing parameters are critical in controlling pattern formation modes in reaction-diffusion systems coupled with flow.
- Computational modeling provides a powerful tool for understanding the dynamics of chemical pattern transitions.
- The findings offer insights into the fundamental principles governing complex chemical oscillations and instabilities.
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