Particle-Based Monte-Carlo Simulations of Steady-State Mass Transport at Intermediate Péclet Numbers
International Journal of Nonlinear Sciences and Numerical Simulation
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Müller, T., Arosio, P., Rajah, L., Cohen, S., Yates, E., Vendruscolo, M., Dobson, C., & et al. (2016). Particle-Based Monte-Carlo Simulations of Steady-State Mass Transport at Intermediate Péclet Numbers. International Journal of Nonlinear Sciences and Numerical Simulation, 17 (3-4), 175-183. https://doi.org/10.1515/ijnsns-2015-0056
Conventional approaches for simulating steady-state distributions of dilute particles under diffusive and advective transport involve solving the diffusion and advection equations in at least two dimensions. Here, we present an alternative computational strategy by combining a particle-based rather than a field-based approach with the initialisation of particles in proportion to their flux. This method allows accurate prediction of the steady state and is applicable even at intermediate and high Péclet numbers (Pe>1) swhere traditional particle-based Monte-Carlo methods starting from randomly initialised particle distributions fail. We demonstrate that generating a flux of particles according to a predetermined density and velocity distribution at a single fixed time and initial location allows for accurate simulation of mass transport under flow. Specifically, upon initialisation in proportion to their flux, these particles are propagated individually and detected by summing up their Monte-Carlo trajectories in predefined detection regions. We demonstrate quantitative agreement of the predicted concentration profiles with the results of experiments performed with fluorescent particles in microfluidic channels under continuous flow. This approach is computationally advantageous and readily allows non-trivial initial distributions to be considered. In particular, this method is highly suitable for simulating advective and diffusive transport in microfluidic devices, for instance in the context of diffusive sizing.
External DOI: https://doi.org/10.1515/ijnsns-2015-0056
This record's URL: https://www.repository.cam.ac.uk/handle/1810/255123