Abstract

We present a new coupling approach for the time advancement of multi-physics models of multiscale systems. This extends the method of E et al. (2009) [5] to deal with an arbitrary number of models. Coupling is performed asynchronously, with each model being assigned its own timestep size. This enables accurate long timescale predictions to be made at the computational cost of the short timescale simulation. We propose a method for selecting appropriate timestep sizes based on the degree of scale separation that exists between models. A number of example applications are used for testing and benchmarking, including a comparison with experimental data of a thermally driven rarefied gas flow in a micro capillary. The multiscale simulation results are in very close agreement with the experimental data, but are produced almost 50,000 times faster than from a conventionally-coupled simulation.

Highlights

  • A multi-physics description of a multiscale system is often referred to as a ‘hybrid’ model

  • The micro and macro models generally have characteristic timescales that are very different, which means that time-accurate simulations can be extremely challenging: the size of the timestep required to make the micro model stable and accurate is so small that simulations over significant macro-scale time periods are intractable

  • In this paper we extend this idea to multiscale systems comprising an arbitrary number of coupled models

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Summary

Introduction

A multi-physics description of a multiscale system is often referred to as a ‘hybrid’ model. If the system is ‘scale-separated’, a physical (as distinct from numerical) approximation can be made that enables the coupled models to advance at different rates (asynchronously) with negligible penalty on macro-scale accuracy. E et al [5] were the first to introduce and implement this concept in a time-stepping method for coupled systems, referred to in the classification of Lockerby et al [6] as a continuous asynchronous (CA) scheme (‘continuous’ since the micro and macro models advance without interruption [5]). In this paper we extend this idea to multiscale systems comprising an arbitrary number of coupled models

Extension to multi-model systems
Example 1: A simple mass–spring system
Example 2: A Lotka–Volterra system
Example 3: A lubrication system
Example 4: A Knudsen compressor
Findings
Discussion and summary
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