Large-scale multiscale particle models in inhomogeneous domains
Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
In this thesis, we develop multiscale models for particle simulations in population dynamics. These models are characterised by prescribing particle motion on two spatial scales: microscopic and macroscopic.At the microscopic level, each particle has its own mass, position and velocity, while at the macroscopic level the particles are interpolated to a continuum quantity whose evolution is governed by a system of transport equations.This way, one can prescribe various types of interactions on a global scale, whilst still maintaining high simulation speed for a large number of particles. In addition, the interplay between particle motion and interaction is well tuned in both regions of low and high densities.
We analyse links between models on these two scales and prove that under certain conditions, a system of interacting particles converges to a nonlinear coupled system of transport equations.We use this as a motivation to derive a model defined on both modelling scales and prescribe the intercommunication between them. Simulation takes place in inhomogeneous domains with arbitrary conditions at inflow and outflow boundaries. We realise this by modelling obstacles, sources and sinks.Integrating these aspects into the simulation requires a route planning algorithm for the particles. Several algorithms are considered and evaluated on accuracy, robustness and efficiency.
All aspects mentioned above are combined in a novel open source prototyping simulation framework called Mercurial. This computational framework allows the design of geometries and is built for high performance when large numbers of particles are involved. Mercurial supports various types of inhomogeneities and global systems of equations.
We apply our framework to simulate scenarios in crowd dynamics.We compare our results with test cases from literature to assess the quality of the simulations.
Place, publisher, year, edition, pages
2016. , 112 p.
, Master Industrial and Applied Mathematics (IAM), 231
crowd dynamics stochastic partial differential equations simulation numerical analysis
IdentifiersURN: urn:nbn:se:kau:diva-45862OAI: oai:DiVA.org:kau-45862DiVA: diva2:970453
Subject / course
Muntean, Adrian, prof. dr. habil.Jalba, Andrei, dr.
Master Thesis in Industrial and Applied Mathematics2016-09-152016-09-132016-09-15Bibliographically approved