Langevin Particle: A Self-Adaptive Lagrangian Primitive for Flow Simulation Enhancement

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dc.contributor.authorChen, Fanen_US
dc.contributor.authorZhao, Yeen_US
dc.contributor.authorYuan, Zhien_US
dc.contributor.editorM. Chen and O. Deussenen_US
dc.date.accessioned2015-02-27T10:21:46Z
dc.date.available2015-02-27T10:21:46Z
dc.date.issued2011en_US
dc.description.abstractWe develop a new Lagrangian primitive, named Langevin particle, to incorporate turbulent flow details in fluid simulation. A group of the particles are distributed inside the simulation domain based on a turbulence energy model with turbulence viscosity. A particle in particular moves obeying the generalized Langevin equation, a well known stochastic differential equation that describes the particle's motion as a random Markov process. The resultant particle trajectory shows self-adapted fluctuation in accordance to the turbulence energy, while following the global flow dynamics. We then feed back Langevin forces to the simulation based on the stochastic trajectory, which drive the flow with necessary turbulence. The new hybrid flow simulation method features nonrestricted particle evolution requiring minimal extra manipulation after initiation. The flow turbulence is easily controlled and the total computational overhead of enhancement is minimal based on typical fluid solvers.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2011.01872.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.titleLangevin Particle: A Self-Adaptive Lagrangian Primitive for Flow Simulation Enhancementen_US
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