Abstract

We present a novel storage-and-access approach for Lagrangian particles in a multiresolution local-timestepping framework for multiphase flow simulations. The proposed method applies a block-particle mapping strategy for efficient access of all particles on a specific refinement level while traversing through the multiresolution tree. This allows for extending the local timestepping algorithm with its refinement level-dependent timestep sizes to the evolution of particles, which results in significant speed-up in comparison to standard timestepping. For multi-scale multiphase flow simulations, the particle model is combined with a level-set based multiphase model on a Cartesian grid. To maintain robustness of fluid-state interpolation for particles near the level set-based fluid–fluid interface, WENO-based interpolation is applied which includes both real- and ghost-fluid cells. This enforces the sharp interface property also for interpolated fluid states, and suppresses spurious oscillations in the event of discontinuities.We validate the particle model with a one-dimensional simulation of a single particle in an air–helium shock tube for one-way coupling, and with two-dimensional simulations of a particle injected in a quiescent domain for the feedback force. Simulations of two-dimensional aerodynamic fragmentation in shear-induced entrainment and Rayleigh–Taylor piercing regimes use Lagrangian particles as sub-grid scale representation of small droplets post-breakup. Finally, three-dimensional massively-parallel simulations of single- and triple-bubble collapse near a wall coated with free-floating particles are presented. The particle cleaning radius of the single-bubble setup agrees reasonably well with experimental reference data. These simulations consider 106 particles and an Eulerian grid with effectively 1012 finite-volume cells at compression rates of more than 90% for the particulate phase. This underlines the advantageous effect of embedding the particles in the multiresolution tree with its spatial and temporal adaptivity, which is necessary for performing such large scale simulations efficiently.

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