Efficient parallel algorithms for coupled fluid-particle simulation

Abstract

Coupled fluid-particle simulations are routinely used in a variety of applications, ranging from respiratory droplet spreading to internal combustion engines, from ink-jet printing to in-flight ice accretion. The efficiency of parallel algorithms to simulate fluid-particle systems is strongly influenced by the different evolution of the flow and the particles dynamics. Indeed, a domain partitioning based on particle workload is possibly sub-optimal in terms of the number of fluid volume elements associated to each process. In this work, an efficient mesh partitioning based on graph representation is implemented. It can handle unstructured hybrid meshes composed by triangles and quadrilaterals in two spatial dimensions, and by tetrahedra, hexahedra, prisms, and pyramids in three dimensions. In order to obtain a domain decomposition to efficiently follow the particle trajectories, a preliminary solution is computed to suitably tag the fluid domain cells. The obtained weights represent the element probabilities to be crossed by particles. The algorithm is implemented using MPI distribute memory environment. The proposed approach is tested against reference cases for the coupled flow-particle simulation of ice accretion over 2D and 3D geometries. Two different cloud droplet impact test cases have been simulated: a NACA 0012 wing section and a NACA 64A008 swept horizontal tail. The computed collection efficiency compares fairly well with reference numerical and experimental data. The parallel efficiency of the algorithm is verified on a distributed memory cluster.

Efficient Lagrangian particle tracking algorithms for distributed-memory architectures

“…This is due to the experimental particle diameter distribution adopted in the LEWICE computation. The presence of droplets with a diameter greater than the mean value used in the other cases tends to increase the collection efficiency [39,40].…”