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See detailOn the performance of an overlapping-domain parallelization strategy for Eulerian-Lagrangian Multiphysics software
Pozzetti, Gabriele UL; Besseron, Xavier UL; Rousset, Alban UL et al

in AIP Conference Proceedings (in press)

In this work, a strategy for the parallelization of a two-way CFD-DEM coupling is investigated. It consists on adopting balanced overlapping partitions for the CFD and the DEM domains, that aims to reduce ... [more ▼]

In this work, a strategy for the parallelization of a two-way CFD-DEM coupling is investigated. It consists on adopting balanced overlapping partitions for the CFD and the DEM domains, that aims to reduce the memory consumption and inter-process communication between CFD and DEM. Two benchmarks are proposed to assess the consistency and scalability of this approach, coupled execution on 252 cores shows that less than 1\% of time is used to perform inter-physics data exchange. [less ▲]

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See detailComparing Broad-Phase Interaction Detection Algorithms for Multiphysics DEM Applications
Rousset, Alban UL; Mainassara Chekaraou, Abdoul Wahid UL; Liao, Yu-Chung UL et al

Scientific Conference (2017, September)

Collision detection is an ongoing source of research and optimization in many fields including video-games and numerical simulations [6, 7, 8]. The goal of collision detection is to report a geometric ... [more ▼]

Collision detection is an ongoing source of research and optimization in many fields including video-games and numerical simulations [6, 7, 8]. The goal of collision detection is to report a geometric contact when it is about to occur or has actually occurred. Unfortunately, detailed and exact collision detection for large amounts of objects represent an immense amount of computations, naivly n 2 operation with n being the number of objects [9]. To avoid and reduce these expensive computations, the collision detection is decomposed in two phases as it shown on Figure 1: the Broad-Phase and the Narrow-Phase. In this paper, we focus on Broad-Phase algorithm in a large dynamic three-dimensional environment. We studied two kinds of Broad-Phase algorithms: spatial partitioning and spatial sorting. Spatial partitioning techniques op- erate by dividing space into a number of regions that can be quickly tested against each object. Two types of spatial partitioning will be considered: grids and trees. The grid-based algorithms consist of a spatial partitioning processing by dividing space into regions and testing if objects overlap the same region of space. And this reduces the number of pairwise to test. The tree-based algorithms use a tree structure where each node spans a particular space area. This reduces the pairwise checking cost because only tree leaves are checked. The spatial sorting based algorithm consists of a sorted spatial ordering of objects. Axis-Aligned Bounding Boxes (AABBs) are projected onto x, y and z axes and put into sorted lists. By sorting projection onto axes, two objects collide if and only if they collide on the three axes. This axis sorting reduces the number of pairwise to tested by reducing the number of tests to perform to only pairs which collide on at least one axis. For this study, ten different Broad-Phase collision detection algorithms or framework have been considered. The Bullet [6], CGAL [10, 11] frameworks have been used. Concerning the implemented algorithms most of them come from papers or given implementation [less ▲]

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