Parallel GPU-based collision detection of irregular vessel wall for massive particles
Author(s) -
Binbin Yong,
Jun Shen,
Hongyu Sun,
Huaming Chen,
Qingguo Zhou
Publication year - 2017
Publication title -
cluster computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.335
H-Index - 50
eISSN - 1573-7543
pISSN - 1386-7857
DOI - 10.1007/s10586-017-0741-7
Subject(s) - collision detection , computer science , collision , limit (mathematics) , frame (networking) , decomposition , algorithm , computational science , mathematics , mathematical analysis , computer security , telecommunications , ecology , biology
In this paper, we present a novel GPU-based limit space decomposition collision detection algorithm (LSDCD) for performing collision detection between a massive number of particles and irregular objects, which is used in the design of the Accelerator Driven Sub-Critical (ADS) system. Test results indicate that, the collisions between ten million particles and the vessel can be detected on a general personal computer in only 0.5 s per frame. With this algorithm, the collision detection of maximum sixty million particles are calculated in 3.488030 s. Experiment results show that our algorithm is promising for fast collision detection.
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