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Parallel GPU-based collision detection of irregular vessel wall for massive particles

Journal Article


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Abstract


  • 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 ADS (Accelerator Driven Sub-Critical) 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 second per frame. With this algorithm, the collision detection of maximum sixty million particles are calculated in 3.488030 seconds. Experiment results show that our algorithm is promising for fast collision detection.

Authors


  •   Yong, Binbin (external author)
  •   Shen, Jun
  •   Sun, Hongyu (external author)
  •   Chen, Huaming (external author)
  •   Zhou, Qingguo (external author)

Publication Date


  • 2017

Citation


  • Yong, B., Shen, J., Sun, H., Chen, H. & Zhou, Q. (2017). Parallel GPU-based collision detection of irregular vessel wall for massive particles. Cluster Computing, 20 (3), 2591-2603.

Scopus Eid


  • 2-s2.0-85013040504

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=7489&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6459

Number Of Pages


  • 12

Start Page


  • 2591

End Page


  • 2603

Volume


  • 20

Issue


  • 3

Place Of Publication


  • United States

Abstract


  • 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 ADS (Accelerator Driven Sub-Critical) 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 second per frame. With this algorithm, the collision detection of maximum sixty million particles are calculated in 3.488030 seconds. Experiment results show that our algorithm is promising for fast collision detection.

Authors


  •   Yong, Binbin (external author)
  •   Shen, Jun
  •   Sun, Hongyu (external author)
  •   Chen, Huaming (external author)
  •   Zhou, Qingguo (external author)

Publication Date


  • 2017

Citation


  • Yong, B., Shen, J., Sun, H., Chen, H. & Zhou, Q. (2017). Parallel GPU-based collision detection of irregular vessel wall for massive particles. Cluster Computing, 20 (3), 2591-2603.

Scopus Eid


  • 2-s2.0-85013040504

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=7489&context=eispapers

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6459

Number Of Pages


  • 12

Start Page


  • 2591

End Page


  • 2603

Volume


  • 20

Issue


  • 3

Place Of Publication


  • United States