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GPU Based Simulation of Collision Detection of Irregular Vessel Walls

  • Binbin Yong
  • Jun Shen
  • Hongyu Sun
  • Zijian Xu
  • Jingfeng Liu
  • Qingguo Zhou
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 422)

Abstract

Collision detection is a commonly used technique in the fields of computer games, physical simulation , virtual technology, computing and animation. When simulating the process of particle collision of ADS (Accelerator Driven Sub-Critical) system, complex and irregular vessel walls need to be considered. Generally, an irregular vessel wall is a curve surface, which cannot be defined as an exact mathematical function, and it is difficult to calculate the distance between particles and the wall directly. In this paper, we present an algorithm to perform collision detection between particles and irregular wall. When the number of particles reaches the level of 106, our algorithm implements a considerable improvement in performance if running on GPU, nearly 10 times faster than running on CPU. Results have demonstrated that our algorithm is promising.

Keywords

Collision detection Irregular vessel Physical simulating GPU 

Notes

Acknowledgements

This work was supported by Dongguan’s Recruitment of Innovation and entrepreneurship talent program, National Natural Science Foundation of China under Grant No. 61402210 and 60973137, Program for New Century Excellent Talents in University under Grant No. NCET-12-0250, Strategic Priority Research Program of the Chinese Academy of Sciences with Grant No. XDA03030100, Gansu Sci. and Tech. Program under Grant No. 1104GKCA049, 1204GKCA061 and 1304GKCA018, Google Research Awards and Google Faculty Award, China.

References

  1. 1.
    NVIDIA, Particle Simulation using CUDA, 1st ed., NVIDIA, 9 2013.Google Scholar
  2. 2.
    J. Zheng, X. An, and M. Huang, “Gpu-based parallel algorithm for particle contact detection and its application in self-compacting concrete flow simulations,” Computers & Structures, vol. 112, pp. 193–204, 2012.Google Scholar
  3. 3.
    Y. Shen, Q. Jia, G. Chen, Y. Wang, and H. Sun, “Study of rapid collision detection algorithm for manipulator,” in Industrial Electronics and Applications (ICIEA), 2015 IEEE 10th Conference on, Jun. 2015, pp. 934–938.Google Scholar
  4. 4.
    S. Xue-li and Z. Ji-suo, “Research of collision detection algorithm based on particle swarm optimization,” Computer Design and Applications (ICCDA), vol. 1, 2010.Google Scholar
  5. 5.
    H. Qu and W. Zhao, “Fast collision detection algorithm based on parallel ant,” Virtual Reality and Visualization (ICVRV), pp. 261–264, 2013.Google Scholar
  6. 6.
    S. Xue-li and L. Tao, “Fast collision detection based on projection parallel algorithm,” Future Computer and Communication (ICFCC), vol. 1, 2010.Google Scholar
  7. 7.
    H. Qu and W. Zhao, “Fast collision detection of space-time correlation,” Computer Science and Electronics Engineering (ICCSEE), vol. 3, pp. 567–571, 2012.Google Scholar
  8. 8.
    M. Tang, D. Manocha, J. Lin, and R. Tong, “Collision-streams: Fast GPU-based collision detection for deformable models,” in I3D ’11: Proceedings of the 2011 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games, 2011, pp. 63–70.Google Scholar
  9. 9.
    X. Zhang and Y. J. Kim, “Scalable collision detection using p-partition fronts on many-core processors,” IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 3, pp. 447–456, Mar. 2014.Google Scholar
  10. 10.
    L. Wang, Y. Shi, and R. Li, “An image-based collision detection optimization algorithm,” in Signal and Information Processing (ChinaSIP), 2015 IEEE China Summit and International Conference on, Jul. 2015, pp. 220–224.Google Scholar
  11. 11.
    H. Karunasena, W. Senadeera, Y. Gu, and R. Brown, “A coupled sph-dem model for fluid and solid mechanics of apple parenchyma cells during drying,” in 18th Australian Fluid Mechanics Conference. Australasian Fluid Mechanics Society Launceston, Australia, 2012.Google Scholar
  12. 12.
    M. Rhodes, X. S. Wang, M. Nguyen, P. Stewart, and K. Liffman, “Study of mixing in gas-fluidized beds using a dem model,” Chemical Engineering Science, vol. 56, no. 8, pp. 2859–2866, 2001.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Binbin Yong
    • 1
  • Jun Shen
    • 2
  • Hongyu Sun
    • 1
  • Zijian Xu
    • 1
  • Jingfeng Liu
    • 3
  • Qingguo Zhou
    • 1
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.School of Computing and Information TechnologyUniversity of WollongongWollongongAustralia
  3. 3.LinkspriteWuhanChina

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