Collision detection is a fundamental task in computer simulations, widely applied in multibody dynamics, granular mechanics, and computer graphics. In granular dynamics or Discrete Element Method (DEM) simulations, collision detection faces two critical challenges: first, due to the small size and high stiffness of particles, extremely small-time steps (e.g., 1×10⁻⁶ to 1×10⁻⁵ seconds) are required for numerical stability; second, the collision detection stage is often computationally intensive, becoming a major bottleneck in large-scale simulations.
To address this, a variety of parallel collision detection strategies have been proposed in the literature, including OpenMP- and GPU-based parallel frameworks, asynchronous decoupling of contact detection and dynamic solving, and spherical decomposition methods for simplifying complex geometries.
This thesis aims to integrate such parallel techniques into a multibody dynamics simulation framework to achieve efficient DEM simulations. The research begins with a comprehensive literature review of state-of-the-art parallel collision detection algorithms. Subsequently, selected methods are implemented within a C++ simulation platform, incorporating both parallel collision detection algorithms and DEM contact models. Finally, the performance and scalability of the implemented methods are evaluated through benchmark case studies.
Keywords: Computer Graphics, Discrete Element Method, Collision Detection Algorithm, GPU computing, Dynamics Simulation
Requirements:
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You are studying Electrical Engineering, Automation, or Robotics Systems Engineering.
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You are interested in robot simulation or game physics engines; ideally, you have studied multibody dynamics or robotics dynamics.
- Ideally, you have programming skills in C++
Betreuer: Shao, Email:
