Abstract:
Understanding the dynamic behavior of particulate materials is extremely important to many industrial processes with a wide range of applications ranging from hopper flows in agriculture to tumbling mills in the mining industry. Thus simulating the dynamics of particulate materials is critical in the design and optimization of such processes. The mechanical behavior of particulate materials is complex and cannot be described by a closed form solution for more than a few particles. A popular and successful numerical
approach in simulating the underlying dynamics of particulate materials is the discrete element method (DEM). However, the DEM is computationally expensive and computationally viable simulations are typically restricted to a few particles with realistic particle shape or a larger number of particles with an often oversimplified particle shape. It has been demonstrated for numerous applications that an accurate representation of the particle shape is essential to accurately capture the macroscopic transport of particulates.
The most common approach to represent particle shape is by using a cluster of spheres to approximate the shape of a particle. This approach is computationally intensive as multiple spherical particles are required to represent a single non-spherical particle. In addition spherical particles are for certain applications a poor approximation when sharp
interfaces are essential to capture the bulk transport behavior. An advantage of this approach is that non-convex particles are handled with ease. Polyhedra represent the geometry of most convex particulate materials well and when combined with appropriate contact models exhibit realistic transport behavior to that of the actual system. However detecting collisions between the polyhedra is computationally expensive, often limiting
simulations to only a few thousand of particles.
Driven by the demand for real-time graphics, the Graphical Processor Unit (GPU) offers cluster type performance at a fraction of the computational cost. The parallel nature of the GPU allows for a large number of simple independent processes to be executed in parallel. This results in a significant speed up over conventional implementations utilizing
the Central Processing Unit (CPU) architecture, when algorithms are well aligned and
optimized for the threading model of the GPU. This thesis investigates the suitability of
the GPU architecture to simulate the transport of particulate materials using the DEM.
The focus of this thesis is to develop a computational framework for the GPU architecture
that can model (i) tens of millions of spherical particles and (ii) millions of polyhedral
particles in a realistic time frame on a desktop computer using a single GPU.
The contribution of this thesis is the development of a novel GPU computational frame-
work Blaze-DEM, that encompasses collision detection algorithms and various heuristics
that are optimized for the parallel GPU architecture. This research has resulted in a new
computational performance level being reached in DEM simulations for both spherical
