| The use of Population Balance Models (PBM) for simulating dynamics of particulate systems are inevitably limited at some point by the demands they place on computational resources. PBMs are widely used to describe the time evolutions and distributions of many industrial particulate processes, and its efficient and quick simulation would certainly be beneficial for process design, control and optimization. This thesis is an elucidation of how MATLAB's Parallel Computing Toolbox (PCT), a third-party toolbox called JACKET, and the MATLAB Distributed Computing Server (MDCS) may be combined with algorithmic modification of the PBM to speed up these computations on a CPU (Central Processing Unit), GPU (Graphics Processing Unit) and a computer cluster respectively. Parallel algorithms were developed for three dimensional and four dimensional population balance models incorporating hardware class-specific parallel constructs such as SPMD and gfor. Results indicate significant reduction in computational time without compromising numerical accuracy for all cases except for the GPU. The GPU seemed promising for larger problems despite its limitations of lower clock speeds and on-board memory compared to the CPU. Evaluations of the speedup and scalability further affirm the algorithms' performance. |
