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Parallel simulation of population balance model-based particulate processes using multi-core CPUs and GPUs

Anuj V. Prakash, Anwesha Chaudhury, Rohit Ramachandran
Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
Modelling and Simulation in Engineering, 2013
@article{prakash2013parallel,

   title={Parallel simulation of population balance model-based particulate processes using multi-core CPUs and GPUs},

   author={Prakash, Anuj V and Chaudhury, Anwesha and Ramachandran, Rohit},

   year={2013}

}

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Computer-aided modeling and simulation is a crucial step in developing, integrating and optimizing unit operations and subsequently entire processes, in the chemical/pharmaceutical industry. This study details two methods of reducing the computational time to solve complex process models, namely the population balance model which given the source terms can be very computationally intensive. Population balance models are also widely used to describe the time evolutions and distributions of many particulate processes and its efficient and quick simulation would be very beneficial. The first method illustrates utilization of MATLAB’s Parallel Computing Toolbox (PCT) and the second method makes use of another toolbox, JACKET, to speed up computations on the CPU and GPU respectively. Results indicate significant reduction in computational time for the same accuracy using multi-core CPUs. Many-core platforms such as GPUs are also promising towards computational time reduction for larger problems despite the limitations of lower clock speed and device memory. This lends credence to the use of high-fidelity models (in place of reduced order models) for control and optimization of particulate processes.
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