Parallel calculation methods for molecular weight distribution of batch free radical polymerization

The molecular weight distribution (MWD) is at the core of establishing key quality indices for free radical polymerization processes. Due to large-scale features of the rigorous model, consisting of a very large number of differential and algebraic equations, dynamic simulation of MWD is always challenging. A sequential variable decoupling method (SVD) has been proposed to calculate the MWD for any reasonably large chain-length number. In the current paper, parallel computing methods were developed to accelerate the MWD simulation. Both coarse-grained and fine-grained parallelism methods have been proposed. A theoretical analysis of the proposed methods was conducted to demonstrate its high efficiency in parallel computing. Both Intel multi-core-processor-based and NVIDIA graphics-processing-unit-based parallel computing platforms were implemented, achieving significant speedups in computation for MWD simulation.

► Parallel computing methods were developed to accelerate the MWD simulation. ► Theoretical analysis proves the best efficiency in parallel computing of the method. ► Coarse-grained parallelism was implemented on Intel multi-core-processor platform. ► Hybrid form parallelism was implemented on NVIDIA GPU platform.