A parallel unidirectional coupled DEM-PBM model for the efficient simulation of computationally intensive particulate process systems. (2nd November 2018)

• Record Type: Journal Article
• Authors: Sampat, Chaitanya; Bettencourt, Franklin; Baranwal, Yukteshwar; Paraskevakos, Ioannis; Chaturbedi, Anik; Karkala, Subhodh; Jha, Shantenu; Ramachandran, Rohit; Ierapetritou, Marianthi
• Is Part Of: Computers & chemical engineering. Volume 119(2018)
• Page Start: 128
• Page End: 142
• Publication Date: 2018-11-02
• Subjects: Population balance model -- Granulation -- Discrete element method -- MPI and OpenMP -- Pharmaceutical process design; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.08.006
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A new framework for a unidirectional multi-scale model of wet granulation. A multi-dimensional Population Balance Model is developed using mechanistic kernel. Population Balance Model is parallelized to address the computational complexity requirements. Results demonstrate scaling of DEM and PBM in multiple cores. The methodology is implemented to high shear wet granulation process. Abstract: The accurate modeling of the physics underlying particulate processes is complicated and requires significant computational capabilities to solve using particle-based models. In this work, a unidirectional multi-scale approach was used to model the high shear wet granulation process. A multi-dimensional population balance model (PBM) was developed with a mechanistic kernel, which in turn obtained collision data from the discrete element modeling (DEM) simulation. The PBM was parallelized using a hybrid OpenMP+MPI approach. The DEM simulations were performed using LIGGGHTS, which was parallelized using MPI. Speedups of approximately 14 were obtained for the PBM simulations and approximately 12 for the DEM simulations. The uni-directional coupling of DEM to PBM was performed using middle-ware components (RADICAL-Pilot) that did not require modifications of the DEM or PBM codes, yet supported flexible execution on high-performance platforms. Results demonstrate scaling from 1 to 128 cores for the PBM and up to 256 cores for the DEM. The proposed method, implementations and middle-ware enable the modeling of high shear wet granulation process faster than existing approaches in literature.