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theses

Particulate processes are prevalent in the pharmaceutical industry but, the physics underlying‎ these processes are complicated. The particle-level models used to describe these systems require large amounts of computation to solve which makes simulations slow. Another approach to model these systems is less accurate‎ bulk model which does not capture all the particle level data but is more efficient to simulate and requires lesser computational power. A quicker and a more accurate way to model such systems is to use a multi-scale model ‎i.e. use the particle-scale data into a bulk model. Using a coupled model does not decrease the time taken by each individual component of the simulation, thus there is also a need to increase the speed of the separate simulations.
In this work, a unidirectional multi-scale model‎ was used to model the high shear wet granulation process. A multi-dimensional population balance‎ model (PBM) was developed with a mechanistic kernel, which obtained‎ collision data from the discrete element modeling (DEM) simulation. The PBM was run in parallel‎ using MPI + OMP hybrid technique. The DEM simulations were performed on LIGGGHTS, which‎ runs in parallel using MPI. Speedup of about 14 times was obtained for the PBM simulations‎ and around 12 for the DEM simulations. This coupling was performed using the radical pilot‎ for scaling studies from 1 to 128 cores for the PBM and up to 256 cores for the DEM. A further improvement to the PBM code was also done by developing a code in CUDA C++ such that it could utilize up to 1024 cores on the NVIDIA graphical processor units (GPU). Using‎ this developed framework, the granulation process has been modeled accurately much faster than‎ existing approaches in literature

ETD_9455

M.S.

Includes bibliographical references

by Chaitanya Sampat

doi:10.7282/t3-jssh-ss48

ETD graduate

The author owns the copyright to this work.