Abstract
Multiphase reactors’ performance depends on the mesoscale structures
formed due to multiphase hydrodynamics. Examples of mesoscale structures
include gas bubbles in a fluidized bed and particle clusters in a riser.
Experimental investigation of these mesoscale structures is challenging
and expensive. To this end, Computational Fluid Dynamics (CFD)
simulations are extensively employed; however, post-processing CFD data
to capture mesoscale structures is challenging. This work develops a
DBSCAN-based methodology to capture and characterize mesoscale
structures from multiphase CFD simulation data. DBSCAN is an
unsupervised machine-learning algorithm, which requires the value of two
hyperparameters. A simple technique to calculate these hyperparameters
is provided and the performance of DBSCAN is assessed on CFD-DEM
simulations of bubbling fluidized beds and particle clustering. We
demonstrate the computational complexity of DBSCAN to be Ο(n log n),
lower than the existing techniques, by testing its scalability on highly
resolved grids (up to 100 million grid points).