Capturing mesoscale structures in computational fluid dynamics simulations of gas-solid flows

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 article develops a density-based spatial clustering of applications with noise (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 O(nlogn), lower than the existing techniques, by testing its scalability on highly resolved grids (up to 100 million grid points).