Granular materials are commonly encountered in many natural hazards and industrial applications. To study such complex flow via continuum description, a well-known 𝜇(𝐼) stress model has been developed to describe the granular materials as a non-Newtonian fluid so that we can simulate its flow behaviors using computational fluid dynamics (CFD). 

Our lab aims to apply artificial intelligence (AI) algorithms to the complex fluid researches which are numerically or experimentally intractable. For instance, to develop a data-driven model to solve CFD problems, a supervised machine learning approach to predict the micro-scale force network of granular media and other neural network-based algorithms for non-Newtonian fluid modeling. 

Discrete element method (DEM) is an approach to simulate particulate systems towards the microscopic understanding. Our lab has developed an in-house DEM solver based on soft-sphere contact models to study various granular flow problems. The novel discrete element modeling for multiphase flow would be focused on as well.

The non-Brownian suspensions of mixture of solid particles in a viscous fluid are widely present in geophysical flows and industrial processing.  In our lab, we study the two-phase avalanche flow experimentally and develop the simulation approaches for multi-phase systems such as coupled computational fluid dynamics and discrete element method (CFD-DEM) and compressible Navier-Stokes solver.