A Monte Carlo approach to modeling homogeneous cooling in a granular gas of spherocylinders

Solid particles and granular flows are prevalent in natural phenomena and industrial applications. Many of such granular flows involve particles with irregular or non-spherical shapes. The study of such flows is of paramount importance for optimizing material handling processes and the design of industrial equipment. Particle morphology plays a pivotal role in determining the behavior of bulk solids, which presents several challenges when modeling the fluid dynamics. The Discrete Element Method (DEM) is a valuable tool for simulating the behavior of non-spherical particles. However, the computational cost associated with contact detection limits the application of DEM to small systems. To overcome this limitation, we have developed a Monte Carlo method that leverages data from high-fidelity discrete element simulations to construct probabilistic models that relate the post-collisional states of particles to the pre-collisional state. Doing so eliminates the computational overhead associated with contact detection in a traditional deterministic model. In this work, we extend the Monte Carlo method to dissipative non-spherical granular particles. The predictions generated by our new method are subsequently compared to DEM simulations for different particle shapes. Since non-spherical particles have aspect ratios that deviate from unity, a significant amount of energy is stored in their rotational modes. As a result, the shape of the particles profoundly influences how the system cools and clusters over time. We present the homogeneous cooling state of a dilute granular gas of spherocylinders using our Monte Carlo framework.