Machine learning and numerical investigation on drag coefficient of arbitrary polygonal particles

Abstract

The drag coefficient, as the most important parameter that characterizes particle dynamics in flows, has been the focus of a large number of investigations. Although good predictability is achieved for simple shapes, it is still challenging to accurately predict drag coefficient of complex-shaped particles even under moderate Reynolds number (Re). The problem is that the small-scale shape details of particles can still have considerable impact on the drag coefficient, but these geometrical details cannot be described by single shape factor. To address this challenge, we leverage modern deep-learning method's ability for pattern recognition, take multiple shape factors as input to better characterize particle-shape details, and use the drag coefficient as output. To obtain a high-precision data set, the discrete element method coupled with an improved velocity interpolation scheme of the lattice Boltzmann method is used to simulate and analyze the sedimentation dynamics of polygonal particles. Four different machine-learning models for predicting the drag coefficient are developed and compared. The results show that our model can well predict the drag coefficient with an average error of less than 5% for particles. These findings suggest that data-driven models can be an attractive option for the drag-coefficient prediction for particles with complex shapes.