Advanced graph neural network-based surrogate model for granular flows in arbitrarily shaped domains

• A new signed distance function-based graph neural network (SGN) is proposed. • The signed distance function is crucial for modeling arbitrarily shaped domains. • The SGN accurately simulates granular dynamics. • The SGN significantly reduces calculation costs. In next-generation manufacturing, the introduction of a surrogate model is crucial for constructing a digital twin. This trend is no exception in powder industry. Various mesh-free approaches such as the discrete element method (DEM) are versatile tools for facilitating simulation-based digital twins of powder industries. Unfortunately, the DEM-based high-fidelity simulation of actual industrial processes poses a significant challenge because of the prohibitive computational costs. In this context, the surrogate model has emerged as a promising technique to solve this problem. However, surrogate modeling of powder processes within arbitrarily shaped boundaries is substantially impossible. To resolve this essential problem in the existing surrogate models, we propose a novel surrogate model, namely, a signed distance function-based graph neural network (SGN), to accelerate the simulations of granular flows. In the SGN, the wall boundary is efficiently modeled by combining the graph structure with a signed distance function. Validation studies were conducted in typical powder systems, such as a sandglass, and a rotating-paddle mixer. A systematic comparison of the simulation results between the high-fidelity and SGN simulations shows that the proposed SGN accurately simulates granular dynamics, with a remarkable increase in computational efficiency. This study significantly contributes to the next-generation manufacturing in the powder industry.