Data-Driven Screening of Particle Grading for Dense Granular Packing

Optimizing particle grading for dense granular packing remains challenging because the formulation space expands rapidly with increasing particle-size complexity, while direct evaluation using the discrete element method (DEM) is computationally expensive. In this work, a data-driven framework integrating DEM, Gaussian process regression, and Bayesian optimization is developed for efficient screening of ternary particle-grading formulations under a common high-density condition. Normalized residual interparticle overlap is introduced as a fixed-density measure of geometric incompatibility, which enables different formulations to be compared without repeatedly determining their maximum packing states. The surrogate-assisted search increases the occurrence of low-overlap candidates and shows that favorable formulations are distributed among several regions of the grading space rather than converging to a unique optimum. Repeat evaluations further identify two low-overlap strategies with distinct particle-size distributions and stable packing responses. Contact network and particle rearrangement analyses show that low overlap can be achieved through either a coarse–intermediate framework assisted by local fine-particle accommodation or a more distributed multiscale contact network. Fine particles provide most of the configurational mobility during relaxation, while the distribution of this mobility depends strongly on the underlying grading architecture. These findings establish practical relationships among particle-size hierarchy, distribution breadth, component fraction, and packing response, which can guide the selection of dense powder formulations and reduce reliance on extensive trial-and-error screening.