3D random packing algorithm of ellipsoidal particles based on the Monte Carlo method. Issue 7 (4th April 2021)

• Record Type: Journal Article
• Authors: Chen, Changhong; Bai, Songlin; Huang, Ying; Lam, Lik; Yao, Yao; Keer, Leon M.
• Is Part Of: Magazine of concrete research. Volume 73:Issue 7(2021)
• Page Start: 343
• Page End: 355
• Publication Date: 2021-04-04
• Subjects: aggregates -- composite materials -- finite element methods; Concrete -- Periodicals; Concrete construction -- Periodicals; Concrete -- Testing -- Periodicals; 624.1834
• DOI: 10.1680/jmacr.20.00228
• Languages: English
• ISSNs: 0024-9831

Abstract

Abstract : The random packing of aggregate particles is an important factor affecting the mechanical properties of concrete at the mesoscopic scale. In the current study, a meso-mechanical pretreatment algorithm is developed to construct the random ellipsoidal aggregate model for the mesoscopic structure of fully graded concrete. The Fuller curve combined with equivalent diameter is adopted to ensure equality between the gradation and content of the random ellipsoidal aggregates and those of the actual geometric aggregates. A 'removing occupied space' method is proposed to improve the packing efficiency based on the background grids strategy. A modified search algorithm consisting of rough and fine detection for determining the overlaps is proposed to improve the optimised simulation of the meso-structure of cement-based composites. A random ellipsoidal aggregate model with different aspect ratios of ellipsoid is developed and compared with the existing algorithms to test the efficiency of the new pretreatment algorithm. The effect of the ellipsoidal shape on the random packing fraction is investigated based on the proposed pretreatment algorithm. The pretreatment algorithm proposed greatly improves the efficiency of packing and provides a powerful tool for the realisation of three-dimensional large-scale numerical meso-concrete.