Clustering-enhanced Lattice discrete particle modeling for quasi-brittle fracture and fragmentation analysis

Abstract

This study focuses on predicting and quantifying fragmentation phenomena under high impulsive dynamic loading, such as blast, impact, and penetration events, which induce plastic deformation, fracture, and fragmentation in materials. The research addresses the challenge of accurately quantifying fragmentation through individual fragment mass and velocities. To achieve this, the Lattice Discrete Particle Model (LDPM) is utilized to predict failure modes and crack patterns and analyze fragments in reinforced concrete protective structures subjected to dynamic loads. An innovative unsupervised learning clustering technique is developed to identify and characterize fragment mass and velocity. The study demonstrates that the proposed method efficiently and accurately quantifies fragmentation, offering significant speed and efficiency gains while maintaining high fidelity. By combining a high-fidelity physics-based model for fragment formation with advanced geometric algorithms and distance-based approximations, the method accurately characterizes fragment size, position, and velocity. This approach circumvents computational costs associated with simulations across various time scales of fragment generation, trajectory, and secondary impacts. Experimental validation confirms the effectiveness of the proposed method in simulating real-world fragmentation phenomena, making it a valuable tool for applications in materials science, engineering, and beyond. The integrated workflow of LDPM simulations with machine learning clustering also offers an efficient means for structural engineers and designers to develop protective structures for dynamic impulsive loads.