Fragments identification method for SPH debris cloud and analysis of maximum fragments under hypervelocity normal and oblique impacts

Smoothed Particle Hydrodynamics (SPH) is widely used in hypervelocity impact simulations, but its mesh-free nature results in poorly defined material boundaries, complicating the accurate identification and analysis of fragments. To address this, a fragment identification method combining SPH characteristics with machine learning is proposed and validated against experimental data. Based on this method, the characteristics of the largest fragments under normal and oblique impacts were analyzed, and a new model relating impact conditions to maximum fragment mass and velocity was established through genetic algorithms. Additionally, this method enables debris cloud analysis at the fragment scale. Quantitatively analyzing fragment momentum reveals the distribution characteristics at different positions, allowing for an effective assessment of perforation risk to spacecraft protective structures.