Spatial dispersion characteristics of behind-armor debris generated during the penetration of tantalum alloy explosively-formed projectile

WEI Guoxu; XU Hongwei; GUO Rui; LI Xiangdong; ZHANG Lei; JI Long

doi:10.11883/bzycj-2025-0326

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WEI Guoxu, XU Hongwei, GUO Rui, LI Xiangdong, ZHANG Lei, JI Long. Spatial dispersion characteristics of behind-armor debris generated during the penetration of tantalum alloy explosively-formed projectile[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0326

Citation:

WEI Guoxu, XU Hongwei, GUO Rui, LI Xiangdong, ZHANG Lei, JI Long. Spatial dispersion characteristics of behind-armor debris generated during the penetration of tantalum alloy explosively-formed projectile[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0326

Citation:

WEI Guoxu, XU Hongwei, GUO Rui, LI Xiangdong, ZHANG Lei, JI Long. Spatial dispersion characteristics of behind-armor debris generated during the penetration of tantalum alloy explosively-formed projectile[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0326

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Spatial dispersion characteristics of behind-armor debris generated during the penetration of tantalum alloy explosively-formed projectile

doi: 10.11883/bzycj-2025-0326

1.
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
2.
National Key Laboratory of Land and Air Based Information Perception and Control, Xi’an Modern Control Technology Research Institute, Xi’an 710065, Shanxi, China

Received Date: 2025-09-29
Rev Recd Date: 2025-12-26

Available Online: 2026-01-05

Abstract

Abstract

To investigate the spatial dispersion characteristics of behind-armor debris (BAD) generated by the penetration of tantalum alloy explosively-formed projectile (EFP) into steel targets, a comprehensive study combining experimental testing, numerical simulation, and machine learning prediction was performed. First, X-ray imaging and fragment-distribution experiments were conducted on 45 steel targets penetrated by tantalum alloy EFP to obtain initial experimental data. Subsequently, the finite element-smoothed particle hydrodynamics (FE-SPH) fixed-coupling method, which had been validated by the experimental data, was employed to simulate the perforation process. These numerical simulations were carried out under a wide range of working conditions, specifically varying the projectile velocity and target thickness. Through this process, a comprehensive dataset describing the spatial dispersion of BAD was generated. Finally, to achieve rapid prediction capabilities, a support vector regression (SVR) model was established. The Bayesian optimization algorithm was utilized to train the model using the dense-fragment dispersion angle data extracted from the simulation dataset, thereby creating a robust predictive model for spatial dispersion of BAD. The experimental results indicate that the morphology of the BAD cloud exhibits a typical truncated-ellipsoidal shape. Due to the density difference between tantalum and steel, fragments composed of different materials display distinct radial expansion behaviors, i.e. steel fragments are distributed along the outer surface of the ellipsoid whereas tantalum fragments are concentrated on the inner surface. Spatially, the debris is primarily concentrated within a circular region surrounding the central perforation area of the witness plate. The FE-SPH fixed-coupling method successfully reproduced the BAD formation process, yielding debris-cloud morphologies that closely match the experimental results. The relative error between the simulated and measured mean maximum fragment dispersion angles is less than 10%, thereby confirming the accuracy of the numerical simulations. Furthermore, the analysis reveals that the Bayesian-optimized SVR model enables accurate prediction of dense-fragment dispersion angles under varying target thicknesses and EFP impact velocities, with maximum relative errors below 10%. Based on these predictions, the damage area on witness plates within a certain distance behind the target can be rapidly estimated.
- explosively formed projectile,
- behind-armor debris,
- spatial dispersion,
- support vector regression,
- bayesian optimization

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References(26)

References

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