Damage characteristics and structural optimization of PELE with gradient reactive filling penetrating steel targets

To enhance the radial velocity and damage effectiveness of fragments after PELE projectile penetration, Al/Ep/B reactive material was selected as the core filler. Ballistic gun tests were conducted to study the dynamic penetration of steel targets by PELE projectiles with different core structures. The damage mechanisms of fragments were analyzed through mass distribution statistics and three-dimensional morphological reconstruction, while high-speed imaging was used to quantitatively characterize the axial and radial velocity fields of the fragments. A numerical model corresponding to the experimental conditions was established using Autodyn, incorporating a subroutine to describe the equation of state of the reactive material. The results showed high consistency between the simulated fragment morphology and velocity distribution and the experimental data. To further optimize the reactive core structure, a deep neural network was employed to establish the relationship between material parameters, fragment mass, and radial velocity. Multi-objective optimization was performed using the Non-dominated Sorting Genetic Algorithm (NSGA-II), ultimately identifying the gradient configuration as the optimal solution. A comparison between the optimization results and numerical simulations showed less than 5 % error in key parameters, verifying the reliability of the optimization. • Al/Ep/B material is employed as the filler material for PELE and two filling configurations were designed. • Fragment damage mechanisms were analyzed based on mass distribution statistics and planar morphology reconstruction. • Axial and radial velocity field characteristics of the fragments were quantitatively characterized. • Simulation model of user-defined subroutine was used to characterize the unreacted material, ignition growth model. • Deep neural network was developed to establish the relationship between material parameters, fragment mass and radial velocity.

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Publication Details

Journal
Polymer Testing
Published
2025-11-21
DOI
https://doi.org/10.1016/j.polymertesting.2025.109054
Citations
1
Primary Topic
High-Velocity Impact and Material Behavior
Type
article
Field-Weighted Citation Impact
0.52
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article

Damage characteristics and structural optimization of PELE with gradient reactive filling penetrating steel targets

Enling Tang, Zhe Zhai, Guowen Gao, Guolai Yang et al.
1 citations
Polymer Testing
High-Velocity Impact and Material Behavior
0.52
article

Damage characteristics and structural optimization of PELE with gradient reactive filling penetrating steel targets

Enling Tang, Zhe Zhai, Guowen Gao, Guolai Yang, Yafei Han
article en
1 citations

Abstract

To enhance the radial velocity and damage effectiveness of fragments after PELE projectile penetration, Al/Ep/B reactive material was selected as the core filler. Ballistic gun tests were conducted to study the dynamic penetration of steel targets by PELE projectiles with different core structures. The damage mechanisms of fragments were analyzed through mass distribution statistics and three-dimensional morphological reconstruction, while high-speed imaging was used to quantitatively characterize the axial and radial velocity fields of the fragments. A numerical model corresponding to the experimental conditions was established using Autodyn, incorporating a subroutine to describe the equation of state of the reactive material. The results showed high consistency between the simulated fragment morphology and velocity distribution and the experimental data. To further optimize the reactive core structure, a deep neural network was employed to establish the relationship between material parameters, fragment mass, and radial velocity. Multi-objective optimization was performed using the Non-dominated Sorting Genetic Algorithm (NSGA-II), ultimately identifying the gradient configuration as the optimal solution. A comparison between the optimization results and numerical simulations showed less than 5 % error in key parameters, verifying the reliability of the optimization. • Al/Ep/B material is employed as the filler material for PELE and two filling configurations were designed. • Fragment damage mechanisms were analyzed based on mass distribution statistics and planar morphology reconstruction. • Axial and radial velocity field characteristics of the fragments were quantitatively characterized. • Simulation model of user-defined subroutine was used to characterize the unreacted material, ignition growth model. • Deep neural network was developed to establish the relationship between material parameters, fragment mass and radial velocity.

Polymer TestingVol. 154
Shenyang Ligong University (CN), Nanjing University of Science and Technology (CN)
Openalex Percentile: Top 30%
High-Velocity Impact and Material Behavior
0.52
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