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Acta Armamentarii ›› 2025, Vol. 46 ›› Issue (10): 250424-.doi: 10.12382/bgxb.2025.0424

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Data-Driven Dimensional Analysis of the Dynamic Response of Low-Carbon Steel Circular Plates under Uniform Impulsive Loading

GE Puxin1, SONG Zihao1, LI Zhiyang1, WANG Hairen2, LEI Jianyin1,3,*(), LIU Zhifang1   

  1. 1 School of Aerospace Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
    2 School of Civil Engineering, Yulin University, Yulin 719000, Shaanxi, China
    3 Institute of Nuclear Emergency and Nuclear Safety, China Institute of Radiation Protection, Taiyuan, 030024, Shanxi, China
  • Received:2025-05-29 Online:2025-11-05
  • Contact: LEI Jianyin

Abstract:

Under explosive loading, the key to predicting a structure’s response is to accurately establish the relationship among the input load, material properties, and mechanical response. This study proposes a method that combines data-driven modeling with dimensional-invariance analysis to identify the key dimensionless parameters governing the dynamic plastic response of circular plates under impulsive loading, and to build a deflection-prediction model. An explicit dynamic model of a clamped circular plate is constructed in ABAQUS. Data are generated by varying the plate radius L, thickness H, density ρ, yield strength σ0, and impulse I. An artificial neural network is used to fit the response surface and compute gradients; together with the exponent-matrix method and active subspace analysis, feature construction and dimensionality reduction are performed to identify the dominant dimensionless group(s). The results show that, after analysis in the principal subspace, the five original variables can ultimately be expressed as a combination of the Johnson damage number I2/(ρσ0H2) and the geometric parameter H/L, thereby reducing a multivariable input to a single core variable. The identified dimensionless group effectively characterizes impact damage and dynamic response and exhibits good applicability. The findings provide an efficient data-driven analysis tool for impact-dynamics problems and demonstrate the potential of machine learning in engineering-physics applications.

Key words: Impulsive loading, data-driven, dimensional analysis, dynamic plastic response, finite-element simulation