关键词: 雷达罩, 纤维增强复合材料, 神经网络, 有限元, 爆炸载荷, 厚度等效

Abstract: To investigate the thickness equivalence of composite radome under the far-field explosive blast, the fiber-reinforced polymer (FRP) laminate is taken as the research object. A serial artificial neural network (S-ANN) model based on the principle of deflection equivalence is proposed to predict the thickness equivalence relationship between different performance glass fiber radomes. A finite element model of the dynamic response of FRP laminates under explosive loads was established. By performing batch calculations of this finite element model, the maximum deflection of the FRP laminates under various parameters—including different detonation distances, laminate thicknesses, densities, and longitudinal elastic moduli—was obtained, which facilitated the development of an S-ANN thickness equivalence model. This model achieved thickness equivalence for different types of FRP materials under far-field explosive loads. In addition, the frequency response characteristics of the glass fiber equivalent radome are analyzed using the transmission matrix and numerical simulation method. The research results show that the longitudinal elastic modulus has the greatest influence on the explosion resistance and equivalent thickness of the glass fiber radome under distant field explosion load; the influence of equivalent thickness on the amplitude of the radome's transmission efficiency is small, but it changes the resonant frequency of the radome's transmission efficiency. This study can provide reference for the thickness equivalence research and optimization design of radomes.

Key words: radome, fiber reinforced polymer, neural network, finite element, explosive load, thickness equivalent