Peng Chucai, Huang Jingnan, Sun Xichen, Nan Yifei, Chen Yaohui, Chen Kun, Feng Jun
School of Civil Engineering and Architecture, Hunan Institute of Science and Technology, Yueyang 414006, China.
National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.
Materials (Basel). 2024 Nov 21;17(23):5696. doi: 10.3390/ma17235696.
As an important civil and military infrastructure, airport runway pavement is faced with threats from cluster munitions, since it is vulnerable to projectile impacts with internal explosions. Aiming at the damage assessment of an island airport runway pavement under impact, this work dealt with discrete modeling of rigid projectile penetration into concrete pavement and the calcareous sand subgrade multi-layer structure. First, the Discrete Element Method (DEM) is introduced to model concrete and calcareous sand granular material features, like cohesive fracture and strain hardening due to compression, with mesoscale constitutive laws governing the normal and shear interactions between adjacent particles. Second, the subsequent DEM simulations of uniaxial and triaxial compression were performed to calibrate the DEM parameters for pavement concrete, as well as subgrade calcareous sand. Prior to the multi-layer structure investigations, penetration into sole concrete or calcareous sand is validated in terms of projectile deceleration and depth of penetration (DOP) with relative error ≤ 5.6% providing a reliable numerical tool for deep penetration damage assessments. Third, projectile penetration into the airport runway structure with concrete pavement and calcareous sand subgrade was evaluated with validated DEM model. Penetration numerical simulations with various projectile weight, pavement concrete thickness as well as striking velocity, were performed to achieve the DOP. Moreover, the back-propagation (BP) neural network proxy model was constructed to predict the airport runway penetration data with good agreement realizing rapid and robust DOP forecasting. Finally, the genetic algorithm was coupled with the proxy model to realize intelligent optimization of pavement penetration, whereby the critical velocity projectile just perforates concrete pavement indicating the severest subsequent munition explosion damage.
作为重要的民用和军事基础设施，机场跑道道面面临着集束弹药的威胁，因为它易受内部爆炸的弹丸撞击。针对某海岛机场跑道道面在冲击作用下的损伤评估问题，本文研究了刚性弹丸侵彻混凝土道面及钙质砂地基多层结构的离散模型。首先，引入离散元法（DEM）来模拟混凝土和钙质砂颗粒材料的特性，如粘结断裂和压缩引起的应变硬化，采用细观本构定律来控制相邻颗粒之间的法向和剪切相互作用。其次，进行了后续的单轴和三轴压缩DEM模拟，以校准道面混凝土以及地基钙质砂的DEM参数。在进行多层结构研究之前，通过弹丸减速和侵彻深度（DOP）验证了弹丸对单一混凝土或钙质砂的侵彻，相对误差≤5.6%，为深部侵彻损伤评估提供了可靠的数值工具。第三，利用验证后的DEM模型评估了弹丸对具有混凝土道面和钙质砂地基的机场跑道结构的侵彻。进行了不同弹丸重量、道面混凝土厚度以及撞击速度下的侵彻数值模拟，以获得侵彻深度。此外，构建了反向传播（BP）神经网络代理模型来预测机场跑道侵彻数据，结果吻合良好，实现了快速且可靠的侵彻深度预测。最后，将遗传算法与代理模型相结合，实现了道面侵彻的智能优化，由此确定了刚好能穿透混凝土道面的临界速度弹丸，这表明随后的弹药爆炸损伤最为严重。
