The aircraft wings are a crucial component, particularly during mechanical shock excitation. Thus, for the first time, an inventive adaptable model to simulate the nonlinear dynamics of the wings under explosive blast loading at the moment of the collision is described in this article. It is crucial to increase the structure's stability because of explosive blast loading. The mentioned structure is made of two composite face sheets and a carbon fiber-reinforced polymer (CFRP). Functionally graded (FG) graphene origami (GOri)-enabled auxetic metallic metamaterial (GOEAM) is introduced to improve the stability of this kind of applicable structure, especially in air maneuvers. The analysis is conducted utilizing Reddy's third-order shear deformation of Reddy theory (TSDRT), incorporating Hamilton's principle and Von Kármán nonlinear geometric assumptions. The governing and boundary equations are accurately computed at the domain and boundary edges of the doubly curved structure. The coupled mesh-free radial point interpolation method (MRPIM) and Newton-Raphson method are applied for numerically simulating the current doubly curved structure by employing Newmark's temporal integration method with a constant-average acceleration approach. The present findings are cross-checked against the machine learning method and open-source results from the literature. Based on physical data, machine learning integrates supervised machine learning to predict the nonlinear vibration of the system. In this case, nonlinear transient bending properties are found using hybrid machine learning methods and solutions. The findings demonstrate the significance of GOEAM for nonlinear vibrations of the composite system. The current study's conclusions could be used as a benchmark and useful recommendations for future structural design techniques with enhanced features.

中文翻译：

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爆炸爆炸载荷下飞机CFRP夹层机翼的非线性动力学分析：引入SVM-DNN算法预测动力学信息

飞机机翼是一个至关重要的部件，特别是在机械冲击激励期间。因此，本文首次描述了一种创新的适应性模型，用于模拟碰撞时爆炸载荷下机翼的非线性动力学。由于爆炸载荷，提高结构的稳定性至关重要。上述结构由两个复合面板和碳纤维增强聚合物（CFRP）制成。引入功能梯度（FG）石墨烯折纸（GOri）的拉胀金​​属超材料（GOEAM）来提高这种适用结构的稳定性，特别是在空中机动中。该分析是利用 Reddy 的三阶剪切变形 Reddy 理论 (TSDRT) 进行的，并结合了 Hamilton 原理和 Von Kármán 非线性几何假设。在双曲结构的域和边界边缘精确计算控制方程和边界方程。采用耦合无网格径向点插值法（MRPIM）和Newton-Raphson法，采用Newmark时间积分法和恒平均加速度法对当前双曲结构进行数值模拟。目前的研究结果与机器学习方法和文献中的开源结果进行了交叉检查。机器学习基于物理数据，集成监督机器学习来预测系统的非线性振动。在这种情况下，使用混合机器学习方法和解决方案发现非线性瞬态弯曲特性。研究结果证明了 GOEAM 对于复合系统非线性振动的重要性。当前研究的结论可以作为未来具有增强功能的结构设计技术的基准和有用的建议。