发表机构
University of California Los Angeles; Adani University; Indian Institute of Technology Kanpur（加州大学洛杉矶分校; 阿达尼大学; 印度理工学院坎普尔分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发PiNN框架预测薄壁截锥壳临界屈曲载荷，结合RBD公式，经133次实验验证，其精度与物理一致性优于DNN，可用于不确定性感知的壳体结构设计。
AI 中文摘要
薄壁截锥壳因高比强度与几何效率，广泛应用于航空航天、船舶、海洋及轻量化基础设施系统。然而其轴向压缩下的屈曲抗力对几何缺陷、制造公差、材料变异性及非线性失稳效应高度敏感。传统设计流程依赖保守的 knockdown 因子（KDF），如 NASA SP-8019 推荐的因子，未明确考虑壳体几何、制造质量、数据不确定性或目标可靠性。本研究开发了物理信息神经网络（PiNN）框架，用于预测薄壁截锥壳的临界屈曲载荷，并将训练后的代理模型集成到基于可靠性的设计（RBD）公式中。该模型结合几何与材料描述符，以及从壳体稳定理论和局域缩减刚度法（LRSM）导出的力学信息特征。物理信息损失函数会惩罚超过理论弹性屈曲载荷的力学不可接受预测。该框架使用133次轴向压缩下的Mylar锥壳实验进行训练和评估。与传统深度神经网络（DNN）相比，PiNN提高了预测精度，降低了平均绝对误差，增强了物理一致性。随后，训练后的PiNN用于评估可靠性指标并校准符合安全要求的KDF，以满足规定的目标可靠性水平。结果表明，PiNN-RBD框架为缺陷敏感型壳体结构的不确定性感知设计提供了一种高效方法。
英文摘要
Thin-walled truncated conical shells are widely used in aerospace, marine, offshore, and lightweight infrastructure systems due to their high strength-to-weight ratio and geometric efficiency. Their buckling resistance under axial compression, however, is highly sensitive to geometric imperfections, manufacturing tolerances, material variability, and nonlinear instability effects. Conventional design procedures rely on conservative knockdown factors (KDFs), such as those recommended in NASA SP-8019, which do not explicitly account for shell geometry, fabrication quality, data uncertainty, or target reliability. This study develops a physics-informed neural network (PiNN) framework for predicting critical buckling loads of thin truncated conical shells and integrates the trained surrogate within a reliability-based design (RBD) formulation. The model combines geometric and material descriptors with mechanics-informed features derived from shell stability theory and the localized reduced stiffness method (LRSM). A physics-informed loss function penalizes mechanically inadmissible predictions exceeding the theoretical elastic buckling load. The framework is trained and evaluated using 133 experimental Mylar conical shell tests under axial compression. Compared with a conventional deep neural network (DNN), the PiNN improves predictive accuracy, reduces mean absolute error, and enhances physical consistency. The trained PiNN is then used to evaluate reliability indices and calibrate safety-consistent KDFs for prescribed target reliability levels. Results demonstrate that the PiNN-RBD framework provides an efficient approach for uncertainty-aware design of imperfection-sensitive shell structures.
