Identification of worst-case gust loads is a critical step in the certification of very flexible aircraft, yet the computational cost of nonlinear full-order simulations renders exhaustive parametric searches impractical. This paper presents a reduced-order model (ROM) based methodology for rapid worstcase gust identification that achieves computational speedups of up to 600 times relative to full-order nonlinear simulations. The approach employs nonlinear model order reduction via Taylor series expansion and eigenvector projection of the coupled fluid-structure-flight dynamic system. Three test cases of increasing complexity are considered: a three-degree-of-freedom aerofoil (14 states, worst-case identified from 1,000 design sites), a Global Hawk-like UAV (540 states, 80 parametric calculations with 30 times speedup), and a very flexible flying-wing (1,616 states, 37 parametric calculations reduced from 222 hours to 22 minutes). The linear ROM is shown to be accurate for deformations below 10% of the wingspan, while the nonlinear ROM with second-order Taylor expansion accurately captures the large-deformation regime. The methodology provides a practical tool for integrating worst-case gust search into aircraft certification workflows.

翻译：最坏情况阵风载荷的识别是极柔飞机认证中的关键步骤，然而非线性全阶仿真的计算成本使得穷举参数搜索难以实现。本文提出一种基于降阶模型的快速最坏情况阵风识别方法，相比全阶非线性仿真，计算速度提升可达600倍。该方法采用泰勒级数展开和特征向量投影实现流固飞行动力耦合系统的非线性模型降阶。文中考虑了三个复杂度递增的测试案例：三自由度翼型（14个状态量，从1000个设计点中识别最坏情况）、类全球鹰无人机（540个状态量，80次参数计算，速度提升30倍）以及极柔飞翼布局飞行器（1616个状态量，37次参数计算从222小时缩短至22分钟）。研究表明，线性降阶模型在变形小于翼展10%时保持精度，而采用二阶泰勒展开的非线性降阶模型能准确捕捉大变形区域。该方法为将最坏情况阵风搜索集成至飞行器认证流程提供了实用工具。