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：最坏阵风载荷的识别是极柔性飞机适航认证中的关键环节，然而非线性全阶仿真的计算成本使得详尽的参数化搜索难以实现。本文提出一种基于降阶模型（ROM）的快速最坏阵风识别方法，相较于全阶非线性仿真可实现高达600倍的计算加速。该方法通过对流-固-飞行动力学耦合系统进行泰勒级数展开和特征向量投影，实现非线性模型降阶。研究考虑了三个复杂度递增的测试案例：三自由度翼型（14个状态变量，从1000个设计点中识别最坏工况）、全球鹰式无人机（540个状态变量，80次参数计算实现30倍加速）以及极柔性飞翼布局飞机（1616个状态变量，37次参数计算从222小时缩减至22分钟）。研究表明线性降阶模型在变形量小于翼展10%时具有良好精度，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工况。该方法为将最坏阵风搜索集成至飞机适航认证流程提供了实用工具。