Rapid Worst-Case Gust Identification for Very Flexible Aircraft Using Reduced-Order Models

TL;DR

This paper introduces a reduced-order modeling approach that significantly accelerates the identification of worst-case gust loads in very flexible aircraft, enabling practical integration into certification processes.

Contribution

It develops a nonlinear model order reduction method using Taylor series expansion and eigenvector projection for rapid worst-case gust analysis.

Findings

Achieves up to 600 times speedup compared to full simulations.

Linear ROM accurate for deformations below 10% of wingspan.

Nonlinear ROM with second-order Taylor expansion captures large deformations accurately.

Abstract

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.