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

2026-03-17

AI Summary

This study addresses the challenge of identifying worst-case gust load scenarios for ultra-flexible aircraft, a task hindered by the prohibitive computational cost of full-order nonlinear simulations that precludes integration into airworthiness certification workflows. To overcome this, the authors propose an efficient reduced-order modeling (ROM) approach that, for the first time, combines second-order Taylor expansion with eigenvector projection to construct a nonlinear ROM of the strongly coupled fluid–structure–flight dynamics system. The method maintains high fidelity under large deformations while dramatically accelerating computations—achieving up to a 600× speedup across three test cases, reducing a 222-hour simulation of a flying-wing aircraft to just 22 minutes. Results demonstrate that linear ROMs are only valid for wingtip deflections below 10% of span, whereas the proposed nonlinear ROM accurately captures large-deformation responses, enabling rapid worst-case scenario identification and seamless integration into certification processes.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• worst-case gust
• very flexible aircraft
• computational cost
• certification
• gust loads

Innovation

• reduced-order model
• worst-case gust identification
• very flexible aircraft
• nonlinear model order reduction
• fluid-structure-flight dynamics