Rapid Worst-Case Gust Identification for Very Flexible Aircraft Using Reduced-Order Models
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915937560559616 |
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| author | Tantaroudas, Nikolaos D. Karachalios, Ilias |
| author_facet | Tantaroudas, Nikolaos D. Karachalios, Ilias |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_16212 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Rapid Worst-Case Gust Identification for Very Flexible Aircraft Using Reduced-Order Models Tantaroudas, Nikolaos D. Karachalios, Ilias Computational Engineering, Finance, and Science 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. |
| title | Rapid Worst-Case Gust Identification for Very Flexible Aircraft Using Reduced-Order Models |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2603.16212 |