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23 December 2025

A Multi-Objective Point Response Prediction Method for Vertical Tail Buffeting Based on Elastic Scaling Transformation

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1
School of Mechanics and Aerospace Engineering, Dalian University of Technology, Dalian 116024, China
2
State Key Laboratory of Structural Analysis, Optimization and CAE Software for Industrial Equipment, Dalian 116024, China
3
Advanced Technology for Aerospace Vehicles of Liaoning Province, Dalian University of Technology, Dalian 116024, China
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This article belongs to the Special Issue Structural Dynamics Modelling, Aeroelastic Analysis and Experimental Verification Methods for Aircraft Systems

Abstract

Aircraft with a twin vertical tail and leading-edge extension configuration may experience vertical tail buffeting during high-angle-of-attack maneuvering flight. This issue can lead to structural fatigue damage in the vertical tail, shortening its service life and increasing maintenance costs, ultimately compromising flight safety. Therefore, accurate prediction of buffeting loads and responses is essential during design. In the preliminary stage, wind tunnel testing is the primary means to obtain dynamic data such as fluctuating pressure and acceleration response, which can be transformed to full-scale conditions through similitude principles. However, the elastic scaling model used in buffeting tests is usually established for a specific flight condition. When the flow velocity or objective flight condition changes, the similitude relationship becomes invalid, limiting the applicability of test results and preventing full-envelope strength verification. To overcome this limitation, this study proposes a multi-objective point response prediction method for vertical tail buffeting. The method enables the prediction of full-scale responses at multiple objective flight conditions using wind tunnel data that do not strictly satisfy similitude criteria. A complete aircraft vertical tail buffet (rigid/elastic) hybrid model was developed for testing, and an Adjusted Model incorporating elastic scaling transformation was established. The proposed method was validated through experiments, demonstrating improved test data utilization and prediction accuracy across multiple-objective flight conditions.

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