Design and Optimization Method for Scaled Equivalent Model of T-Tail Configuration Structural Dynamics Simulating Fuselage Stiffness

Abstract

The T-tail configuration, while offering advantages for large transport aircraft, is susceptible to peculiar aerodynamic phenomena such as deep stall and flutter, necessitating high-fidelity dynamic scaling for wind tunnel testing. In order to address the issue of similarity in the dynamic characteristics of scaled T-tail models, we propose a comprehensive optimization design method for dynamic scaled equivalent models of T-tail structures with rear fuselages. The development of an elastic-scaled model is accomplished through the integration of the least squares method with a genetic sensitivity hybrid algorithm. In this framework, the objective function is defined as minimizing a weighted sum of the frequency errors and the modal shape discrepancies ($1-$ Modal Assurance Criterion) for the first five modes, subject to lower and upper bound constraints on the design variables (e.g., beam cross-sectional dimensions). The findings indicate that the application of finite element modelling in conjunction with multi-objective optimization results in the scaled model that closely aligns with the dynamic characteristics of the actual aircraft structure. Specifically, the frequency error of the optimized model is maintained below 2%, while the modal confidence level exceeds 95%. A ground vibration test (GVT) was conducted on a fabricated scaled model, with all frequency errors below 3%, successfully validating the optimization approach. This GVT-validated high-fidelity model establishes a reliable foundation for subsequent wind tunnel tests, such as flutter and buffet experiments, the results of which are vital for validating the full-scale aircraft’s aeroelastic model and informing critical flight safety assessments. The T-tail elastic model design methodology presented in this study serves as a valuable reference for the analysis of T-tail characteristics and the design of wind tunnel models. Furthermore, it provides insights applicable to multidisciplinary optimisation and the design of wind tunnel models for other similar elastic scaled-down configurations.