Data-driven collaborative optimal design of acoustic black hole in panel flutter suppression

Abstract

This study delves into the flutter critical boundary of panel with an Acoustic Black Hole (ABH) and its optimization and proposed an innovative optimization framework that combines a data-driven approach with physical mechanisms. At the panel-ABH interface, an improved coupled model was established by considering both the z-direction displacements and the rotations around the x and y axes. Leveraging the modal condensation theory, a comprehensive analysis is performed on how various modal parameters intricately interact to influence the aeroelastic response. To achieve larger flutter boundary, a hybrid surrogate model is developed to effectively capture the relationships between multiple inputs and output objectives. A closed-loop verification mechanism linking the surrogate model’s prediction errors and finite element solutions is established, requiring only 225 model simulations to obtain an optimal design. The results show the optimized ABH can enhance the panel’s flutter boundary by up to 29.4 %, nearly tripling the initial effect, fully demonstrating the superiority of the optimization. Moreover, the analysis of ABH’s effective modal distribution and effective modal mass were conducted, thereby elucidating the mapping relationship between physical mechanisms and optimization performance, providing a theoretical basis for the superiority of the proposed method.