Nonlinear Dynamic Aeroacoustic Performance of Nanocomposite-Enhanced Composite Structures for Improving the Stability of Sport Equipment: A Machine Learning Approach for Result Verification

The dynamic aeroacoustic performance and vibration control of advanced structural materials are critical in a stadium roof and as a sports equipment, where minimizing noise emissions and optimizing mechanical efficiency are essential. This study introduces a comprehensive theoretical model to investigate the aeroacoustic characteristics and sound radiation behavior of composite doubly curved panel structures reinforced with graphene platelet (GPL) nanocomposites. Utilizing the shear deformation theory, the proposed framework integrates aerodynamic forces and acoustic interactions, ensuring accurate predictions of nonlinear behavior under high-frequency excitation. The research emphasizes phase velocity (PV) variations to evaluate the system’s dynamic response and acoustic efficiency. Results indicate that the incorporation of GPLs within composite structures significantly improves PV, sound radiation efficiency, and vibration suppression — key factors for engineering applications. For results verification, a deep neural network as a machine learning algorithm is presented to verify the results. Additionally, the impact of GPL weight fraction, excitation frequency, and structural configuration on the nonlinear acoustic response is systematically examined. The findings underscore the potential of GPL-reinforced nanocomposites to enhance energy dissipation and reduce undesirable noise, providing valuable insights for optimizing composite structures for advanced sports equipment applications.