Fast-turn around methods to predict airfoil trailing-edge noise are crucial for incorporating noise limitations into design optimization loops of several applications. Among these aeroacoustic predictive models, Amiet's theory offers the best balance between accuracy and simplicity. The accuracy of the model relies heavily on precise wall-pressure spectrum predictions, which are often based on single-equation formulations with adjustable parameters. These parameters are calibrated for particular airfoils and flow conditions and consequently tend to fail when applied outside their calibration range. This paper introduces a new wall-pressure spectrum empirical model designed to enhance the robustness and accuracy of current state-of-the-art predictions while widening the range of applicability of the model to different airfoils and flow conditions. The model is developed using AI-based symbolic regression via a genetic-algorithm-based approach, and applied to a dataset of wall-pressure fluctuations measured on NACA 0008 and NACA 63018 airfoils at multiple angles of attack and inflow velocities, covering turbulent boundary layers with both adverse and favorable pressure gradients. Validation against experimental data (outside the training dataset) demonstrates the robustness of the model compared to well-accepted semi-empirical models. Finally, the model is integrated with Amiet's theory to predict the aeroacoustic noise of a full-scale wind turbine, showing good agreement with experimental measurements.

翻译：快速预测翼型后缘噪声的方法对于将噪声限制纳入多种应用的设计优化循环至关重要。在这些航空声学预测模型中，Amiet理论在精度与简洁性之间提供了最佳平衡。该模型的精度高度依赖于精确的壁压谱预测，而后者通常基于带有可调参数的单方程公式。这些参数针对特定翼型和流动条件进行校准，因此当应用于校准范围之外时往往失效。本文提出了一种新的壁压谱经验模型，旨在增强当前最先进预测的鲁棒性和准确性，同时将模型的适用范围扩展至不同翼型和流动条件。该模型通过基于遗传算法的人工智能符号回归方法开发，并应用于在NACA 0008和NACA 63018翼型上、多个攻角和来流速度下测量的壁压波动数据集，涵盖了具有逆压梯度和顺压梯度的湍流边界层。通过与实验数据（训练数据集之外）的验证，证明了该模型相较于公认的半经验模型具有更好的鲁棒性。最后，将模型与Amiet理论结合预测全尺寸风力涡轮机的航空声学噪声，结果显示与实验测量值吻合良好。