AI 中文总结
针对衬层温度变化导致SFANC降噪性能下降的问题，提出温度感知SFANC方法，采用多任务学习训练的轻量型1D CNN动态选择控制滤波器，数值模拟验证了其在不同工况下的降噪有效性。
AI 中文摘要
主动噪声控制（ANC）装饰板是抑制飞机多音调噪声的有效解决方案。选择性固定滤波ANC（SFANC）方法具有计算复杂度低、鲁棒性高、响应迅速的特点，适用于处理因发动机轴转速变化导致频率变化的多音调发动机噪声。但实际工况中，衬层温度变化会改变声学和结构路径，降低降噪性能。为应对该挑战，本文提出温度感知SFANC（TP-SFANC）方法，采用轻量型一维卷积神经网络（1D CNN），通过多任务学习策略训练。该网络处理参考信号与误差信号，学习频率和温度特性以动态选择最优控制滤波器。数值模拟表明，所提方法在不同频率和衬层温度下，对多音调噪声的衰减效果显著。
英文摘要
Active noise control (ANC) trim panels offer an effective solution to suppress multi-tonal noise in aircraft. The selective fixed-filter ANC (SFANC) method, characterized by low computational complexity, high robustness and rapid response, is suitable to handle multi-tonal engine noise that varies in frequency due to changes in the rotational speed of the engine shaft. However, real-world conditions introduce variations in lining temperature, altering acoustic and structural paths and degrading noise reduction performance. To address this challenge, a temperature-perceptive SFANC (TP-SFANC) approach is proposed that employs a lightweight one-dimensional convolutional neural network (1D CNN) trained using a multi-task learning strategy. By processing both reference and error signals, the 1D CNN learns frequency and temperature characteristics to dynamically select the optimal control filter. Numerical simulations demonstrate the effectiveness of the proposed method in attenuating multi-tonal noise across varying frequencies and lining temperatures.
