Kim Younghoon, Lee Soogab
Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea.
Department of Aerospace Engineering, Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea.
J Acoust Soc Am. 2024 Jan 1;155(1):171-187. doi: 10.1121/10.0024242.
A deep learning based method is proposed to predict the urban air mobility (UAM) noise propagation in the urban environment. This method aims to efficiently estimate the noise impact of UAM flights on the complex urban area. The noise hemisphere was created via the comprehensive multirotor noise assessment framework to determine the noise level of UAM. The noise propagation to a randomly generated three-dimensional (3D) urban area was then calculated using the ray tracing method, including atmospheric attenuation and multiple reflections. 45 000 two-dimensional noise maps were used to train and evaluate the modified convolutional neural network. The results demonstrated high accuracy, with a root mean square error of only 2.56 dB compared to the ray tracing method, while reducing computation time by more than 1800 times. This model was applied to analyze the noise impact of various UAM flight conditions and landing scenarios at a vertiport. This deep learning approach is a fast method with adequate accuracy for predicting UAM noise impact in 3D urban environments. Also, it can inform the development of noise based strategies for UAM operations.
提出了一种基于深度学习的方法来预测城市环境中的城市空中交通（UAM）噪声传播。该方法旨在有效估计UAM飞行对复杂城市区域的噪声影响。通过综合多旋翼噪声评估框架创建噪声半球，以确定UAM的噪声水平。然后使用射线追踪方法计算噪声传播到随机生成的三维（3D）城市区域的情况，包括大气衰减和多次反射。使用45000个二维噪声图来训练和评估改进的卷积神经网络。结果显示出高精度，与射线追踪方法相比，均方根误差仅为2.56dB，同时计算时间减少了1800倍以上。该模型被应用于分析垂直机场各种UAM飞行条件和着陆场景的噪声影响。这种深度学习方法是一种快速方法，对于预测3D城市环境中的UAM噪声影响具有足够的准确性。此外，它可以为UAM运营基于噪声的策略制定提供参考。
