基于深度学习的预测城市空气流动在城市环境中的噪音传播
1Department of Aerospace Engineering, Seoul National University, Seoul, Republic of Korea.
The Journal of the Acoustical Society of America
|January 5, 2024
概括
一个新的深度学习模型准确地预测了城市中的城市空气流动 (UAM) 噪音. 这种方法显著减少了计算时间,有助于制定电动航空噪音减轻策略.
科学领域:
- 声学 声学 在声学方面
- 人工智能的人工智能
- 城市规划 城市规划
背景情况:
- 城市空气流动 (UAM) 运营正在扩大,引发了人们对城市环境噪音污染的担忧.
- 准确预测UAM噪声传播对于评估环境影响和公众接受度至关重要.
- 现有的噪声建模方法对于复杂的城市环境而言可能是计算密集的.
研究的目的:
- 开发一种高效,准确的基于深度学习的方法,用于预测城市空中交通噪声传播.
- 评估UAM飞行在复杂的三维城市环境中的噪音影响.
- 为UAM操作提供基于噪音的策略提供信息的工具.
主要方法:
- 一个深度学习模型,一个修改的卷积神经网络,使用45,000个噪音图进行了训练.
- 噪声水平是使用多轮轮噪声评估框架来确定的.
- 使用光线追踪方法计算噪声传播,其中包括大气衰减和多重反射.
主要成果:
- 深度学习模型实现了高精度,与光线追踪方法相比,根平均平方误差为2.56dB.
- 与传统方法相比,计算时间减少了1800倍以上.
- 该模型成功地分析了各种UAM飞行条件和着陆场景的噪音影响.
结论:
- 拟议的深度学习方法为预测3D城市环境中的UAM噪声提供了快速而准确的解决方案.
- 这种方法可以有效地支持开发用于可持续UAM集成的降噪策略.
- 该模型的效率使其适用于实时噪声影响评估.
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