用数据驱动方法快速精准预测城市街道峡谷的气流与污染物扩散。
End-to-end data-driven prediction of urban airflow and pollutant dispersion
- 通过谱本征正交分解和自编码器降维，构建低维动态表示。
- 利用LSTM在低维空间建模，实现长时间序列的气流预测。
- 适合城市环境模拟、空气质量预警与智慧城市建设人员参考。
气候变化与城市人口快速增长加剧了城市环境压力，城市大气流动行为对公共健康、能源消耗和宜居性至关重要。本文提出端到端数据驱动方法，用于快速准确预测街谷区域在掠流条件下的气流与污染物扩散。基于大涡模拟（LES）生成的一系列时序快照构成数据库。框架包含四个步骤：首先，采用谱本征正交分解（SPOD）提取低维基底，投影得到时间系数；其次，使用自编码器对时间系数进行非线性压缩，进一步降低维度；第三，在隐空间中利用长短期记忆网络（LSTM）学习降阶模型（ROM）；最后，通过卷积神经网络将预测的速度场映射为污染物分布。结果表明，该模型可在长时间范围内准确预测瞬时及统计稳态场。
原文摘要 · Abstract (English)
Climate change and the rapid growth of urban populations are intensifying environmental stresses within cities, making the behavior of urban atmospheric flows a critical factor in public health, energy use, and overall livability. This study targets to develop fast and accurate models of urban pollutant dispersion to support decision-makers, enabling them to implement mitigation measures in a timely and cost-effective manner. To reach this goal, an end-to-end data-driven approach is proposed to model and predict the airflow and pollutant dispersion in a street canyon in skimming flow regime. A series of time-resolved snapshots obtained from large eddy simulation (LES) serves as the database. The proposed framework is based on four fundamental steps. Firstly, a reduced basis is obtained by spectral proper orthogonal decomposition (SPOD) of the database. The projection of the time series snapshot data onto the SPOD modes (time-domain approach) provides the temporal coefficients of the dynamics. Secondly, a nonlinear compression of the temporal coefficients is performed by autoencoder to reduce further the dimensionality of the problem. Thirdly, a reduced-order model (ROM) is learned in the latent space using Long Short-Term Memory (LSTM) netowrks. Finally, the pollutant dispersion is estimated from the predicted velocity field through convolutional neural network that maps both fields. The results demonstrate the efficacy of the model in predicting the instantaneous as well as statistically stationary fields over long time horizon.
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