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Non-intrusive reduced order model of urban airflow with dynamic boundary conditions

机译：具有动态边界条件的城市气流的非侵入性降低阶模型

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摘要

To address the problem that urban wind field simulations are limited by high computational requirements, a supervised machine learning framework for the non-intrusive model order reduction of urban airflow is proposed to provide fast predictions with dynamic boundary conditions. An integrated atmospheric modeling system is established by coupling the Parallelized LES Model (PALM) with the Urban Canopy Model (UCM) which embedded in the Weather Research and Forecasting (WRF) model to simulate the high-resolution urban wind field. Within this machine learning framework, encoders and decoders are trained to compress and reproduce the full-order airflow data. The eXtreme Gradient Boost regression models (XGBoost) are trained to map the boundary conditions to the latent vectors in the encoders and decoders. Thus, high resolution urban airflow can be predicted rapidly by the XGBoost-decoder models with the input of boundary conditions. By comparing four types of encoding-decoding models (i.e., the proper orthogonal decomposition models, autoencoders with linear/nonlinear fully connected neural networks and autoencoders with Convolutional Neural Network (AE-CNN)), we find that the AE-CNN model with advantages of portable model size and convenient updating procedure is capable for high resolution simulations of urban airflow with dynamic boundary conditions at a low computational cost.
机译：为了解决城市风野仿真受高计算要求受限的问题，提出了一种用于城市气流的非侵入式模型顺序减少的监督机器学习框架，以提供动态边界条件的快速预测。通过将平行化的LES模型（Palm）与嵌入在天气研究和预测（WRF）模型中耦合的平行化LES模型（PALP）来建立一体的大气建模系统，以模拟高分辨率城市风野。在本机中，学习框架，编码器和解码器培训被培训以压缩和再现全阶气流数据。训练极端梯度升压回归模型（XGBoost），以训练边界条件，以将边界条件映射到编码器和解码器中的潜伏向量。因此，通过输入边界条件的XGBoost-解码器模型可以快速预测高分辨率城市气流。通过比较四种类型的编码解码模型（即，适当的正交分解模型，具有线性/非线性完全连接的神经网络和具有卷积神经网络（AE-CNN）的Autoencoders的自动码器），我们发现AE-CNN模型具有优势便携式模型规模和方便的更新程序能够高分辨率模拟城市气流，具有低计算成本。

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  • 来源
    《Building and Environment》 |2021年第1期|107397.1-107397.10|共10页
  • 作者

    Xiang Songlin; Fu Xiangwen; Zhou Jingcheng; Wang Yuqing; Zhang Yizhou; Hu Xiurong; Xu Jiayu; Liu Huazhen; Liu Junfeng; Ma Jianmin; Tao Shu;

  • 作者单位

    Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China;

    Princeton Univ Princeton Sch Publ & Int Affairs Princeton NJ 08544 USA;

    Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China;

    Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China;

    Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China;

    Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China;

    Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China;

    Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China;

    Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China;

    Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China;

    Peking Univ Coll Urban & Environm Sci Lab Earth Surface Proc Beijing 100871 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Non-intrusive reduced order model; Urban airflow; Proper orthogonal decomposition; Autoencoder;

    机译：非侵入性减少阶模型;城市气流;适当的正交分解;自动化器;

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