SCI和EI收录∣中国化工学会会刊

中国化学工程学报 ›› 2019, Vol. 27 ›› Issue (11): 2712-2724.DOI: 10.1016/j.cjche.2019.02.029

• Process Systems Engineering and Process Safety • 上一篇    下一篇

Wind field reconstruction for the dispersion modeling of accidental chemical spills on complex geometry

Bing Wang, Feng Qian, Weimin Zhong   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  • 收稿日期:2018-11-13 修回日期:2019-01-23 出版日期:2019-11-28 发布日期:2020-01-19
  • 通讯作者: Bing Wang
  • 基金资助:
    Supported by the National Natural Science Foundation of China (21706069 and 61751305) and the Fundamental Research Funds for the Central Universities (222201814039).

Wind field reconstruction for the dispersion modeling of accidental chemical spills on complex geometry

Bing Wang, Feng Qian, Weimin Zhong   

  1. Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
  • Received:2018-11-13 Revised:2019-01-23 Online:2019-11-28 Published:2020-01-19
  • Contact: Bing Wang
  • Supported by:
    Supported by the National Natural Science Foundation of China (21706069 and 61751305) and the Fundamental Research Funds for the Central Universities (222201814039).

摘要: Chemical spills on complex geometry are difficult to model due to the uneven concentration distribution caused by air flow over ground obstacles. Computational fluid dynamics (CFD) is one of the powerful tools to estimate the building-resolving wind flow as well as pollutant dispersion. However, it takes too much time and requires enormous computational power in emergency situations. As a time demanding task, the estimation of the chemical spill consequence for emergency response requires abundant wind field information. In this paper, a comprehensive wind field reconstruction framework is proposed, providing the ability of parameter tuning for best reconstruction accuracy. The core of the framework is a data regression model built on principal component analysis (PCA) and extreme learning machine (ELM). To improve the accuracy, the wind field estimation from the regression model is further revised from local wind observations. The optimal placement of anemometers is provided based on the maximum projection on minimum eigenspace (MPME) algorithm. The fire dynamic simulator (FDS) generates high-resolution data of wind flow over complex geometries for the framework to be implemented. The reconstructed wind field is evaluated against simulation data and an overall reconstruction error of 9% is achieved. When used in real case, the error increases to around 12% since no convergence check is available. With parameter tuning abilities, the proposed framework provides an efficient way of reconstructing the wind flow in congested areas.

关键词: Wind field reconstruction, CFD, PCA, Extreme learning machine, Sensor placement

Abstract: Chemical spills on complex geometry are difficult to model due to the uneven concentration distribution caused by air flow over ground obstacles. Computational fluid dynamics (CFD) is one of the powerful tools to estimate the building-resolving wind flow as well as pollutant dispersion. However, it takes too much time and requires enormous computational power in emergency situations. As a time demanding task, the estimation of the chemical spill consequence for emergency response requires abundant wind field information. In this paper, a comprehensive wind field reconstruction framework is proposed, providing the ability of parameter tuning for best reconstruction accuracy. The core of the framework is a data regression model built on principal component analysis (PCA) and extreme learning machine (ELM). To improve the accuracy, the wind field estimation from the regression model is further revised from local wind observations. The optimal placement of anemometers is provided based on the maximum projection on minimum eigenspace (MPME) algorithm. The fire dynamic simulator (FDS) generates high-resolution data of wind flow over complex geometries for the framework to be implemented. The reconstructed wind field is evaluated against simulation data and an overall reconstruction error of 9% is achieved. When used in real case, the error increases to around 12% since no convergence check is available. With parameter tuning abilities, the proposed framework provides an efficient way of reconstructing the wind flow in congested areas.

Key words: Wind field reconstruction, CFD, PCA, Extreme learning machine, Sensor placement