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Qu Hongquan [1] and Wang Yongjiao [1] and Pang Liping [2] and Chang Haijuan [3] and Zhang Changnian [1]

Academic Editor: Manuel Ruiz Galan

, 1, , College of Information Engineering, , North China University of Technology, , Beijing 100041, , China, , ncut.edu.cn,

, 2, , School of Aeronautic Science and Engineering, , Beijing University of Aeronautics and Astronautics, , Beijing 100191, , China, , buaa.edu.cn,

, 3, , Center of Quality Engineering, , China Aero-Polytechnology Establishment, , Beijing 100028, , China,

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

A pollution incident has the potential harm on people, especially when it happens in an enclosed ventilation space, such as a cabin in a manned spacecraft, submarine, or aircraft [1, 2]. It is necessary to develop related technology to efficiently identify a sudden contaminant source so that control actions can be taken rapidly. Contaminant source identification is a process that realizes source term estimation reversely by using limited information. However, this inverse identification for a source is extremely difficult. In the recent years, researchers from different countries carried on some related studies on the source identification. Most of the methods used retrospective methods. They used the sensor observed data to estimate the unknown contaminant source and then reconstruct the polluting progress. Methods widely used include the analytical approach, the optimization approach, the probabilistic approach, and the backward computational fluid dynamics approach. Skliar, Ramirez, Mamonov and Tsai carried out much research about point source identification in an enclosed environment [3–5]. Liu, Zhai, Zhang, and Chen thoroughly researched to identify virus source in an aircraft cabin [6, 7]. Wagner, Mahar and Datta proposed an optimization method to identifying the source term with combined forward simulation, optimization and regression methods [8–10]. Neupauer, Wilson and Sohn et al. developed backward location and travel time possibilities to realize point source estimation [11, 12]. The above endeavors have been promoting the development of source identification.

In some actual instance, the emission process of contaminant sometimes is a continuous and not instantaneous process, and the measured data is influenced by sensor noise. Considering these two factors, a...

 

 

 

     

           

                                      

   

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