Data Driven Simulation of Polluted Gas Dispersion Using Source Estimation and Particle Filter

doi:10.16182/j.issn1004731x.joss.201709030

Abstract

Abstract: The real-time prediction of the air contaminant dispersion in chemical industry park is important to the emergency management of air pollution accident. Due to the unknown source terms and the error of input parameters in the atmospheric dispersion model, the accuracy of traditional simulation is limited. A data driven atmospheric dispersion simulation based on source estimation and particle filter was proposed. Based on the results of the source estimation, particle filter was applied to assimilate the UAV observation into the dispersion model in real time to calibrate the system state and obtain more accurate prediction results. Experiments show that the proposed data driven atmospheric dispersion simulation method can predict the concentration distribution more precisely and provide strong support for emergency treatment.

Key words: air contaminant dispersion, data driven simulation, source estimation, data assimilation, particle filter, UAV

CLC Number:

TP391.9

Wang Rongxiao, Chen Bin, Qiu Sihang, Zhu Zhengqiu, Qiu Xiaogang. Data Driven Simulation of Polluted Gas Dispersion Using Source Estimation and Particle Filter[J]. Journal of System Simulation, 2017, 29(9): 2100-2108.

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