摘要
诸如载人航天器和大型飞机等密闭微环境,随着人员停留时间的延长,舱室突发污染问题已成为危害工作人员生命安全的主要因素,所以迫切需要开展突发不确定污染源辨识及危害性预测研究技术,以提高上述密闭环境主动应对突发污染的能力。本文提出一种新的浓度离散随机模型,并建立敏感性分析方法实现污染源定位及强度初步估计,之后利用隐式与显式卡尔曼滤波相结合的方法同时完成污染源散发特性的动态辨识及舱室空气污染物的浓度预测。上述研究能够实现污染源散发特性的快速准确辨识。仿真结果证实了该算法的有效性。
Along with the prolonging of people' s staying time in enclosed environments such as spacecraft, aircraft and so on, air pollution in the cabin has become a main factor which endangers occupants' life safety. Therefore, it' s crucial to develop investigation about identification of sudden unknown contaminant source and forecast of endangerment, which could help improve the ability of enclosed environments to deal with sudden contaminant. This paper presented a novel discrete concentration stochastic model, moreover, presented the sensitivity analysis algorithm which can identify source location and strength, then realize the dynamic identification of source emitting characters and prediction of contaminant concentration using combined implicit and explicit Kalman filter. The research above can identify the source emitting characters rapidly and accurately. Simulation results has proved the virtues of the novel method.
出处
《宇航学报》
EI
CAS
CSCD
北大核心
2010年第2期593-597,共5页
Journal of Astronautics
基金
国家自然科学基金资助(50808007)
关键词
污染源辨识
浓度预测
浓度离散随机模型
卡尔曼滤波
Source identification
Concentration prediction
Discrete concentration stochastic model
Kalman filter