Contaminant concentrations dynamic prediction method for aircraft cabin based on estimating emission rates
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摘要: 随着大型民机飞行时间的延长,座舱空气污染事故发生概率也随之增大,快速准确的污染浓度预测对保证乘客生命安全具有重要意义.座舱各污染浓度的动态预测和污染源项强度辨识是实现座舱空气质量实时预测的关键技术.污染源项散发强度辨识,如采用最小二乘算法,参数估计是静态的,一般延迟较大;如采用单模卡尔曼滤波算法,虽能实现动态辨识,但不能同时兼顾稳态和过渡过程(突发污染)的参数估计性能,导致误差较大.为解决上述难题,本文提出基于源项辨识的飞机座舱污染浓度动态预测方法,同时完成污染源散发强度动态辨识和污染浓度状态实时预测.该算法由2个滤波器组成,分别用于匹配系统的稳态和突发过渡过程特征,提高浓度方程参数估计和状态预测性能,保证飞机座舱空气质量态势预测的快速性和准确性.仿真结果证实了该算法的有效性.Abstract: The probability of air pollution in aircraft cabin will be increased with the prolongation of passenger aircraft pilot time, so it is very important to predict the contaminant concentrations dynamically. The key is source emission rate estimation and concentration dynamic prediction method. For the source emission rate estimation, Minimum mean-square value can only get the static estimate values, while extended Kalman filter (EKF) can realize dynamic parameter estimation, but single EKF couldn't exhibit good ability both for the normal process and fault process. Therefore, a contaminant concentrations dynamic prediction method for aircraft cabin based on estimating emission rates was presented to solve this question. Double model filter was used to trace the steady state and transient state (sudden contaminant source happening) of system. The performance of parameter estimation and state prediction could be improved using this method, and then the accuracy and speed of air quality prediction could also be improved. Simulations were done to demonstrate the performance of algorithm.
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