论文部分内容阅读
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.
With the flight time of large-scale civil aircraft, the probability of cockpit air pollution accidents also increases, the rapid and accurate pollution concentration prediction is of great significance to ensure the life safety of passengers.Dynamic prediction of pollution concentration in cockpit and the identification of pollution source item intensity are Such as using the least squares algorithm, the parameter estimation is static, the general delay larger; such as the use of single-mode Kalman filter algorithm, although the dynamic identification can be achieved, but to achieve dynamic identification of the cabin air quality real- In order to solve the above problems, this paper proposes a dynamic prediction method of pollution concentration of aircraft cockpit based on source item identification, and simultaneously dynamic identification of emission intensity of pollution sources And real-time prediction of pollution concentration.The algorithm consists of two filters, which are used to match the characteristics of steady-state and sudden transition of the system respectively, to improve the parameter estimation and state prediction performance of the concentration equation, and to ensure the rapid prediction of the air quality status of the cockpit Sexual and accuracy.The simulation results confirm the effectiveness of the algorithm.