摘要
基于神经网络方法建立了公路隧道纵向通风在线控制模型,结合交通模型、空气动力学模型及污染模型,对隧道内污染物进行了通风控制过程的动态模拟.结果表明,当交通量在1 000～1 400 辆/h、污染物基准排放量在0.008～0.010 m3/(辆*km)范围内变化时,该系统能够依据交通量和污染物基准排放量的变化,相应增加或减少风机开启台数,使隧道内的CO体积分数控制在限制值(150×10-6)以下.
An on-llne control model for roal tunnel with longitudinal ventihtion was proposed based on neural networks and by combining traffic model, aerodynamics model and polhtion model. Simulation was conducted with the model, where the traffic volume changed from 1000 to 1 400 veh/h and the benchmark emission rate was from 0.008 to 0.010 m^3/（veh · km）. The results show that the proposed system increases or decreases the number of fans in realtime following the changes in the traffic volume and the benchmark emission rate, so that the volumetric fraction of CO, as a control target, is controlled just under the standard limit （150 ×10^-6）.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2005年第6期765-768,共4页
Journal of Southwest Jiaotong University
关键词
公路隧道
通风控制
神经网络
模型
模拟
road tunnel
ventilation control
neural network
model
simulation
