地铁隧道全年逐时温度计算方法
摘要：采用STESS模拟方法得到基础工况下的地铁隧道全年逐时温度，观察其特征总结得出地铁隧道全年逐时温度预测模型的函数形式，全年逐时温度为日均温度叠加日逐时温度波动，其中日均温度为正弦型函数，日逐时温度波动为时刻的分段函数。对于考虑不同影响因素的工况进行了计算，通过响应面法给出了预测模型中年均温度、月均温度振幅和白天高峰期的温度波动值3个重点参数的预测公式，从而得出考虑了乘客人数、车厢质量、制动回收系数、土壤导热系数和单位体积热容这5个影响因素的地铁隧道全年逐时温度预测模型。
关键词：地铁隧道全年逐时温度预测模型日均温度日逐时温度波动
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[3] ZHANG Y,LI X F.Research on airflow and energy performance in PBD,PSD and PBD-PSD-combined environment control systems in subway[J].Sustainable cities and society,2018,42:434- 443.
[4] SU Z Y,LI X F.Sub-system energy model based on actual operation data for subway stations[J].Sustainable cities and society,2020,52:101835.
[5] ZHANG Y,LI X F.Methodology of developing operation strategy for VAC system in subway stations with PSDs and APDs[J].Energy and buildings,2021,253:111525.
[6] 秦旭，王曼.不同岩土类型对地铁隧道空气温度影响的模拟分析[C]//2016年全国铁道与城轨暖通学术年会文集，2016:109- 111.
[7] 唐莎.屏蔽门系统地铁隧道温度分布特性与监测方法研究[D].成都：西南交通大学，2017:5- 8.
[8] 张越，李晓锋.地铁区间隧道温度的预测模型研究[J].都市快轨交通，2021,34(5):134- 137.
[9] WANG Y,LI X F.STESS:subway thermal environment simulation software[J].Sustainable cities and society,2018,38:98- 108.
[10] ZHANG Y,LI X F.Monitoring and analysis of subway tunnel thermal environment:a case study in Guangzhou,China[J].Sustainable cities and society,2020,55:102057.
Calculation method for annual hourly temperature in subway tunnels
Abstract: The annual hourly temperature in the subway tunnel under the basic working conditions is obtained by using the STESS simulation method, and through observing its characteristics, the functional form of the annual hourly temperature prediction model of the subway tunnel is obtained. The annual hourly temperature is the daily average temperature superimposed by daily hourly temperature fluctuation, in which the daily average temperature is a sinusoidal function and the daily hourly temperature fluctuation is a piecewise function of time. The working conditions considering different influencing factors are calculated, and the prediction formulas of three key parameters in the prediction model, namely, the annual average temperature, the monthly average temperature amplitude and the temperature fluctuation value during the daytime peak period, are given by the response surface method. The annual hourly temperature prediction model of subway tunnels is obtained, which takes into account the passenger number, carriage quality, brake recovery coefficient, thermal conductivity and heat capacity per unit volume of soil.
Keywords: subway; tunnel; annual hourly temperature; prediction model; daily average temperature; daily hourly temperature fluctuation;
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