Monitoring and analysis of subway tunnel thermal environment: A case study in Guangzhou, China

Abstract Tunnel thermal environment has a great impact on the energy consumption and safety in the subway system. Due to the complexity of subway network structure and the influence of piston effect, analytical and numerical methods are limited in showing the real situation. In comparison, full-scale experiments are effective in verifying models and presenting the real situation in subway tunnels. In this study, the experimental method was adopted. The tunnel wall and air temperature were monitored in 5 subway lines in Guangzhou, China for more than 1 year. These selected tunnels have different operation conditions; namely operation year, train density, passenger number, and ventilation form. Based on long-term and multipoint monitoring, the temperature profiles were obtained. In aspect of time-varying features, it was found that the daily temperature profiles present different shapes nder different heat sources and ventilation situations. The temperature profiles in a typical year show 60 and 30–60 days delayed effect compared to outdoor temperature in winter and summer, respectively. In regard to the temperature distribution features, it was revealed that the temperature distribution within a tunnel is not sensitive to the length of tunnel but susceptible to ventilation condition; the tunnel temperature differences between two ends are 0.1-0.8℃ and 1.4-1.6℃ in high-ventilation and low-ventilation tunnels, respectively. Moreover, the possible influencing factors, including operation year, train density, passenger flow, and ventilation condition were analyzed. It was discovered that the factors related to the heat source (train density and passenger flow) have the most important impact, and the ventilation condition takes second place, while the operation years almost have no impact on the thermal environment in subway tunnels. The monitoring data are useful for both analytical and numerical studies and this study provides first-hand research data for subway operators.