Relative importance of certain factors affecting the thermal environment in subway stations based on field and orthogonal experiments

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Abstract

As subway stations in cold-climate regions typically have no heating systems, passengers often feel cold in such stations during winter. People desire a comfortable thermal environment, which is sensitive to certain factors. Therefore, identifying the key influencing factors is fundamental for improving thermal environment. In this study, two environmental factors (outdoor temperature and soil temperature) and four factors concerning operation conditions (train interval, platform screen door opening area, ridership, and the number of open piston vent shafts) were analyzed. An orthogonal experimental design was applied to efficiently identify influencing factors, assisted by field test and numerical study. Through range analysis and analysis of variance, it was discovered that the outdoor temperature and soil temperature were significant to the concourse environment and platform environment, respectively. Moreover, the analyses revealed the separate rankings of the relative importance of factors for the concourse environment and platform environment. Using these rankings, targeted strategies could be proposed more effectively for the controllable influencing factors in future research. The orthogonal experimental design is an efficient alternative to qualitative information collection for complex subway systems. This study provides new insights into subway thermal environment in winter, and is helpful in creating a more comfortable thermal environment.

Introduction

The public area (including the concourse and platform) in subway stations located in cold-climate regions, where the average ambient temperature in the coldest month is below 0 °C (Deliège & Nicolay, 2016; Kottek, Grieser, Beck, Rudolf, & Rubel, 2006; Peel, Finlayson, & McMahon, 2007), generally have no heating systems during winter, as they are designed to only guarantee temporary comfort for passengers. Previous studies have found that passengers feel cool or slightly cool at these stations (Chen, 2018; Ordódy, 2000; Yang et al., 2008). As people are becoming increasingly accustomed to an ideal indoor environment, passengers have begun to desire a more comfortable thermal environment in subways. To improve the thermal environment (which is complex and sensitive to different factors), the key influencing factor must be identified.

Researchers have discussed the factors influencing subway stations from different perspectives, such as environmental parameters (Ordódy, 2000; Yang, Zang, & Gong, 2013), operating conditions (Ampofo, Maidment, & Missenden, 2004b; Guan, Liu, Zhang, & Xia, 2018; Guan, Zhang, & Liu, 2018; Liu, 2003; Yang et al., 2013;), and design parameters (Guan, Liu et al., 2018; Liu, Zhu, & Wang, 2017; Pope, Newman, & Henson, 2000; Wang, 2007).

However, the effects of the factors influencing the thermal environment in winter have not been analyzed, and efficient experimental design strategies have not been applied. A good design of experiments (DOE) approach can facilitate the efficient collection of valid statistical information. Previous studies have followed the one-factor-at-a-time (OFAT) method (Liu, Li, Yang, & Zhang, 2017; Liu, Zhu et al., 2017; Wang, 2007; Wang et al., 2011), which varies the levels of only one factor at a time while the other factors are fixed (Czitrom, 1999). The OFAT method is labor-intensive and time-consuming when several factors are considered. For multivariate research, DOE is more efficient, as the method simultaneously changes several factors and requires fewer resources (such as experiments and time). The effect estimates of each factor are also more precise (Min, 2001). Orthogonal experimental design is an efficient strategy that selects only a fraction of all possible trials, whereas full factorial design conducts trials for all combinations of levels across all factors. Orthogonal experimental design applies an orthogonal array to uniformly arrange experiments. Therefore, all factor settings and all pairs of settings are tested simultaneously. Then, combined with range analysis and analysis of variance (ANOVA), the results can be analyzed to rank the factors according to their degree of influence, thereby identifying the key influencing factor.

In this study, an orthogonal experimental design was employed to elucidate the relative importance of factors influencing the complex subway thermal environment in a cold-climate region during winter. A flowchart for identifying the key influencing factors of the subway thermal environment is shown in Fig. 1. Based on a preliminary analysis and on-site measurement results, temperature was employed as the subway thermal environment index. Then, using the IDA Tunnel software, orthogonal experiments were conducted, and the results were analyzed to identify the key influencing factors, providing new insights for targeted strategy proposals for improving the subway thermal environment.

Section snippets

Field measurements

Xi’an (33°29’–34°44’ N, 107°40’–109°49’ E) is the largest city in northwestern China (Cao et al., 2005), and has a cold climate (the average temperature in the coldest month is below 0 °C) under the Köppen climate classification. The subway in Xi’an uses air-conditioning system only in summer, and it relies on natural ventilation both in winter and transitional seasons. The temperature of the constant soil temperature layer is 16 °C (Ren et al., 2012).

A typical subway station on the Xi’an Metro

Field experiments

The variations in the relative humidity inside and outside of the Xi’an subway station on the test day are shown in Fig. 7. The relative humidity inside the station was approximately 20–40 %, and it decreased near 15:30 as the temperature increased at the station. According to the thermal comfort zone in winter (ASHRAE Insights, 2002; ASHRAE, 2017; Parker, 1972), comfortable relative humidity range is 30–60 %, and sometimes a little higher than 70 % or as low as 25 % is still acceptable (

Discussions

Orthogonal experiments were conducted using IDA Tunnel, and the results were analyzed to detect the degrees of effects of different factors, thereby identifying the key influencing factor. In IDA Tunnel (EQUA Simulation AB, 2016) used in this study, the heat gains from passengers are calculated by the heat dissipated from a passenger and the ridership in station. The thermal resistance of clothes and the activity of passengers are also considered. As passengers are usually dressed in clothes

Conclusions

This study applied orthogonal design to provide a comprehensive analysis of the influencing factors (two environmental influencing factors and four factors concerning operation conditions) of a subway thermal environment in cold regions during winter. The orthogonal experiments conducted were aided by on-site measurements and IDA Tunnel simulations. The results were analyzed using range analysis and ANOVA, and the main findings are summarized below.

  • (1)

    As determined from the range analysis and

Declaration of Competing Interest

The authors declare no conflict of interest.

Acknowledgments

This study was supported by the National Key R&D Program of China (No. 2019YFC0605105) and the project of Key Technology of Thermal Environment Control of Subway Station in Northern China. Furthermore, the authors wish to acknowledge the support of China Railway First Survey and Design Institute Group Ltd., for their support to field experiments and numerical modelling for the present work. We are also grateful to Zhixiang Cao for the valuable advice and assistance during writing this

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