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Moving model test of the smoke movement characteristics of an on-fire subway train running through a tunnel
Tunnelling and Underground Space Technology 96 (2020) 103211
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Moving model test of the smoke movement characteristics of an on-fire subway train running through a tunnel Zhe Wanga,b,c, Dan Zhoua,b,c, Sinisa Krajnovicd, Hongkang Liua,b,c,
T
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a
Key Laboratory of Traffic Safety on Track (Central South University), Ministry of Education, Changsha 410075, Hunan, China Joint International Research Laboratory of Key Technology for Rail Traffic Safety, Changsha 410075, Hunan, China c National & Local Joint Engineering Research Center of Safety Technology for Rail Vehicle, Changsha 410075, Hunan, China d Division of Fluid Dynamics, Department of Applied Mechanics, Chalmers University of Technology, 41296 Gothenburg, Sweden b
A R T I C LE I N FO
A B S T R A C T
Keywords: Moving fire source Moving model test Piston effect Smoke movement Subway tunnel
A moving model test was carried out to investigate the associated smoke movement characteristics when a subway train on fire runs in a tunnel. Train models of the 1/10 and 1/15 scales were used. The spatial distributions of airflow velocity and smoke concentration were then analyzed, and the differences between moving fire sources and stationary fire sources were discussed. The results show that the smoke movement characteristics of a stationary fire source were greatly different from those of a moving one. Specifically, the smoke movement for the moving fire source was dominated by piston wind. Moreover, the process of the smoke spread could be divided into three stages, during which time the flow direction changed. The peak smoke concentration value occurred after the train tail passed by the measuring point. Besides, the impacts of train speed (60 km/h, 80 km/h, 100 km/h, and 120 km/h) and blockage ratio (0.19 and 0.43) on airflow velocity and smoke concentration were also investigated. With increasing train velocity, the airflow velocity increased, and the smoke concentration decreased. The maximum airflow velocity was approximately linear with the train velocity. Furthermore, the increasing blockage ratio enhanced the piston effect in the tunnel, thus increasing the airflow velocity and reducing the smoke concentration.
1. Introduction As a major mode of transportation in modern society, subways are being built in an increasing number of cities due to its convenience and efficiency. However, as subway traffic continues to boom, the operational security of subway systems is becoming increasingly critical and being paid remarkable attention. Fires are one of the most significant threats to the secure operation of subway systems. Because of the relatively closed internal space of the subway tunnel, it is difficult to discharge the high-temperature toxic smoke generated in the event of a fire, which not only damages the tunnel structures but also prevents the escape safety of subway train passengers. Generally, the source of subway tunnel fires can be divided into two types: stationary fires, which have a stationary source, and moving fires with a moving source. Stationary fires are mostly caused by the aging and failure of internal facilities in the tunnel, whereas moving fires are mostly caused by the failure of electrical equipment during the operation of a subway train. When an operating train is on fire, the intense piston wind generated in the tunnel has a significant impact on the
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smoke movement, which makes the smoke movement characteristics in the tunnel more complicated than those with stationary fire sources. In recent years, lots of scholars have conducted extensive research on stationary fires in tunnels. Their work mainly focus on four angles: (1) the distribution of temperature along the longitudinal and vertical directions of the tunnel (Zhao et al., 2018; Li et al., 2011; Ingason and Li, 2010; Tang et al., 2017; Meng et al., 2018; Tang et al., 2017; Yan et al., 2017); (2) the distribution of smoke along the longitudinal and vertical directions of the tunnel (Giachetti et al., 2017; Zhou et al., 2017; Wang et al., 2019; Hu et al., 2010; Tang et al., 2014; Hu et al., 2007); (3) the smoke back-layering length and its dimensionless expression, especially considering the blockage effect caused by a subway train inside a tunnel (Zhang et al., 2016; Weng et al., 2015; Tang et al., 2016; Wang et al., 2016; Gannouni and Ben Maad, 2016; Wu et al., 2018); and (4) the critical velocity and its dimensionless expression (Jiang et al., 2018a; Lee and Ryou, 2016; Li et al., 2010; Wang et al., 2017; Yi et al., 2014). The results shows that when the fire source is stationary, the longitudinal smoke flow velocity and the concentration distribution in the tunnel are symmetric about the fire source under
Corresponding author. E-mail address: [email protected] (H. Liu).
https://doi.org/10.1016/j.tust.2019.103211 Received 25 April 2019; Received in revised form 27 October 2019; Accepted 19 November 2019 0886-7798/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Schematic of moving model experimental device.
mostly been studied through numerical simulations. Moreover, the simulations are generally set to slow down and stop when the train catches fire, resulting in a lack of knowledge on the scenario when an onfire train runs in the tunnel at a constant velocity. In tunnel fire model tests, the model trains are also greatly simplified, and the movement of the train is mostly simulated by relative motion. Although this method can reflect the effect of piston wind generated by an operating train, the relative movement between a fire source and tunnel cannot be achieved since the location of the fire source does not change in reality. Therefore, the simulations are quite different from the actual operation case of an on-fire train. In this study, two model trains (with scales of 1/10 and 1/15) were adopted, and the smoke movement characteristics when an on-fire train ran through a tunnel were acquired through pressure sensors and concentration sensors. Additionally, numerical simulations were used to further illustrate the smoke movement characteristics. Then, the longitudinal and vertical distribution of airflow velocity and smoke concentration was investigated. Through a detailed comparison between the moving model test and numerical simulations, the differences in smoke movement characteristics between moving fire sources and stationary fire sources were compared and discussed. In addition, the effects of train velocity and the blockage ratio on the airflow velocity and smoke concentration were determined quantitatively.
natural ventilation; both of them decrease with the increasing distance to the fire source. In addition, the smoke mainly concentrates underneath the ceiling; below this smoke layer, a fresh air layer is maintained, and it is driven toward the heat source. Thus, smoke concentration tends to increase with increasing height. Moreover, when longitudinal ventilation is operating in a tunnel, the diffusion of smoke is hindered against the direction of ventilation. When the ventilation velocity is larger than the critical velocity, the back-layering length drops to zero; in other words, all the smoke spread to one side of the fire source. Thus, on the other side of the fire source, the smoke concentration is very low. When the ventilation velocity is too high, the stratification of smoke is destroyed due to the strengthening of shear stress. However, there have been relatively few studies on smoke movement in the case of a running subway train on fire. Based on the computational fluid dynamics (CFD) method, Xie et al. (2010) simulated the train movement by using equivalent winds induced by a train and then investigated the track of smoke when an on-fire train ran through the tunnel. Their study showed that the piston wind dominated the smoke movement, and all the smoke moved downstream instead of upstream. Zhou et al. (2015) adopted a dynamic mesh technique to study the law of smoke diffusion when the bottom of a subway train was on fire and the train was forced to stop in the tunnel. During the early stage of deceleration, the smoke spread to the train tail since the train velocity was greater than the piston wind velocity. As the train velocity further decreased, the front of the train was gradually covered with smoke because the residual piston wind velocity was larger than the train velocity. Furthermore, Zhang et al. (2018) established a threedimensional full-scale calculation model of the subway tunnel to research the smoke spread velocity around a fire source when a train running at a constant velocity caught on fire and then slowed down to stop in the middle of the tunnel. During the early stage of stopping, the residual piston wind caused the smoke around the fire source to move downstream. When the piston wind completely disappeared, however, the smoke moved upstream. The greater the residual piston wind in the tunnel was, the later the flow direction changed. Similarly, Xi et al. (2015) used sliding mesh technology to study the variation in the velocity field in the tunnel when an on-fire train ran at different velocities. They pointed out that the direction of airflow in the annular space between the train and the tunnel was opposite to the velocity of train, and a higher train velocity generated a higher airflow velocity. Based on the principle of relative motion, Xi et al. (2016) also investigated the relationship between the heat release rate and train velocity by using ventilation to simulate the piston wind. In summary, the piston wind generated in a tunnel has been found to be the main influencing factor in the smoke movement when an onfire train runs through a tunnel. However, because of the unsteady flow field in the tunnel, the smoke movement driven by the piston wind is more complicated. Due to the limitation of real vehicle tests, the smoke movement characteristics during the operation of an on-fire train have
2. Experimental apparatus The experiment was carried out using a moving model device from the Key Laboratory of Track Traffic Safety's moving model device at Central South University, China. The moving model test can simulate the train’s movement and the unsteady flow around the train when the train runs through the tunnel. Thus, it can be used to study the impact of the piston wind on the smoke movement characteristics of an on-fire train running through a tunnel. 2.1. Moving model rig design and operation Fig. 1 shows a schematic of the moving model device (Zhang et al., 2017; Meng et al., 2019). The device consists of two layers: an upper layer, which is the track that the model train runs along, and the bottom layer, which is the track that the power transfer car runs along. The train velocity is determined by the traction of the power transfer car. The test track is 164 m long and can be divided into three sections according to the running state of the train: the accelerating section, the test section (the uniform-speed section), and the braking section. The accelerating section and the braking section are 57 m long, and the test section is 50 m long. The basic principle of the moving model test was to pull the driven car and the model train connected to it backward by using a line extremity winch. The power control system controlled the tension on the driven car. At the same time, the power transfer car at the bottom layer 2
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also moved backward, such that the elastic rope was gradually tensioned. When the tension reached a specified value, the control system maintained constant tension, and the model train was in the to-belaunched state. Afterward, the solenoid valve was controlled by the decoupling device of the driven car. The release of the decoupling device and the elastic rope rebound drove the forward movement of the power transfer car and the model train. When the model train reached the entrance of the test section, it automatically separated from the power transfer car. Then, the model train was launched and ran at a high speed along the track through inertia. To ensure that the velocity of the train entering and leaving the test section met the test requirements, photoelectric sensors were installed at the entrance and exit of the tunnel. Moreover, a high potential was triggered as the train passed the sensor. The train velocity could be accurately calculated by the duration of the high potential. When the train ran from the test section to the braking section, the braking system rapidly decelerated the model train. To ensure smooth deceleration, the braking system adopted a combination of various mechanical braking methods, including a friction brake, a piston brake, and a brake disc.
Fig. 3. Transparent tunnel model.
transparent Plexiglas, as presented in Fig. 3. For convenience of description, the direction of the running train was defined as the x axis, with the origin specified at the entrance of the tunnel; the y axis was defined as the center line of the track outward, with the origin specified at the center of the track; and the z axis was vertically upward, originating at the rail surface. According to researches by Gannouni and Ben Maad (2016), smallscale experimental models can be divided into two types: cold models in which the heat source is represented by an injection at ambient temperature of a fluid with a different density from that of the surrounding fluid, and hot models in which the fire is represented by a heat source such as a gas burner or a thermal resistor. The cold models have been widely used in previous studies (Clanche et al., 2014; Michaux and Vauquelin, 2009; Vauquelin, 2008; Vauquelin and Megret, 2002). In this study, in order to ensure the safety of the test, the cold model was also selected to simulate smoke generated by an on-fire train. Specifically, a smoke generator was installed inside the train head. It generated smoke steadily and continuously, and the time of duration was larger than 2 min, which satisfied the requirement of smoke generation. Moreover, the smoke particles produced by the smoke generator were extremely small, and the density of the smoke was about 0.7 kg/m3 (lower than that of air), so that it could diffuse into the air quickly and spread upward. Based on the above all, the current model could be feasible to simulate the smoke movement driven by the piston wind.
2.2. Experimental model and smoke generation device In this test, the model train consisted of three coaches: a head car, a middle car, and a tail car. To simulate the piston effect when a real subway train runs through a tunnel, complex parts including an equipment cabin and bogies were added to the model train, as shown in Fig. 2. The model train was made by 3D printing. To study the impact of the blockage ratio on the smoke movement, we used two model trains with different scales. The research by Niu et al. (2018) indicated that the scale of train model had a significant effect on the flow field around the train. With an increase in model scale, the flow field around the train becomes closer to the result of the full-scale model. Therefore, two relatively large-scale train models, including 1/10 and 1/15, were selected to validate our methodology in this study. The length, width, and height of the 1/10 scale train were Ltr = 7.09 m, W = 0.313 m, and H = 0.367 m, respectively, and the cross-sectional area of the train was 0.095 m2. For the 1/15 scale train, the length, width, and height were Ltr = 4.73 m, W = 0.209 m, and H = 0.245 m, respectively, and the cross-sectional area of the train was 0.042 m2. Generally, the velocity range of the subway trains is 60–80 km/h. However, with the increasing passenger flow, the velocities of the subway trains in some cities have gradually increased to 120 km/h to shorten the running time, such as in Guangzhou and Shenzhen. To obtain a more thorough understanding, therefore, the test velocities selected in this study were within 60–120 km/h. The model tunnel used in the test was a single-track one with a cross-sectional area of 0.22 m2, and its length was 35.4 m. For the two different scaled trains, the blockage ratios were 0.43 and 0.19, respectively. To facilitate the observation and recording of smoke movement in the tunnel, part of the model tunnel was made of
2.3. Measurement setup and test system The arrangement of the measuring points for airflow velocity and smoke concentration is shown in Fig. 4. There were 11 airflow velocity measuring points, located at the T1 to T11 positions, and seven smoke concentration measuring points were located at T3 to T9 positions. This arrangement facilitated the analysis on the changes of airflow velocity and smoke concentration along the longitudinal and vertical directions of the tunnel. The heights of the measuring points T5, T6, and T7 were 0.49 m, 0.32 m, and 0.17 m, respectively. Since the center line of the tunnel overlapped with the center line of the track, the measuring points were only placed on the right side of the forward direction of the train. The test system included a data acquisition device and a data storage device. Five-hole probes were installed on the tunnel surface, and the airflow velocity was measured by the probes with the help of pressure sensors (with an accuracy of ± 0.04%); the smoke concentration was measured by concentration sensors (with an accuracy of ± 0.5%). The electrical signals collected by the data acquisition device were then transmitted to the data storage device through an optical cable for subsequent analysis. 2.4. Numerical simulation
Fig. 2. Subway train and tunnel mode.
To further explain the smoke movement characteristics when an on3
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Fig. 4. Arrangement of measuring points on the tunnel surface (mm). (a) Front view. (b) Left view.
Fig. 5. Computational domain and mesh.
Fig. 6. Repeatability test results and numerical calculation results validation. (a) Airflow velocity. (b) Smoke concentration.
Fig. 5. To achieve a well-developed flow field and avoid the impact of boundary conditions, the train was placed at a distance of 13 H from the tunnel entrance and 47 H from the back face of the computational domain. The no-slip wall conditions were applied into the top, side, and ground faces of the computational domain.
fire train runs through a tunnel, a numerical model corresponding to the moving model test was established by using Ansys 18.1. The 1/10 and 1/15 model train were selected, as well as the tunnel model used in the moving model test. The fire source was located at the bottom of the head car, and the velocity was set as 60 km/h. The fire-smoke governing equations adopted in the numerical simulation were based on the combination of three-dimensional compressible unsteady NavierStokes equations and the RNG k-ε turbulence model. The schematics of the computation domain, boundary conditions, and mesh was shown in
3. Results and discussion For convenience of analysis and comparison, the airflow velocity 4
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1/10 scale model train ran through the tunnel at a velocity of 60 km/h. In the figure, t = 0 corresponded to the moment of time interval of 1 s before the train head reached the tunnel entrance. Similarly, t = HP and t = TP denoted the moments when the head and tail of the train passed the measuring point, respectively. As shown in Fig. 7, when the train entered the tunnel, the airflow velocity in front of the train head began to increase. After the train had fully entered the tunnel, the airflow velocity fluctuated at around 0.38, corresponding to zone 1 in the figure. Once the train head reached the measuring point, both the airflow direction and the value changed suddenly, and the airflow velocity remained negative before the train tail reached, as shown in zone 2. Then, as the train tail passed by T6, a large negative pressuredominated zone, namely the wake region, occurred behind the train tail. Under the vacuum suction of the wake region, the surrounding air rapidly flowed in, reversing the airflow direction again. At the moment, the flow velocity reached its peak of around 0.57, referring to zone 3 in the figure. As the train left T6, the wake effect at the measuring point weakened, which caused the airflow velocity to decrease gradually. According to the analysis above, obviously the smoke movement could be divided into three stages when an on-fire train runs in the tunnel. The smoke generated at the bottom of the head car firstly spread to the train tail. After that, it moved upward due to the buoyancy. More importantly, it soon spread downstream, driven by the piston wind behind the train tail. It was noted that the flow state of smoke under a moving fire source apparently differed from that under a stationary fire source. For the latter, the smoke on both sides of the fire moved towards the openings, and the direction of airflow would not change (Chow et al., 2016). Due to the diverse flow states, the forces of smoke were different accordingly. Generally, the driving forces of smoke mainly consisted of buoyancy and inertia, and the larger airflow velocity generated greater inertia effects (Zhong et al., 2015; Eftekharian et al., 2019; Yao et al., 2019). To clearly distinguish their effects, Jiang et al. (2018b) suggested that the dominant contributing force to smoke movement could be derived from the following equation:
Table 1 Repeatability test results of maximum airflow velocity and smoke concentration. Variables
(u/V)max (C/Cref)max Diff./Ref.(%)
Airflow velocity
Smoke concentration
Test 1
Test 2
Test 3
NS
Test 1
Test 2
Test 3
NS
0.561 /
0.573 / 2.1
0.568 / 1.2
0.570 / 1.6
/ 0.995 /
/ 0.983 −1.2
/ 1.008 1.3
/ 0.989 −0.6
and the smoke concentration were dimensionless, represented by the velocity ratio u/ V and the concentration ratio C / Cref respectively. In which, u denotes the airflow velocity in the x direction, and V represents the train velocity. C is the smoke concentration at a certain measuring point, and Cref is the mean of the maximum smoke concentration measured at the measuring point T6 when the 1/10 scale model train ran through the tunnel at a velocity of 60 km/h in the repeatability test. 3.1. Repeatability test and numerical calculation results validation To ensure the accuracy of the results, 10 repeatability tests were carried out for each case. Fig. 6 shows the repeatability verification of three test data sets collected using the 1/10 scale model train, compared with numerical predictions. The train velocity in the test was 60 km/h. Specifically, Fig. 6a displays the airflow velocity curves of T6 in the tunnel, and Fig. 6b presents the smoke concentration curves at this measuring point. From the figures, the trends of the four curves were generally consistent. Those few differences were inevitable since it was impossible to completely reshape the airflow in the moving model test. Table 1 tabulated the three results for the maximum airflow velocity and smoke concentration of the 10 repeatability tests, and the results of the numerical simulations were also listed (NS represents the numerical simulation). The standard deviations of airflow velocity and concentration were 0.004 and 0.011, respectively. The results of airflow velocity and smoke concentration were within 95% confidence intervals. As the table showed, the differences for both the maximum airflow velocity and the maximum smoke concentration were within 3%. Moreover, the airflow velocity and smoke concentration curves of other measuring points also presented good repeatability.
Fr =
v2 , gH
(1)
where Fr was the Froude number, v was the airflow velocity, g denoted the gravitational acceleration of 9.8 m/s2, and H stood for height of the tunnel. If Fr > 1, the inertia force mainly dominated the smoke flow; otherwise, the buoyancy force made a dominating effect. As shown in Fig. 7, when the vehicle velocity was 60 km/h, the maximum piston wind velocity could reach about 9.5 m/s. By substituting v in Eq. (1), we can see that, Fr is larger than 1 when an on-fire train running in the tunnel. In other words, the piston wind dominated the movement of smoke in our study. Fig. 8 showed the smoke concentration variation at measuring point T6. At 2 s, the train head reached T6, and the train tail left T6 at 2.48 s. It could be seen that the smoke concentration at T6 did not increase until the train tail passed by the measuring point. This was because the smoke generated by the train head was confined to the space underneath the bottom of the train and spread to the train tail. Therefore, the concentration did not increase when the train head reached T6, although the on-fire position was located at the train head. At the moment the train tail passed T6, some of the smoke behind the train tail gathered at the measuring point, and thus the smoke concentration rose rapidly to its peak. Afterward, the smoke gathering near the measuring point was continuously diluted by the piston wind and then moved to the downstream, which reduced the smoke concentration to the level that it was at before the on-fire train passed by. As a comparison, when the fire source is stationary, the smoke concentration in the tunnel would accumulate continuously and remain at a high level after reaching a stable state (Zhao et al., 2018). Fig. 9 shows the concentration field around an on-fire train at
3.2. Smoke movement characteristics with on-fire train running through the tunnel Fig. 7 showed the airflow velocity at measuring point T6 when the
Fig. 7. Airflow velocity variation with an on-fire train running through the tunnel. 5
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zone 1. In zone 2, it could be found that the amplitude of the airflow velocity at T1 was greater than those at the other measuring points. This phenomenon could be interpreted by the fact that when the train head reached T1, the airflow velocity in front of the train head was still low, and the volume of air discharged from the tunnel exit was small. As a consequence, a larger amount of air flowed back to the tunnel entrance through the annular space between the train and the tunnel in the same duration. In zone 3, the maximum airflow velocities at T1 and T11 were smaller than that at T6. This was because T1 and T11 were more closer to the tunnel entrance and exit. It also could be seen that the maximum velocity ratios at the other points were nearly the same, except for those near the tunnel entrance and exit. By contrast, when a stationary train caught fire in a tunnel, the peak value of smoke velocity occurred near the fire source. On both sides of the fire source, the spread directions of smoke were opposite, and the velocity decreased with an increase in the distance from the fire source (Chow et al., 2016). Fig. 12 displayed the airflow velocity distributions at different heights at the same cross section of the tunnel. Measuring point T5 was located at the top of the tunnel, 0.49 m away from the rail surface; T6 was 0.32 m away from the rail surface; and T7 was 0.17 m away from the rail surface. From the figure, the nearly consistent peak values of the airflow velocity could be observed at different heights in zone 1 and zone 2. Furthermore, the impact of height on airflow velocity was mainly reflected in zone 3. As the height increased, the maximum airflow velocity decreased. Compared to T5, the maximum airflow velocity at T7 increased by 31.8%. The primary reason for this is the difference in the strength of the wake effect at different heights. The structures of the bogies and skirts at the train bottom are rougher than the surface of the train body, causing greater disturbances. Thus, as the height enlarged, the wake effect was gradually weakened, and the maximum airflow velocity at the top of the tunnel declined. This was significantly different from the typical distribution of airflow velocity in the vertical direction when the fire source was stationary. For a stationary fire source, the flow directions in the upper and lower part of the tunnel were opposite. The upper hot smoke spread toward the tunnel openings, whereas the lower fresh air moved toward the fire source (Gannouni and Ben Maad, 2016). Fig. 13 showed the smoke concentration curves at measuring points T3, T6 and T9. Apparently, they possessed the identical maximum smoke concentration, and it took nearly the same duration for the smoke concentration to rise and then return to the initial level. This could be attributed to the similar maximum airflow velocity at these measuring points. When the power of the fire source was constant, the airflow velocity determined the degree of smoke dilution. However, researches had indicated that for stationary conditions, the smoke concentration at different locations was only related to the distance
Fig. 8. Smoke concentration variation with an on-fire train running through the tunnel.
different times. As the figure displayd, there was no smoke in front of the head car; all the smoke generated by the train head spread to the train tail along the bottom of the train. Comparing Fig. 9a with Fig. 9b and 9c, we found that when a running train suddenly caught fire, the smoke concentration around the train first increased and then maintained at a certain level. Additionally, throughout the train range, the smoke was confined underneath the bottom of the train. Nearly no smoke could be found in the annular space formed by the tunnel ceiling and train roof. After the smoke reached the train tail, the smoke plume inclined with the ground and spread upward, as shown in the red circle. However, the smoke was rapidly diluted by the piston wind. Thus, the concentration behind the train tail declined rapidly after a rapid rise. Fig. 10 represented the airflow velocity at the front, middle and rear of the tunnel for the 1/10 scale train with the velocity of 60 km/h. Measuring points T1, T6 and T11 were 2.5 m, 17.7 m and 32.9 m away from the tunnel entrance respectively. Moreover, as shown in Fig. 11, the airflow velocity variations at different longitudinal positions in the tunnel. By comparing each curve in zone 1, we found that the variation of T1 was different from that of the other measuring points. This could be ascribed to the varying distances from these measuring points to the tunnel entrance. When the train head reached T1, the train did not fully enter the tunnel. Therefore, the airflow velocity at T1 in zone 1 was smaller than those at the other measuring points. In addition, since the distances from T6 and T11 to the tunnel entrance were larger than the length of the train, the airflow velocity at these two measuring points continued to increase until the train fully entered the tunnel. Thus, the maximum airflow velocities were generally identical at T6 and T11 in
Fig. 9. Concentration field around an on-fire train at different times. (a) Train tail 4.5 m from the tunnel entrance. (b) Train tail 8.9 m from the tunnel entrance. (c) Train tail 17.7 m from the tunnel entrance. 6
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Fig. 10. Longitudinal distribution of airflow velocity. (a) Measuring point T1. (b) Measuring point T6. (c) Measuring point T11.
Fig. 11. Maximum airflow velocity along the longitudinal direction.
Fig. 12. The vertical distribution of airflow velocity.
from the fire source. In the process of diffusion, smoke was diluted by the entrainment of fresh air into the smoke flow. Thus, the larger the distance was, the smaller the concentration was. Eventually, the whole tunnel would be filled with smoke (Hu et al., 2010). For a moving fire source, because the piston wind velocity was always less than the vehicle velocity, the smoke could not spread to the front of the head car. In the rear of the tail, with fresh air continuously inhaled from the tunnel entrance, the smoke concentration could rapidly decrease after rising. Fig. 14 plotted the smoke concentration at different heights. As the height increased, the smoke concentration diminished. In terms of T6,
the maximum smoke concentration at T5 decreased by 65.6%. This phenomenon could be explained by the angle of the smoke plume in Fig. 17. Because the velocity of piston wind in the x-axis direction was much larger than the upward movement velocity of smoke, the smoke rarely reached the top of the tunnel. As a result, the concentration at the top of the tunnel was low. This phenomenon was contrary to that of a stationary fire source. For the latter, the smoke was mainly at the top of the tunnel, and the concentration increased with an increasing height. Basically, the smoke stratification could be discerned in the smoke spreading process (Tang et al., 2014). Besides, as shown in Fig. 14, the duration for the concentration at T6 from rise to fall was longer, while 7
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Table 2 Maximum airflow velocity at different train velocities. Vtrain
T1 (u/V)max
T5 (u/V)max
T6 (u/V)max
T7 (u/V)max
T11 (u/V)max
60 km/h 80 km/h 100 km/h 120 km/h
0.403 0.414 0.413 0.409
0.466 0.476 0.465 0.472
0.561 0.553 0.548 0.551
0.614 0.605 0.602 0.611
0.491 0.497 0.493 0.487
velocities, dimensionless time was adopted for the abscissa in the figure. The time was normalized by:
t' = Fig. 13. The longitudinal distribution of smoke concentration.
t Vtrain, 5Ltr
(2)
where t was the actual operation time (s) of the train, Ltr was the train length (m), and Vtrain was the train velocity (m/s). According to Fig. 15, the maximum velocity ratios were basically consistent at the different train velocities, indicating that the maximum airflow velocity increased linearly with the increase in train velocity. The similar results were obtained by Xi et al. (2015) and Zhang et al. (2018). This was because the larger the train velocity, the greater the piston effect as well as the greater the positive pressure in the front of the train and the greater the negative pressure behind the train. In other words, a larger train velocity could accelerate the spread of smoke. Table 2 summarized the maximum airflow velocities at measuring points T1, T5, T6, T7 and T11. The airflow velocity data at T1, T6 and T11 showed that the train velocity had no effect on the longitudinal distributions of the airflow velocity ratios. Moreover, the maximum airflow velocity ratio in the middle of the tunnel was still higher than that at the tunnel entrance and exit. However, the differences in airflow velocity between T1, T6 and T7 increased with an enlarging train velocity. This caused the smoke to reach the train tail more rapidly when the train was running in the middle of the tunnel than at the tunnel entrance and exit. Besides, the airflow velocity results at T5, T6 and T7 showed that the maximum airflow velocity at the top of the tunnel was still the lowest at the same cross-section. Fig. 16 presented the smoke concentrations at measuring point T6 at four different train velocities. Similarly, Table 3 listed the maximum smoke concentrations at measuring points T3, T5, T6, T7 and T9. The figure illustrated that the maximum smoke concentrations in the tunnel decreased as the train velocity increased. In addition, the larger the train velocity was, the more the concentration decreased. As the train velocity increased from 60 km/h to 80 km/h, the smoke concentration at T6 decreased by 15.58%. However, it decreased by 34.98% as the train velocity increased from 80 km/h to 100 km/h. Moreover, as the train velocity increased, the duration of concentration from the rise to
Fig. 14. Vertical distribution of smoke concentration.
the maximum smoke concentration at T7 was greater. Based on the analysis above, the differences in airflow velocities at different heights could explain this behavior. On the contrary, the measuring point T5 achieved the lowest maximum airflow velocity and maximum smoke concentration, as well as the shortest duration of the concentration from rise to fall. 3.3. Influence of train velocity on smoke movement characteristics Fig. 15 displayed the airflow velocity at measuring point T6 for the 1/10 scale model train with velocities of 60 km/h, 80 km/h, 100 km/h and 120 km/h. To facilitate the comparison between different train
Fig. 15. Airflow velocity at different train velocities.
Fig. 16. Smoke concentration at different train velocities. 8
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Table 3 Maximum smoke concentration at different train velocities. Vtrain
T3 (C/Cref)max
T5 (C/Cref)max
T6 (C/Cref)max
T7 (C/Cref)max
T9 (C/Cref)max
60 km/h 80 km/h 100 km/h 120 km/h
1.019 0.833 0.574 0.256
0.344 0.241 0.176 0.102
0.995 0.840 0.563 0.263
1.073 0.897 0.612 0.346
1.007 0.831 0.568 0.261
fall was shorter, which was consistent with the variation law of smoke concentration found by Xi et al. (2016). When the train velocity was lower, it weakened the dilution effect of fresh air on smoke. In addition, the incline angle α between the smoke plume and the ground was larger, resulting in more smoke reaching the ceiling, as shown in Fig. 17. For the four different train velocities, the incline angles α were 31°, 28°, 23°and 19°, respectively. This was because the larger train velocity generated larger airflow velocity in the x-axis direction, but in the z direction, the increase in airflow velocity was relatively small. Thus, α decreased with an increasing velocity. It also could be found
Fig. 18. Airflow velocity under different blockage ratios.
that when the train velocity was 60 km/h, the concentrations around and behind the train were much larger than that at 120 km/h.
Fig. 17. Concentration filed around the train at different train velocities. (a) 60 km/h. (b) 80 km/h. (c) 100 km/h. (d) 120 km/h. 9
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Moreover, the distance from the smoke front to the train tail decreased from 2.29 m to 1.40 m. Therefore, we concluded that when the on-fire train operated at a smaller velocity, the smoke concentration in the tunnel was higher. From the peak values of smoke concentration at measuring points T3, T6 and T9 presented in Table 3, we could observe that the maximum smoke concentrations under a certain train velocity were nearly the same, which further indicated that smoke movement in a tunnel was mainly affected by piston wind.
3.4. Influence of blockage ratio on smoke movement characteristics Fig. 18 showed the airflow velocities at measuring point T6 when the 1/10 and 1/15 scale model trains ran through the tunnel at a velocity of 60 km/h. The blockage ratios were 0.43 and 0.19, respectively. The figure illustrated that the blockage ratio had a significant impact on the airflow velocity. In zone 1, when the blockage ratio was higher, the pressure gradient between the front of the train head and the tunnel exit was greater, and hence the airflow velocity was higher. In zone 2, similarly, the annular space between the train and the tunnel was smaller, and thus the velocity of the reverse flow in the annular space enlarged. In zone 3, the larger the blockage ratio was, the greater the wake effect was. As expected a large blockage ratio could decelerate the spread of smoke, although the smoke behind the train tail always moved downstream. As a comparison, when a buring train decelerated and then stopped in the tunnel, the smoke would flow back to upstream. Besides, although a larger blockage ratio generated a greater piston wind velocity, the backflow moment first increased and then decreased with increasing blockage ratio. This was for the reason that after the train stopped for a period, the piston wind caused by the movement of the train disappeared, and the buoyancy was the only factor influencing the smoke flow. The larger the blockage ratio was, the greater the buoyancy was. Once the blockage ratio was larger than the certain value, the effect of buoyancy was greater than the effect of the residual piston wind. Table 4 presented the maximum airflow velocities at T1, T5, T6, T7 and T11. When the blockage ratio changed from 0.19 to 0.43, the increments in maximum airflow velocities at T1 and T11 were smaller than that at T6. In addition, the increase at T5 was smaller than those at T6 and T7, indicating that the changes in maximum airflow velocity at different heights in the tunnel differed with increasing blockage ratio. This discrepancies were caused by the different distances from the measuring points to the train surface. The point T5, located at the top of the tunnel, was the farthest from the train, whereas T7 was the closest to the train. Fig. 19 provided the smoke concentration at measuring point T6 when the different scaled trains ran through the tunnel. With a smaller blockage ratio, the maximum smoke concentration in the tunnel was larger, and the duration of the concentration from rising to declining was longer. Moreover, as shown in Fig. 20, the inclination angle α between the smoke plume and the ground decreased with increasing blockage ratio. Similarly, the distance between the smoke front and the train tail also decreased with increasing blockage ratio. This suggested that under a certain power of fire source, the main influencing factor of smoke concentration in the tunnel was the airflow velocity. A smaller blockage ratio corresponded to a smaller airflow velocity, leading to a weaker dilution effect. Also, the difference between the smoke movement velocity in the x direction and z direction was smaller. Thus, the
Fig. 19. Smoke concentration under different blockage ratios.
smoke concentration in the tunnel increased with a decreasing blockage ratio. However, when an on-fire train stopped in a tunnel, there was no piston wind in the tunnel and moreover the blockage reduced the area of cross-section, which resulted in the flow of fresh air would be hampered. Thus, the dilution effect of fresh air on smoke would be reduced (Alva et al., 2017). Moreover, incomplete combustion due to restricted ventilation may produce more smoke (Decimus et al., 2019) causing the smoke concentration increasing with an increasing blockage ratio. Obviously, this features was contrary to the behavior of moving fire sources in our study. Table 5 tabulated the maximum smoke concentrations at these five measuring points. The peak values of smoke concentrations at T3, T6, and T9 were almost identical at a certain blockage ratio. In addition, the distribution law of the maximum smoke concentration along the height direction did not vary with the blockage ratio, and the smoke concentration at T5 was the lowest. 4. Conclusions In this study, moving model tests of an on-fire subway train running through a tunnel were carried out to investigate the smoke movement characteristics of moving fire sources. Numerical simulations were supplemented for a validation and further analysis. The discrepancies between moving fire sources and stationary fire sources were analyzed. Finally, the impacts of train velocity and blockage ratio on smoke movement characteristics were also compared and discussed. Major conclusions were as follows. (1) The smoke movement characteristics of moving fire sources were significantly different from those of the stationary fire sources. For the moving fire sources, the piston wind was the dominant force for driving smoke movement. The smoke first spread to the train tail along the bottom of the train, and then moved upward due to the buoyancy, resulting in an increase in the concentration. After that, it was diluted by the piston wind and spread to the downstream, and the concentration decreased as well. (2) Near the middle of the tunnel, the airflow velocity and smoke concentration at different longitudinal positions were basically consistent. By contrast, they were different at varying heights even at the same cross section. Both of them decreased with increasing height, contrary to the stationary fire sources. Compared to measuring point T7 (0.17 m away from the rail surface), the airflow velocity and smoke concentration at T5 (0.49 m away from the rail surface) decreased by 24.1% and 67.9%, respectively. (3) Train velocity and blockage ratio made a significant influence on the smoke movement characteristics. When the velocity increased from 60 km/h to 120 km/h, the smoke concentration at T6 (center
Table 4 Maximum airflow velocity under different blockage ratios. β
T1 (u/V)max
T5 (u/V)max
T6 (u/V)max
T7 (u/V)max
T11 (u/V)max
0.43 0.19
0.403 0.277
0.466 0.367
0.561 0.415
0.614 0.458
0.491 0.383
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Fig. 20. Concentration filed around the train under different blockage ratios. (a) 0.43. (b) 0.19. wind in a tunnel fire. J. Wind Eng. Ind. Aerodyn. 158, 61–68. Giachetti, B., Couton, D., Plourde, F., 2017. Smoke spreading analyses in a subway fire scale model. Tunn. Undergr. Space Technol. 70, 233–239. Hu, L.H., Fong, N.K., Yang, L.Z., Chow, W.K., Li, Y.Z., Huo, R., 2007. Modeling fireinduced smoke spread and carbon monoxide transportation in a long channel: fire dynamics simulator comparisons with measured data. J. Hazard. Mater. 140, 293–298. Hu, L.H., Tang, F., Yang, D., Liu, S., Huo, R., 2010. Longitudinal distributions of CO concentration and difference with temperature field in a tunnel fire smoke flow. Int. J. Heat Mass Transf. 53, 2844–2855. Ingason, H., Li, Y.Z., 2010. Model scale tunnel fire tests with longitudinal ventilation. Fire Saf. J. 45, 371–384. Jiang, X., Zhang, H., Jing, A., 2018a. Effect of blockage ratio on critical velocity in tunnel model fire tests. Tunn. Undergr. Space Technol. 82, 584–591. Jiang, X., Liu, M., Wang, J., Li, Y., 2018b. Study on induced airflow velocity of point smoke extraction in road tunnel fires. Tunn. Undergr. Space Technol. 71, 637–643. Lee, S.R., Ryou, H.S., 2016. An experimental study of the effect of the aspect ratio on the critical velocity in longitudinal ventilation tunnel fires. J. Fire Sci. 23, 119–138. Li, Y.Z., Lei, B., Ingason, H., 2010. Study of critical velocity and backlayering length in longitudinally ventilated tunnel fires. Fire Saf. J. 45, 361–370. Li, Y.Z., Lei, B., Ingason, H., 2011. The maximum temperature of buoyancy-driven smoke flow beneath the ceiling in tunnel fires. Fire Saf. J. 46, 204–210. Meng, N., Liu, B., Li, X., Jin, X., Huang, Y., Wang, Q., 2018. Effect of blockage-induced near wake flow on fire properties in a longitudinally ventilated tunnel. Int. J. Therm. Sci. 134, 1–12. Meng, S., Zhou, D., Wang, Z., 2019. Moving model analysis on the transient pressure and slipstream caused by a metro train passing through a tunnel. PLoS One 14 (9), e0222151. Michaux, G., Vauquelin, O., 2009. Density effect on the mixing and the flow pattern of an impinging air–helium jet. Exp. Therm Fluid Sci. 33, 976–982. Niu, J.Q., Zhou, D., Liang, X.F., Liu, Scarlett, Liu, T.H., 2018. Numerical simulation of the Reynolds number effect on the aerodynamic pressure in tunnels. J. Wind Eng. Ind. Aerodyn. 173, 187–198. Tang, F., Hu, L.H., Yang, L.Z., Qiu, Z.W., Zhang, X.C., 2014. Longitudinal distributions of CO concentration and temperature in buoyant tunnel fire smoke flow in a reduced pressure atmosphere with lower air entrainment at high altitude. Int. J. Heat Mass Transf. 75, 130–134. Tang, F., Li, L.J., Mei, F.Z., Dong, M.S., 2016. Thermal smoke back-layering flow length with ceiling extraction at upstream side of fire source in a longitudinal ventilated tunnel. Appl. Therm. Eng. 106, 125–130. Tang, F., Mei, F.Z., Wang, Q., He, Z., Fan, C.G., Tao, C.F., 2017b. Maximum temperature beneath the ceiling in tunnel fires with combination of ceiling mechanical smoke extraction and longitudinal ventilation. Tunn. Undergr. Space Technol. 68, 231–237. Tang, F., He, Q., Shi, Q., 2017a. Experimental study on thermal smoke layer thickness with various upstream blockage–fire distances in a longitudinal ventilated tunnel. J. Wind Eng. Ind. Aerodyn. 170, 141–148. Vauquelin, O., 2008. Experimental simulations of fire-induced smoke control in tunnels using an “air–helium reduced scale model”: Principle, limitations, results and future. Tunn. Undergr. Space Technol. 23, 171–178. Vauquelin, O., Megret, O., 2002. Smoke extraction experiments in case of fire in a tunnel. Fire Saf. J. 37, 525–533.
Table 5 Maximum smoke concentration under different blockage ratios. β
T3 (C/Cref)max
T5 (C/Cref)max
T6 (C/Cref)max
T7 (C/Cref)max
T9 (C/Cref)max
0.43 0.19
1.019 1.147
0.344 0.397
0.995 1.168
1.073 1.212
1.007 1.138
of the tunnel) decreased by 73.6%. Moreover, the maximum airflow velocity in the tunnel was approximately linear with train velocity. A higher blockage ratio resulted in a greater airflow velocity in the tunnel. As the blockage ratio grew from 0.19 to 0.43, the airflow velocity at T6 increased by 35.2%, and accordingly the concentration decreased by 14.8%. Declaration of Competing Interest The authors declared that they have no conflicts of interest to this work. Acknowledgements This research work is supported by grants from National Key R&D Program of China (No. 2016YFB1200601-B14) and the National Natural Science Foundation of China (No. 11902367). All authors are grateful to the anonymous reviewers for the constructive comments. References Alva, W.U.R., Jommas, G., Dederichs, A.S., 2017. The influence of vehicular obstacles on longitudinal ventilation in tunnel fires. Fire Saf. J. 87, 25–36. Chow, W.K., Gao, Y., Zhao, J.H., Dang, J.F., Chow, N.C.L., 2016. A study on titled tunnel fire under natural ventilation. Fire Saf. J. 81, 44–57. Clanche, J.L., Salizzoni, P., Creyssels, M., Mehaddi, R., Candelier, F., Vauquelin, O., 2014. Aerodynamics of buoyant releases within a longitudinally ventilated tunnel. Exp. Therm Fluid Sci. 57, 121–127. Decimus, A., Sonnier, R., Zavaleta, P., Suard, T., Ferry, L., 2019. Study of gases released under incomplete combustion using PCFC-FTIR. J. Therm. Anal. Calorim. 138, 753–763. Eftekharian, E., He, T.P., Kwok, K.C.S., Ong, R.H., Yuan, J.P., 2019. Investigation of fire –driven cross-wind velocity enhancement. Int. J. Therm. Sci. 141, 84–95. Gannouni, S., Ben Maad, R., 2016. Numerical analysis of smoke dispersion against the
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Sci. 140, 491–504. Yi, L., Xu, Q., Xu, Z., Wu, D., 2014. An experimental study on critical velocity in sloping tunnel with longitudinal ventilation under fire. Tunn. Undergr. Space Technol. 43, 198–203. Zhang, S., Cheng, X., Yao, Y., Zhu, K., Li, K., Lu, S., Zhang, R., Zhang, H., 2016. An experimental investigation on blockage effect of subway train on the smoke backlayering in subway tunnel fires. Appl. Therm. Eng. 99, 214–223. Zhang, N., Lu, Z.J., Zhou, D., 2018. Influence of train speed and blockage ratio on the smoke characteristics in a subway tunnel. Tunn. Undergr. Space Technol. 74, 33–40. Zhang, L., Yang, M.Z., Liang, X.F., Zhang, J., 2017. Oblique tunnel portal effects on train and tunnel aerodynamics based on moving model tests. J. Wind Eng. Ind. Aerodyn. 167, 128–139. Zhao, S., Liu, F., Wang, F., Weng, M., Zeng, Z., 2018. A numerical study on smoke movement in a subway tunnel with a non-axisymmetric cross-section. Tunn. Undergr. Space Technol. 73, 187–202. Zhong, W., Tu, R., Yang, J., Liang, T., 2015. A study of the fire smoke propagation in subway station under the effect of piston wind. J. Civ. Eng. Manage. 21, 514–523. Zhou, T., Liu, J., Chen, Q., Chen, X., Wang, J., 2017. Characteristics of smoke movement with forced ventilation by movable fan in a tunnel fire. Tunn. Undergr. Space Technol. 64, 95–102. Zhou, D., Tian, H.-Q., Zheng, J.-L., Yan, X., 2015. Smoke movement in a tunnel of a running subway train on fire. J. Central South Univ. 22, 208–213.
Wang, K.-H., Chen, J.-M., Wang, Z.-K., Gao, D.-L., Wang, G.-Y., Lin, P., 2019. An experimental study on self-extinction of tunnel fire under natural ventilation condition. Tunn. Undergr. Space Technol. 84, 177–188. Wang, Y.F., Sun, X.F., Liu, S., Yan, P.N., Qin, T., Zhang, B., 2016. Simulation of backlayering length in tunnel fire with vertical shafts. Appl. Therm. Eng. 109, 344–350. Wang, F., Wang, M., Carvel, R., Wang, Y., 2017. Numerical study on fire smoke movement and control in curved road tunnels. Tunn. Undergr. Space Technol. 67, 1–7. Weng, M.-C., Lu, X.-L., Liu, F., Shi, X.-P., Yu, L.-X., 2015. Prediction of backlayering length and critical velocity in subway tunnel fires. Tunn. Undergr. Space Technol. 47, 64–72. Wu, F., Zhou, R., Shen, G., Jiang, J., Li, K., 2018. Effects of ambient pressure on smoke back-layering in subway tunnel fires. Tunn. Undergr. Space Technol. 79, 134–142. Xi, Y.H., Liu, C., Mao, J., 2015. Numerical simulation of velocity field on moving subway train fire. J. Beijing Jiaotong Univ. 39, 59–66. Xi, Y.H., Mao, J., Bai, G., Hu, J.W., 2016. Safe velocity of on-fire train running in the tunnel. Tunn. Undergr. Space Technol. 60, 210–223. Xie, X.Y., Ding, L.P., Li, Y.S., 2010. CFD simulation on evacuation of a high-speed train continue-to-roll during long tunnel Fires. J. Tongji Univ. (Natl. Sci.) 38 1746–1752+1791. Yan, Z.-G., Guo, Q.-H., Zhu, H.-H., 2017. Full-scale experiments on fire characteristics of road tunnel at high altitude. Tunn. Undergr. Space Technol. 66, 134–146. Yao, Y.Z., Li, Y.Z., Ingason, H., Cheng, X.D., 2019. Numerical study on overall smoke control using naturally ventilated shafts during fires in a road tunnel. Int. J. Therm.
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Tunnelling and Underground Space Technology journal homepage: www.elsevier.com/locate/tust
Moving model test of the smoke movement characteristics of an on-fire subway train running through a tunnel Zhe Wanga,b,c, Dan Zhoua,b,c, Sinisa Krajnovicd, Hongkang Liua,b,c,
T
⁎
a
Key Laboratory of Traffic Safety on Track (Central South University), Ministry of Education, Changsha 410075, Hunan, China Joint International Research Laboratory of Key Technology for Rail Traffic Safety, Changsha 410075, Hunan, China c National & Local Joint Engineering Research Center of Safety Technology for Rail Vehicle, Changsha 410075, Hunan, China d Division of Fluid Dynamics, Department of Applied Mechanics, Chalmers University of Technology, 41296 Gothenburg, Sweden b
A R T I C LE I N FO
A B S T R A C T
Keywords: Moving fire source Moving model test Piston effect Smoke movement Subway tunnel
A moving model test was carried out to investigate the associated smoke movement characteristics when a subway train on fire runs in a tunnel. Train models of the 1/10 and 1/15 scales were used. The spatial distributions of airflow velocity and smoke concentration were then analyzed, and the differences between moving fire sources and stationary fire sources were discussed. The results show that the smoke movement characteristics of a stationary fire source were greatly different from those of a moving one. Specifically, the smoke movement for the moving fire source was dominated by piston wind. Moreover, the process of the smoke spread could be divided into three stages, during which time the flow direction changed. The peak smoke concentration value occurred after the train tail passed by the measuring point. Besides, the impacts of train speed (60 km/h, 80 km/h, 100 km/h, and 120 km/h) and blockage ratio (0.19 and 0.43) on airflow velocity and smoke concentration were also investigated. With increasing train velocity, the airflow velocity increased, and the smoke concentration decreased. The maximum airflow velocity was approximately linear with the train velocity. Furthermore, the increasing blockage ratio enhanced the piston effect in the tunnel, thus increasing the airflow velocity and reducing the smoke concentration.
1. Introduction As a major mode of transportation in modern society, subways are being built in an increasing number of cities due to its convenience and efficiency. However, as subway traffic continues to boom, the operational security of subway systems is becoming increasingly critical and being paid remarkable attention. Fires are one of the most significant threats to the secure operation of subway systems. Because of the relatively closed internal space of the subway tunnel, it is difficult to discharge the high-temperature toxic smoke generated in the event of a fire, which not only damages the tunnel structures but also prevents the escape safety of subway train passengers. Generally, the source of subway tunnel fires can be divided into two types: stationary fires, which have a stationary source, and moving fires with a moving source. Stationary fires are mostly caused by the aging and failure of internal facilities in the tunnel, whereas moving fires are mostly caused by the failure of electrical equipment during the operation of a subway train. When an operating train is on fire, the intense piston wind generated in the tunnel has a significant impact on the
⁎
smoke movement, which makes the smoke movement characteristics in the tunnel more complicated than those with stationary fire sources. In recent years, lots of scholars have conducted extensive research on stationary fires in tunnels. Their work mainly focus on four angles: (1) the distribution of temperature along the longitudinal and vertical directions of the tunnel (Zhao et al., 2018; Li et al., 2011; Ingason and Li, 2010; Tang et al., 2017; Meng et al., 2018; Tang et al., 2017; Yan et al., 2017); (2) the distribution of smoke along the longitudinal and vertical directions of the tunnel (Giachetti et al., 2017; Zhou et al., 2017; Wang et al., 2019; Hu et al., 2010; Tang et al., 2014; Hu et al., 2007); (3) the smoke back-layering length and its dimensionless expression, especially considering the blockage effect caused by a subway train inside a tunnel (Zhang et al., 2016; Weng et al., 2015; Tang et al., 2016; Wang et al., 2016; Gannouni and Ben Maad, 2016; Wu et al., 2018); and (4) the critical velocity and its dimensionless expression (Jiang et al., 2018a; Lee and Ryou, 2016; Li et al., 2010; Wang et al., 2017; Yi et al., 2014). The results shows that when the fire source is stationary, the longitudinal smoke flow velocity and the concentration distribution in the tunnel are symmetric about the fire source under
Corresponding author. E-mail address: [email protected] (H. Liu).
https://doi.org/10.1016/j.tust.2019.103211 Received 25 April 2019; Received in revised form 27 October 2019; Accepted 19 November 2019 0886-7798/ © 2019 Elsevier Ltd. All rights reserved.
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Fig. 1. Schematic of moving model experimental device.
mostly been studied through numerical simulations. Moreover, the simulations are generally set to slow down and stop when the train catches fire, resulting in a lack of knowledge on the scenario when an onfire train runs in the tunnel at a constant velocity. In tunnel fire model tests, the model trains are also greatly simplified, and the movement of the train is mostly simulated by relative motion. Although this method can reflect the effect of piston wind generated by an operating train, the relative movement between a fire source and tunnel cannot be achieved since the location of the fire source does not change in reality. Therefore, the simulations are quite different from the actual operation case of an on-fire train. In this study, two model trains (with scales of 1/10 and 1/15) were adopted, and the smoke movement characteristics when an on-fire train ran through a tunnel were acquired through pressure sensors and concentration sensors. Additionally, numerical simulations were used to further illustrate the smoke movement characteristics. Then, the longitudinal and vertical distribution of airflow velocity and smoke concentration was investigated. Through a detailed comparison between the moving model test and numerical simulations, the differences in smoke movement characteristics between moving fire sources and stationary fire sources were compared and discussed. In addition, the effects of train velocity and the blockage ratio on the airflow velocity and smoke concentration were determined quantitatively.
natural ventilation; both of them decrease with the increasing distance to the fire source. In addition, the smoke mainly concentrates underneath the ceiling; below this smoke layer, a fresh air layer is maintained, and it is driven toward the heat source. Thus, smoke concentration tends to increase with increasing height. Moreover, when longitudinal ventilation is operating in a tunnel, the diffusion of smoke is hindered against the direction of ventilation. When the ventilation velocity is larger than the critical velocity, the back-layering length drops to zero; in other words, all the smoke spread to one side of the fire source. Thus, on the other side of the fire source, the smoke concentration is very low. When the ventilation velocity is too high, the stratification of smoke is destroyed due to the strengthening of shear stress. However, there have been relatively few studies on smoke movement in the case of a running subway train on fire. Based on the computational fluid dynamics (CFD) method, Xie et al. (2010) simulated the train movement by using equivalent winds induced by a train and then investigated the track of smoke when an on-fire train ran through the tunnel. Their study showed that the piston wind dominated the smoke movement, and all the smoke moved downstream instead of upstream. Zhou et al. (2015) adopted a dynamic mesh technique to study the law of smoke diffusion when the bottom of a subway train was on fire and the train was forced to stop in the tunnel. During the early stage of deceleration, the smoke spread to the train tail since the train velocity was greater than the piston wind velocity. As the train velocity further decreased, the front of the train was gradually covered with smoke because the residual piston wind velocity was larger than the train velocity. Furthermore, Zhang et al. (2018) established a threedimensional full-scale calculation model of the subway tunnel to research the smoke spread velocity around a fire source when a train running at a constant velocity caught on fire and then slowed down to stop in the middle of the tunnel. During the early stage of stopping, the residual piston wind caused the smoke around the fire source to move downstream. When the piston wind completely disappeared, however, the smoke moved upstream. The greater the residual piston wind in the tunnel was, the later the flow direction changed. Similarly, Xi et al. (2015) used sliding mesh technology to study the variation in the velocity field in the tunnel when an on-fire train ran at different velocities. They pointed out that the direction of airflow in the annular space between the train and the tunnel was opposite to the velocity of train, and a higher train velocity generated a higher airflow velocity. Based on the principle of relative motion, Xi et al. (2016) also investigated the relationship between the heat release rate and train velocity by using ventilation to simulate the piston wind. In summary, the piston wind generated in a tunnel has been found to be the main influencing factor in the smoke movement when an onfire train runs through a tunnel. However, because of the unsteady flow field in the tunnel, the smoke movement driven by the piston wind is more complicated. Due to the limitation of real vehicle tests, the smoke movement characteristics during the operation of an on-fire train have
2. Experimental apparatus The experiment was carried out using a moving model device from the Key Laboratory of Track Traffic Safety's moving model device at Central South University, China. The moving model test can simulate the train’s movement and the unsteady flow around the train when the train runs through the tunnel. Thus, it can be used to study the impact of the piston wind on the smoke movement characteristics of an on-fire train running through a tunnel. 2.1. Moving model rig design and operation Fig. 1 shows a schematic of the moving model device (Zhang et al., 2017; Meng et al., 2019). The device consists of two layers: an upper layer, which is the track that the model train runs along, and the bottom layer, which is the track that the power transfer car runs along. The train velocity is determined by the traction of the power transfer car. The test track is 164 m long and can be divided into three sections according to the running state of the train: the accelerating section, the test section (the uniform-speed section), and the braking section. The accelerating section and the braking section are 57 m long, and the test section is 50 m long. The basic principle of the moving model test was to pull the driven car and the model train connected to it backward by using a line extremity winch. The power control system controlled the tension on the driven car. At the same time, the power transfer car at the bottom layer 2
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also moved backward, such that the elastic rope was gradually tensioned. When the tension reached a specified value, the control system maintained constant tension, and the model train was in the to-belaunched state. Afterward, the solenoid valve was controlled by the decoupling device of the driven car. The release of the decoupling device and the elastic rope rebound drove the forward movement of the power transfer car and the model train. When the model train reached the entrance of the test section, it automatically separated from the power transfer car. Then, the model train was launched and ran at a high speed along the track through inertia. To ensure that the velocity of the train entering and leaving the test section met the test requirements, photoelectric sensors were installed at the entrance and exit of the tunnel. Moreover, a high potential was triggered as the train passed the sensor. The train velocity could be accurately calculated by the duration of the high potential. When the train ran from the test section to the braking section, the braking system rapidly decelerated the model train. To ensure smooth deceleration, the braking system adopted a combination of various mechanical braking methods, including a friction brake, a piston brake, and a brake disc.
Fig. 3. Transparent tunnel model.
transparent Plexiglas, as presented in Fig. 3. For convenience of description, the direction of the running train was defined as the x axis, with the origin specified at the entrance of the tunnel; the y axis was defined as the center line of the track outward, with the origin specified at the center of the track; and the z axis was vertically upward, originating at the rail surface. According to researches by Gannouni and Ben Maad (2016), smallscale experimental models can be divided into two types: cold models in which the heat source is represented by an injection at ambient temperature of a fluid with a different density from that of the surrounding fluid, and hot models in which the fire is represented by a heat source such as a gas burner or a thermal resistor. The cold models have been widely used in previous studies (Clanche et al., 2014; Michaux and Vauquelin, 2009; Vauquelin, 2008; Vauquelin and Megret, 2002). In this study, in order to ensure the safety of the test, the cold model was also selected to simulate smoke generated by an on-fire train. Specifically, a smoke generator was installed inside the train head. It generated smoke steadily and continuously, and the time of duration was larger than 2 min, which satisfied the requirement of smoke generation. Moreover, the smoke particles produced by the smoke generator were extremely small, and the density of the smoke was about 0.7 kg/m3 (lower than that of air), so that it could diffuse into the air quickly and spread upward. Based on the above all, the current model could be feasible to simulate the smoke movement driven by the piston wind.
2.2. Experimental model and smoke generation device In this test, the model train consisted of three coaches: a head car, a middle car, and a tail car. To simulate the piston effect when a real subway train runs through a tunnel, complex parts including an equipment cabin and bogies were added to the model train, as shown in Fig. 2. The model train was made by 3D printing. To study the impact of the blockage ratio on the smoke movement, we used two model trains with different scales. The research by Niu et al. (2018) indicated that the scale of train model had a significant effect on the flow field around the train. With an increase in model scale, the flow field around the train becomes closer to the result of the full-scale model. Therefore, two relatively large-scale train models, including 1/10 and 1/15, were selected to validate our methodology in this study. The length, width, and height of the 1/10 scale train were Ltr = 7.09 m, W = 0.313 m, and H = 0.367 m, respectively, and the cross-sectional area of the train was 0.095 m2. For the 1/15 scale train, the length, width, and height were Ltr = 4.73 m, W = 0.209 m, and H = 0.245 m, respectively, and the cross-sectional area of the train was 0.042 m2. Generally, the velocity range of the subway trains is 60–80 km/h. However, with the increasing passenger flow, the velocities of the subway trains in some cities have gradually increased to 120 km/h to shorten the running time, such as in Guangzhou and Shenzhen. To obtain a more thorough understanding, therefore, the test velocities selected in this study were within 60–120 km/h. The model tunnel used in the test was a single-track one with a cross-sectional area of 0.22 m2, and its length was 35.4 m. For the two different scaled trains, the blockage ratios were 0.43 and 0.19, respectively. To facilitate the observation and recording of smoke movement in the tunnel, part of the model tunnel was made of
2.3. Measurement setup and test system The arrangement of the measuring points for airflow velocity and smoke concentration is shown in Fig. 4. There were 11 airflow velocity measuring points, located at the T1 to T11 positions, and seven smoke concentration measuring points were located at T3 to T9 positions. This arrangement facilitated the analysis on the changes of airflow velocity and smoke concentration along the longitudinal and vertical directions of the tunnel. The heights of the measuring points T5, T6, and T7 were 0.49 m, 0.32 m, and 0.17 m, respectively. Since the center line of the tunnel overlapped with the center line of the track, the measuring points were only placed on the right side of the forward direction of the train. The test system included a data acquisition device and a data storage device. Five-hole probes were installed on the tunnel surface, and the airflow velocity was measured by the probes with the help of pressure sensors (with an accuracy of ± 0.04%); the smoke concentration was measured by concentration sensors (with an accuracy of ± 0.5%). The electrical signals collected by the data acquisition device were then transmitted to the data storage device through an optical cable for subsequent analysis. 2.4. Numerical simulation
Fig. 2. Subway train and tunnel mode.
To further explain the smoke movement characteristics when an on3
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Fig. 4. Arrangement of measuring points on the tunnel surface (mm). (a) Front view. (b) Left view.
Fig. 5. Computational domain and mesh.
Fig. 6. Repeatability test results and numerical calculation results validation. (a) Airflow velocity. (b) Smoke concentration.
Fig. 5. To achieve a well-developed flow field and avoid the impact of boundary conditions, the train was placed at a distance of 13 H from the tunnel entrance and 47 H from the back face of the computational domain. The no-slip wall conditions were applied into the top, side, and ground faces of the computational domain.
fire train runs through a tunnel, a numerical model corresponding to the moving model test was established by using Ansys 18.1. The 1/10 and 1/15 model train were selected, as well as the tunnel model used in the moving model test. The fire source was located at the bottom of the head car, and the velocity was set as 60 km/h. The fire-smoke governing equations adopted in the numerical simulation were based on the combination of three-dimensional compressible unsteady NavierStokes equations and the RNG k-ε turbulence model. The schematics of the computation domain, boundary conditions, and mesh was shown in
3. Results and discussion For convenience of analysis and comparison, the airflow velocity 4
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1/10 scale model train ran through the tunnel at a velocity of 60 km/h. In the figure, t = 0 corresponded to the moment of time interval of 1 s before the train head reached the tunnel entrance. Similarly, t = HP and t = TP denoted the moments when the head and tail of the train passed the measuring point, respectively. As shown in Fig. 7, when the train entered the tunnel, the airflow velocity in front of the train head began to increase. After the train had fully entered the tunnel, the airflow velocity fluctuated at around 0.38, corresponding to zone 1 in the figure. Once the train head reached the measuring point, both the airflow direction and the value changed suddenly, and the airflow velocity remained negative before the train tail reached, as shown in zone 2. Then, as the train tail passed by T6, a large negative pressuredominated zone, namely the wake region, occurred behind the train tail. Under the vacuum suction of the wake region, the surrounding air rapidly flowed in, reversing the airflow direction again. At the moment, the flow velocity reached its peak of around 0.57, referring to zone 3 in the figure. As the train left T6, the wake effect at the measuring point weakened, which caused the airflow velocity to decrease gradually. According to the analysis above, obviously the smoke movement could be divided into three stages when an on-fire train runs in the tunnel. The smoke generated at the bottom of the head car firstly spread to the train tail. After that, it moved upward due to the buoyancy. More importantly, it soon spread downstream, driven by the piston wind behind the train tail. It was noted that the flow state of smoke under a moving fire source apparently differed from that under a stationary fire source. For the latter, the smoke on both sides of the fire moved towards the openings, and the direction of airflow would not change (Chow et al., 2016). Due to the diverse flow states, the forces of smoke were different accordingly. Generally, the driving forces of smoke mainly consisted of buoyancy and inertia, and the larger airflow velocity generated greater inertia effects (Zhong et al., 2015; Eftekharian et al., 2019; Yao et al., 2019). To clearly distinguish their effects, Jiang et al. (2018b) suggested that the dominant contributing force to smoke movement could be derived from the following equation:
Table 1 Repeatability test results of maximum airflow velocity and smoke concentration. Variables
(u/V)max (C/Cref)max Diff./Ref.(%)
Airflow velocity
Smoke concentration
Test 1
Test 2
Test 3
NS
Test 1
Test 2
Test 3
NS
0.561 /
0.573 / 2.1
0.568 / 1.2
0.570 / 1.6
/ 0.995 /
/ 0.983 −1.2
/ 1.008 1.3
/ 0.989 −0.6
and the smoke concentration were dimensionless, represented by the velocity ratio u/ V and the concentration ratio C / Cref respectively. In which, u denotes the airflow velocity in the x direction, and V represents the train velocity. C is the smoke concentration at a certain measuring point, and Cref is the mean of the maximum smoke concentration measured at the measuring point T6 when the 1/10 scale model train ran through the tunnel at a velocity of 60 km/h in the repeatability test. 3.1. Repeatability test and numerical calculation results validation To ensure the accuracy of the results, 10 repeatability tests were carried out for each case. Fig. 6 shows the repeatability verification of three test data sets collected using the 1/10 scale model train, compared with numerical predictions. The train velocity in the test was 60 km/h. Specifically, Fig. 6a displays the airflow velocity curves of T6 in the tunnel, and Fig. 6b presents the smoke concentration curves at this measuring point. From the figures, the trends of the four curves were generally consistent. Those few differences were inevitable since it was impossible to completely reshape the airflow in the moving model test. Table 1 tabulated the three results for the maximum airflow velocity and smoke concentration of the 10 repeatability tests, and the results of the numerical simulations were also listed (NS represents the numerical simulation). The standard deviations of airflow velocity and concentration were 0.004 and 0.011, respectively. The results of airflow velocity and smoke concentration were within 95% confidence intervals. As the table showed, the differences for both the maximum airflow velocity and the maximum smoke concentration were within 3%. Moreover, the airflow velocity and smoke concentration curves of other measuring points also presented good repeatability.
Fr =
v2 , gH
(1)
where Fr was the Froude number, v was the airflow velocity, g denoted the gravitational acceleration of 9.8 m/s2, and H stood for height of the tunnel. If Fr > 1, the inertia force mainly dominated the smoke flow; otherwise, the buoyancy force made a dominating effect. As shown in Fig. 7, when the vehicle velocity was 60 km/h, the maximum piston wind velocity could reach about 9.5 m/s. By substituting v in Eq. (1), we can see that, Fr is larger than 1 when an on-fire train running in the tunnel. In other words, the piston wind dominated the movement of smoke in our study. Fig. 8 showed the smoke concentration variation at measuring point T6. At 2 s, the train head reached T6, and the train tail left T6 at 2.48 s. It could be seen that the smoke concentration at T6 did not increase until the train tail passed by the measuring point. This was because the smoke generated by the train head was confined to the space underneath the bottom of the train and spread to the train tail. Therefore, the concentration did not increase when the train head reached T6, although the on-fire position was located at the train head. At the moment the train tail passed T6, some of the smoke behind the train tail gathered at the measuring point, and thus the smoke concentration rose rapidly to its peak. Afterward, the smoke gathering near the measuring point was continuously diluted by the piston wind and then moved to the downstream, which reduced the smoke concentration to the level that it was at before the on-fire train passed by. As a comparison, when the fire source is stationary, the smoke concentration in the tunnel would accumulate continuously and remain at a high level after reaching a stable state (Zhao et al., 2018). Fig. 9 shows the concentration field around an on-fire train at
3.2. Smoke movement characteristics with on-fire train running through the tunnel Fig. 7 showed the airflow velocity at measuring point T6 when the
Fig. 7. Airflow velocity variation with an on-fire train running through the tunnel. 5
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zone 1. In zone 2, it could be found that the amplitude of the airflow velocity at T1 was greater than those at the other measuring points. This phenomenon could be interpreted by the fact that when the train head reached T1, the airflow velocity in front of the train head was still low, and the volume of air discharged from the tunnel exit was small. As a consequence, a larger amount of air flowed back to the tunnel entrance through the annular space between the train and the tunnel in the same duration. In zone 3, the maximum airflow velocities at T1 and T11 were smaller than that at T6. This was because T1 and T11 were more closer to the tunnel entrance and exit. It also could be seen that the maximum velocity ratios at the other points were nearly the same, except for those near the tunnel entrance and exit. By contrast, when a stationary train caught fire in a tunnel, the peak value of smoke velocity occurred near the fire source. On both sides of the fire source, the spread directions of smoke were opposite, and the velocity decreased with an increase in the distance from the fire source (Chow et al., 2016). Fig. 12 displayed the airflow velocity distributions at different heights at the same cross section of the tunnel. Measuring point T5 was located at the top of the tunnel, 0.49 m away from the rail surface; T6 was 0.32 m away from the rail surface; and T7 was 0.17 m away from the rail surface. From the figure, the nearly consistent peak values of the airflow velocity could be observed at different heights in zone 1 and zone 2. Furthermore, the impact of height on airflow velocity was mainly reflected in zone 3. As the height increased, the maximum airflow velocity decreased. Compared to T5, the maximum airflow velocity at T7 increased by 31.8%. The primary reason for this is the difference in the strength of the wake effect at different heights. The structures of the bogies and skirts at the train bottom are rougher than the surface of the train body, causing greater disturbances. Thus, as the height enlarged, the wake effect was gradually weakened, and the maximum airflow velocity at the top of the tunnel declined. This was significantly different from the typical distribution of airflow velocity in the vertical direction when the fire source was stationary. For a stationary fire source, the flow directions in the upper and lower part of the tunnel were opposite. The upper hot smoke spread toward the tunnel openings, whereas the lower fresh air moved toward the fire source (Gannouni and Ben Maad, 2016). Fig. 13 showed the smoke concentration curves at measuring points T3, T6 and T9. Apparently, they possessed the identical maximum smoke concentration, and it took nearly the same duration for the smoke concentration to rise and then return to the initial level. This could be attributed to the similar maximum airflow velocity at these measuring points. When the power of the fire source was constant, the airflow velocity determined the degree of smoke dilution. However, researches had indicated that for stationary conditions, the smoke concentration at different locations was only related to the distance
Fig. 8. Smoke concentration variation with an on-fire train running through the tunnel.
different times. As the figure displayd, there was no smoke in front of the head car; all the smoke generated by the train head spread to the train tail along the bottom of the train. Comparing Fig. 9a with Fig. 9b and 9c, we found that when a running train suddenly caught fire, the smoke concentration around the train first increased and then maintained at a certain level. Additionally, throughout the train range, the smoke was confined underneath the bottom of the train. Nearly no smoke could be found in the annular space formed by the tunnel ceiling and train roof. After the smoke reached the train tail, the smoke plume inclined with the ground and spread upward, as shown in the red circle. However, the smoke was rapidly diluted by the piston wind. Thus, the concentration behind the train tail declined rapidly after a rapid rise. Fig. 10 represented the airflow velocity at the front, middle and rear of the tunnel for the 1/10 scale train with the velocity of 60 km/h. Measuring points T1, T6 and T11 were 2.5 m, 17.7 m and 32.9 m away from the tunnel entrance respectively. Moreover, as shown in Fig. 11, the airflow velocity variations at different longitudinal positions in the tunnel. By comparing each curve in zone 1, we found that the variation of T1 was different from that of the other measuring points. This could be ascribed to the varying distances from these measuring points to the tunnel entrance. When the train head reached T1, the train did not fully enter the tunnel. Therefore, the airflow velocity at T1 in zone 1 was smaller than those at the other measuring points. In addition, since the distances from T6 and T11 to the tunnel entrance were larger than the length of the train, the airflow velocity at these two measuring points continued to increase until the train fully entered the tunnel. Thus, the maximum airflow velocities were generally identical at T6 and T11 in
Fig. 9. Concentration field around an on-fire train at different times. (a) Train tail 4.5 m from the tunnel entrance. (b) Train tail 8.9 m from the tunnel entrance. (c) Train tail 17.7 m from the tunnel entrance. 6
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Fig. 10. Longitudinal distribution of airflow velocity. (a) Measuring point T1. (b) Measuring point T6. (c) Measuring point T11.
Fig. 11. Maximum airflow velocity along the longitudinal direction.
Fig. 12. The vertical distribution of airflow velocity.
from the fire source. In the process of diffusion, smoke was diluted by the entrainment of fresh air into the smoke flow. Thus, the larger the distance was, the smaller the concentration was. Eventually, the whole tunnel would be filled with smoke (Hu et al., 2010). For a moving fire source, because the piston wind velocity was always less than the vehicle velocity, the smoke could not spread to the front of the head car. In the rear of the tail, with fresh air continuously inhaled from the tunnel entrance, the smoke concentration could rapidly decrease after rising. Fig. 14 plotted the smoke concentration at different heights. As the height increased, the smoke concentration diminished. In terms of T6,
the maximum smoke concentration at T5 decreased by 65.6%. This phenomenon could be explained by the angle of the smoke plume in Fig. 17. Because the velocity of piston wind in the x-axis direction was much larger than the upward movement velocity of smoke, the smoke rarely reached the top of the tunnel. As a result, the concentration at the top of the tunnel was low. This phenomenon was contrary to that of a stationary fire source. For the latter, the smoke was mainly at the top of the tunnel, and the concentration increased with an increasing height. Basically, the smoke stratification could be discerned in the smoke spreading process (Tang et al., 2014). Besides, as shown in Fig. 14, the duration for the concentration at T6 from rise to fall was longer, while 7
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Table 2 Maximum airflow velocity at different train velocities. Vtrain
T1 (u/V)max
T5 (u/V)max
T6 (u/V)max
T7 (u/V)max
T11 (u/V)max
60 km/h 80 km/h 100 km/h 120 km/h
0.403 0.414 0.413 0.409
0.466 0.476 0.465 0.472
0.561 0.553 0.548 0.551
0.614 0.605 0.602 0.611
0.491 0.497 0.493 0.487
velocities, dimensionless time was adopted for the abscissa in the figure. The time was normalized by:
t' = Fig. 13. The longitudinal distribution of smoke concentration.
t Vtrain, 5Ltr
(2)
where t was the actual operation time (s) of the train, Ltr was the train length (m), and Vtrain was the train velocity (m/s). According to Fig. 15, the maximum velocity ratios were basically consistent at the different train velocities, indicating that the maximum airflow velocity increased linearly with the increase in train velocity. The similar results were obtained by Xi et al. (2015) and Zhang et al. (2018). This was because the larger the train velocity, the greater the piston effect as well as the greater the positive pressure in the front of the train and the greater the negative pressure behind the train. In other words, a larger train velocity could accelerate the spread of smoke. Table 2 summarized the maximum airflow velocities at measuring points T1, T5, T6, T7 and T11. The airflow velocity data at T1, T6 and T11 showed that the train velocity had no effect on the longitudinal distributions of the airflow velocity ratios. Moreover, the maximum airflow velocity ratio in the middle of the tunnel was still higher than that at the tunnel entrance and exit. However, the differences in airflow velocity between T1, T6 and T7 increased with an enlarging train velocity. This caused the smoke to reach the train tail more rapidly when the train was running in the middle of the tunnel than at the tunnel entrance and exit. Besides, the airflow velocity results at T5, T6 and T7 showed that the maximum airflow velocity at the top of the tunnel was still the lowest at the same cross-section. Fig. 16 presented the smoke concentrations at measuring point T6 at four different train velocities. Similarly, Table 3 listed the maximum smoke concentrations at measuring points T3, T5, T6, T7 and T9. The figure illustrated that the maximum smoke concentrations in the tunnel decreased as the train velocity increased. In addition, the larger the train velocity was, the more the concentration decreased. As the train velocity increased from 60 km/h to 80 km/h, the smoke concentration at T6 decreased by 15.58%. However, it decreased by 34.98% as the train velocity increased from 80 km/h to 100 km/h. Moreover, as the train velocity increased, the duration of concentration from the rise to
Fig. 14. Vertical distribution of smoke concentration.
the maximum smoke concentration at T7 was greater. Based on the analysis above, the differences in airflow velocities at different heights could explain this behavior. On the contrary, the measuring point T5 achieved the lowest maximum airflow velocity and maximum smoke concentration, as well as the shortest duration of the concentration from rise to fall. 3.3. Influence of train velocity on smoke movement characteristics Fig. 15 displayed the airflow velocity at measuring point T6 for the 1/10 scale model train with velocities of 60 km/h, 80 km/h, 100 km/h and 120 km/h. To facilitate the comparison between different train
Fig. 15. Airflow velocity at different train velocities.
Fig. 16. Smoke concentration at different train velocities. 8
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Table 3 Maximum smoke concentration at different train velocities. Vtrain
T3 (C/Cref)max
T5 (C/Cref)max
T6 (C/Cref)max
T7 (C/Cref)max
T9 (C/Cref)max
60 km/h 80 km/h 100 km/h 120 km/h
1.019 0.833 0.574 0.256
0.344 0.241 0.176 0.102
0.995 0.840 0.563 0.263
1.073 0.897 0.612 0.346
1.007 0.831 0.568 0.261
fall was shorter, which was consistent with the variation law of smoke concentration found by Xi et al. (2016). When the train velocity was lower, it weakened the dilution effect of fresh air on smoke. In addition, the incline angle α between the smoke plume and the ground was larger, resulting in more smoke reaching the ceiling, as shown in Fig. 17. For the four different train velocities, the incline angles α were 31°, 28°, 23°and 19°, respectively. This was because the larger train velocity generated larger airflow velocity in the x-axis direction, but in the z direction, the increase in airflow velocity was relatively small. Thus, α decreased with an increasing velocity. It also could be found
Fig. 18. Airflow velocity under different blockage ratios.
that when the train velocity was 60 km/h, the concentrations around and behind the train were much larger than that at 120 km/h.
Fig. 17. Concentration filed around the train at different train velocities. (a) 60 km/h. (b) 80 km/h. (c) 100 km/h. (d) 120 km/h. 9
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Moreover, the distance from the smoke front to the train tail decreased from 2.29 m to 1.40 m. Therefore, we concluded that when the on-fire train operated at a smaller velocity, the smoke concentration in the tunnel was higher. From the peak values of smoke concentration at measuring points T3, T6 and T9 presented in Table 3, we could observe that the maximum smoke concentrations under a certain train velocity were nearly the same, which further indicated that smoke movement in a tunnel was mainly affected by piston wind.
3.4. Influence of blockage ratio on smoke movement characteristics Fig. 18 showed the airflow velocities at measuring point T6 when the 1/10 and 1/15 scale model trains ran through the tunnel at a velocity of 60 km/h. The blockage ratios were 0.43 and 0.19, respectively. The figure illustrated that the blockage ratio had a significant impact on the airflow velocity. In zone 1, when the blockage ratio was higher, the pressure gradient between the front of the train head and the tunnel exit was greater, and hence the airflow velocity was higher. In zone 2, similarly, the annular space between the train and the tunnel was smaller, and thus the velocity of the reverse flow in the annular space enlarged. In zone 3, the larger the blockage ratio was, the greater the wake effect was. As expected a large blockage ratio could decelerate the spread of smoke, although the smoke behind the train tail always moved downstream. As a comparison, when a buring train decelerated and then stopped in the tunnel, the smoke would flow back to upstream. Besides, although a larger blockage ratio generated a greater piston wind velocity, the backflow moment first increased and then decreased with increasing blockage ratio. This was for the reason that after the train stopped for a period, the piston wind caused by the movement of the train disappeared, and the buoyancy was the only factor influencing the smoke flow. The larger the blockage ratio was, the greater the buoyancy was. Once the blockage ratio was larger than the certain value, the effect of buoyancy was greater than the effect of the residual piston wind. Table 4 presented the maximum airflow velocities at T1, T5, T6, T7 and T11. When the blockage ratio changed from 0.19 to 0.43, the increments in maximum airflow velocities at T1 and T11 were smaller than that at T6. In addition, the increase at T5 was smaller than those at T6 and T7, indicating that the changes in maximum airflow velocity at different heights in the tunnel differed with increasing blockage ratio. This discrepancies were caused by the different distances from the measuring points to the train surface. The point T5, located at the top of the tunnel, was the farthest from the train, whereas T7 was the closest to the train. Fig. 19 provided the smoke concentration at measuring point T6 when the different scaled trains ran through the tunnel. With a smaller blockage ratio, the maximum smoke concentration in the tunnel was larger, and the duration of the concentration from rising to declining was longer. Moreover, as shown in Fig. 20, the inclination angle α between the smoke plume and the ground decreased with increasing blockage ratio. Similarly, the distance between the smoke front and the train tail also decreased with increasing blockage ratio. This suggested that under a certain power of fire source, the main influencing factor of smoke concentration in the tunnel was the airflow velocity. A smaller blockage ratio corresponded to a smaller airflow velocity, leading to a weaker dilution effect. Also, the difference between the smoke movement velocity in the x direction and z direction was smaller. Thus, the
Fig. 19. Smoke concentration under different blockage ratios.
smoke concentration in the tunnel increased with a decreasing blockage ratio. However, when an on-fire train stopped in a tunnel, there was no piston wind in the tunnel and moreover the blockage reduced the area of cross-section, which resulted in the flow of fresh air would be hampered. Thus, the dilution effect of fresh air on smoke would be reduced (Alva et al., 2017). Moreover, incomplete combustion due to restricted ventilation may produce more smoke (Decimus et al., 2019) causing the smoke concentration increasing with an increasing blockage ratio. Obviously, this features was contrary to the behavior of moving fire sources in our study. Table 5 tabulated the maximum smoke concentrations at these five measuring points. The peak values of smoke concentrations at T3, T6, and T9 were almost identical at a certain blockage ratio. In addition, the distribution law of the maximum smoke concentration along the height direction did not vary with the blockage ratio, and the smoke concentration at T5 was the lowest. 4. Conclusions In this study, moving model tests of an on-fire subway train running through a tunnel were carried out to investigate the smoke movement characteristics of moving fire sources. Numerical simulations were supplemented for a validation and further analysis. The discrepancies between moving fire sources and stationary fire sources were analyzed. Finally, the impacts of train velocity and blockage ratio on smoke movement characteristics were also compared and discussed. Major conclusions were as follows. (1) The smoke movement characteristics of moving fire sources were significantly different from those of the stationary fire sources. For the moving fire sources, the piston wind was the dominant force for driving smoke movement. The smoke first spread to the train tail along the bottom of the train, and then moved upward due to the buoyancy, resulting in an increase in the concentration. After that, it was diluted by the piston wind and spread to the downstream, and the concentration decreased as well. (2) Near the middle of the tunnel, the airflow velocity and smoke concentration at different longitudinal positions were basically consistent. By contrast, they were different at varying heights even at the same cross section. Both of them decreased with increasing height, contrary to the stationary fire sources. Compared to measuring point T7 (0.17 m away from the rail surface), the airflow velocity and smoke concentration at T5 (0.49 m away from the rail surface) decreased by 24.1% and 67.9%, respectively. (3) Train velocity and blockage ratio made a significant influence on the smoke movement characteristics. When the velocity increased from 60 km/h to 120 km/h, the smoke concentration at T6 (center
Table 4 Maximum airflow velocity under different blockage ratios. β
T1 (u/V)max
T5 (u/V)max
T6 (u/V)max
T7 (u/V)max
T11 (u/V)max
0.43 0.19
0.403 0.277
0.466 0.367
0.561 0.415
0.614 0.458
0.491 0.383
10
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Table 5 Maximum smoke concentration under different blockage ratios. β
T3 (C/Cref)max
T5 (C/Cref)max
T6 (C/Cref)max
T7 (C/Cref)max
T9 (C/Cref)max
0.43 0.19
1.019 1.147
0.344 0.397
0.995 1.168
1.073 1.212
1.007 1.138
of the tunnel) decreased by 73.6%. Moreover, the maximum airflow velocity in the tunnel was approximately linear with train velocity. A higher blockage ratio resulted in a greater airflow velocity in the tunnel. As the blockage ratio grew from 0.19 to 0.43, the airflow velocity at T6 increased by 35.2%, and accordingly the concentration decreased by 14.8%. Declaration of Competing Interest The authors declared that they have no conflicts of interest to this work. Acknowledgements This research work is supported by grants from National Key R&D Program of China (No. 2016YFB1200601-B14) and the National Natural Science Foundation of China (No. 11902367). All authors are grateful to the anonymous reviewers for the constructive comments. References Alva, W.U.R., Jommas, G., Dederichs, A.S., 2017. The influence of vehicular obstacles on longitudinal ventilation in tunnel fires. Fire Saf. J. 87, 25–36. Chow, W.K., Gao, Y., Zhao, J.H., Dang, J.F., Chow, N.C.L., 2016. A study on titled tunnel fire under natural ventilation. Fire Saf. J. 81, 44–57. Clanche, J.L., Salizzoni, P., Creyssels, M., Mehaddi, R., Candelier, F., Vauquelin, O., 2014. Aerodynamics of buoyant releases within a longitudinally ventilated tunnel. Exp. Therm Fluid Sci. 57, 121–127. Decimus, A., Sonnier, R., Zavaleta, P., Suard, T., Ferry, L., 2019. Study of gases released under incomplete combustion using PCFC-FTIR. J. Therm. Anal. Calorim. 138, 753–763. Eftekharian, E., He, T.P., Kwok, K.C.S., Ong, R.H., Yuan, J.P., 2019. Investigation of fire –driven cross-wind velocity enhancement. Int. J. Therm. Sci. 141, 84–95. Gannouni, S., Ben Maad, R., 2016. Numerical analysis of smoke dispersion against the
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