CFD simulation and optimization of the ventilation for subway side-platform

Transcription

1 Tunnelling and Underground Space Technology 22 (2007) Tunnelling and Underground Space Technology incorporating Trenchless Technology Research CFD simulation and optimization of the ventilation for subway side-platform Feng-Dong Yuan *, Shi-Jun You School of Environment Science and Technology, Tianjin University, No. 72, Weijin Road, Nankai District, Tianjin , China Received 27 May 2006; received in revised form 23 September 2006; accepted 19 October 2006 Available online 5 December 2006 Abstract To obtain the velocity and temperature field of subway station and the optimized ventilation mode of subway side-platform station, this paper takes the evaluation and optimization of the ventilation for subway side-platform station as main line, builds three dimensional models of original and optimization design of the existed and rebuilt station. And using the two-equation turbulence model as its physics model, the thesis makes computational fluid dynamics (CFD) simulation to subway side-platform station with the boundary conditions collected for simulation computation through field measurement. It is found that the two-equation turbulence model can be used to predict velocity field and temperature field at the station under some reasonable presumptions in the investigation and study. At last, an optimization ventilation mode of subway side-platform station was put forward. Ó 2006 Elsevier Ltd. All rights reserved. Keywords: Ventilation; CFD simulation; k-e model; Subway; Side-platform 1. Introduction Computational fluid dynamics (CFD) software is commonly used to simulate fluid flows, particularly in complex environments (Chow and Li, 1999; Zhang et al., 2006; Moureh and Flick, 2003). CFD is capable of simulating a wide variety of fluid problems (Gan and Riffat, 2004; Somarathne et al., 2005; Papakonstantinou et al., 2000; Karimipanah and Awbi, 2002). CFD models can be realistically modeled without investing in more costly experimental method (Betta et al., 2004; Allocca et al., 2003; Moureh and Flick, 2003). So CFD is now a popular design tool for engineers from different disciplines for pursuing an optimum design due to the high cost, complexity, and limited information obtained from experimental methods (Li and Chow, 2003; Vardy et al., 2003; Katolidoy and Jicha, 2003). Tunnel ventilation system design can be developed in depth from the predictions of various parameters, such * Corresponding author. Tel.: ; fax: address: [email protected] (F.-D. Yuan). as vehicle emission dispersion, visibility, air velocity, etc. (Li and Chow, 2003; Yau et al., 2003; Gehrke et al., 2003). Earlier CFD simulations of tunnel ventilation system mainly focus on emergency situation as fire condition (Modic, 2003; Carvel et al., 2001; Casale, 2003). Many scientists and research workers (Waterson and Lavedrine, 2003; Sigl and Rieker, 2000; Gao et al., 2004; Tajadura et al., 2006) have done much work on this. This paper studied the performance of CFD simulation on subway environment control system which has not been studied by other paper or research report. It is essential to calculate and simulate the different designs before the construction begins, since the investment in subway s construction is huge and the subway should run up for a few decade years. The ventilation of subway is crucial that the passengers should have fresh and high quality air (Lowndes et al., 2004; Luo and Roux, 2004). Then if emergency occurred that the well-designed ventilation system can save many people s life and belongings (Chow and Li, 1999; Modic, 2003; Carvel et al., 2001). The characteristics of emergency situation have been well investigated, but there have been /$ - see front matter Ó 2006 Elsevier Ltd. All rights reserved. doi: /j.tust

2 F.-D. Yuan, S.-J. You / Tunnelling and Underground Space Technology 22 (2007) few studies in air distribution of side-platform in normal conditions. The development of large capacity and high speed computer and computational fluid dynamics technology makes it possible to use CFD technology to predict the air distribution and optimize the design project of subway ventilation system. Based on the human-oriented design intention in subway ventilation system, this study simulated and analyzed the ventilation system of existent station and original design of rebuilt stations of Tianjin subway in China with the professional software AIRPAK, and then found the optimum ventilation project for the ventilation and structure of rebuilt stations. 2. Ventilation system Tianjin Metro, the secondly-built subway in China, will be rebuilt to meet the demand of urban development and expected to be available for Beijing 2008 Olympic Games. The existent subway has eight stations, with a total length of km and a km average interval. For sake of saving the cost of engineering, the existent subway will continue to run and the stations will be rebuilt in the rebuilding Line 1 of Tianjin subway. Although different existent stations of Tianjin Metro have different structures and geometries, the Southwest Station is the most typical one. So the Southwest Station model was used to simulate and analyze in the study. Its geometry model is shown in Fig The structure and original ventilation mode of existent station The subway has two run-lines. The structure of Southwest Station is, length width height = 74.4 m(l) 18.7 m(w) 4.4 m(h), which is a typical side-platform station. Each side has only one passageway (length height = 6.4 m(l) 2.9 m(h)). The middle of station is the space for passengers to wait for the vehicle. The platform mechanical ventilation is realized with two jet openings located at each end of station and the supply air jets towards train and track. There is no mechanical exhaust system at the station and air is removed mechanically by tunnel fans and naturally by the exits of the station The design structure and ventilation of rebuilt station The predicted passenger flow volume increase greatly and the dimension of the original station is too small, so in the rebuilding design, the structure of subway station is changed to, (length width height = 132 m(l) m(w) 4.65 m(h)), and each side has two passageways. The design volume flow of Southwest Station is m 3 /h. For most existent stations, the platform height is only 2.9 m, which is too low to set ceiling ducts. So in the original design, there are two grille vents at each end of the platform to supply fresh air along the platform length direction and two grille vents to jet air breadthways towards trains. The design velocity of each lengthways grille vent is 5.54 m/s. For each breadthways vent, it is 5.28 m/s. Under the platform, 80 grille vents of the same velocity (4.62 m/s, 40 for each platform of the station) are responsible for exhaust. 3. CFD simulation and optimization The application of CFD simulation in the indoor environment is based on conversation equations of energy, mass and momentum of incompressible air. The study Fig. 1. The location of test section and the layout of measuring points.

3 476 F.-D. Yuan, S.-J. You / Tunnelling and Underground Space Technology 22 (2007) adopted a turbulence energy model that is the two-equation turbulence model advanced by Launder and Spalding. And it integrated the governing equation on the capital control volumes and discretized in the definite grids, at last simulated and computed with the AIRPAK software Preceding simplifications and presumptions Because of mechanical ventilation and the existence of train-driven piston wind, the turbulence on platform is transient and complex. Unless some simplifications and presumptions are made, the mathematics model of threedimensional flow is not expressed and the result is divergent. While ensuring the reliability of the computation results, some preceding simplifications and presumptions have to be taken. (1) The period of maximum air velocity is paid attention to in the transient process. Apparently the maximum air velocity is reached at the period when train stops at or starts away from the station (Yau et al., 2003; Gehrke et al., 2003), so the period the simulation concerns about the best period of time for simulation is from the point when at the section of x = 0.0 m (Fig. 1) and the air velocity begin to change under piston-effect to the point when train totally stops at the station (defined as a pulling-in cycle ). (2) Though the pulling-in cycle is a transient process, it is simplified to a steady process. (3) Because the process is presumed to a steady process, the transient velocity of test sections, which was tested in Southwest Station in pulling-in cycle, is presumed to the time-averaged velocity of test sections. (4) The volume flow driven into the station by pulling-in train is determined by such factors as BR (blocking ratio, the ratio of train cross-section area to tunnel cross-section area), the length of the train and the resistance of station etc. For existent and new stations, BRs are almost the same. Although the length of the latter train doubles that of the former which may increase the piston flow volume, the resistance of latter is greater than that of the former which may counteract this increase. So it is presumed that the piston flow volume is same for both existent and new station and that the volume flow through the passenger exits is also same. Based on this presumption, the results of the field measurements at the existent station can be used as velocity boundary conditions to predict velocity filed of new station Original conditions To obtain the boundary conditions for computation and simulation, such as the air velocity and temperature of enclosure, measures were done by times at Southwest Station. All data are recorded during a complete pulling-in cycle. The air velocities were measured by the multichannel anemonmaster hotwire anemoscope and infrared thermometer is used to measure the temperature of the walls of the station which are taken as the constant temperature thermal conditions in the simulation Temperatures of enclosure Divide the platform into five segments and select some typical test positions. The distributing temperature of enclosure is shown in Table 1. It can be seen from Table 1 that all temperatures of enclosure are between 23 C and 25 C, there is little difference in all test positions, and the average temperature is 24 C. So all temperatures of subway station s walls is 24 C in CFD computation and simulation Time-averaged air velocity above the platform Fig. 1 is the location of test section and the layout of measuring points. The data measured include 12 transient velocities in each section (A H in Fig. 1), which were deal with section s time-averaged velocities in the period, 12 point s velocities of passageway, which is used to acquire the average flow, and the velocities of each end of station, which is used to acquire the average piston flow volume. Fig. 2 is the lengthways velocities measured of platform sections, V max is the maximum air velocity, V min is the minimum air velocity and V ave is the average air velocity. Fig. 2 shows that the maximum air velocity is at the passageway. At the passageway the change of air velocity is about 2.25 m/s, which is the maximum and indicates that the passageway is the position effected most by the piston wind effect, and the air velocity of section D and E after the passageway is almost the same, which indicates that the piston wind can hardly effect the air velocity after the passageway. Table 1 Distributing temperature of enclosure Test positions Station walls Platform Top Railway ground Lateral wall Ground Temperature ( C) when x = 12.8 m Temperature ( C) when x = 25.6 m Temperature ( C) when x = 38.4 m Temperature ( C) when x = 51.2 m

4 F.-D. Yuan, S.-J. You / Tunnelling and Underground Space Technology 22 (2007) station in CFD simulation is shown in Table 2. Additionally, the Tianjin Metro is out of run during the measuring time, so the heat load is not typical and is difficult to be determined so that the distributing temperature was not simulated in this study. Referred to the feasibility research report of No. 3 design institute of China s ministry of railway and the measured data in Southwest Station, the boundary conditions of new station in CFD simulation of the existent station is shown in Table Solution process Fig. 2. Measured lengthways velocities at platform sections. In addition, because of the movement of train, the velocity cannot be measured in section U of Fig. 1, the air velocity of section U was computed in CFD simulation Boundary conditions of simulation Through analyzing the boundary data measured at the Southwest Station, the boundary conditions of the existent Method of solution The governing equations to calculate original variables, such as velocity and temperature etc., can be shown (Moureh and Flick, 2003). o ðq/þþdivðq~u/þ ¼divðCgrad/ÞþS ot ð1þ where / is common variable and denotes (u,v,w,t), ~u is velocity vector, q is density, C is dissipation function, S is the source item. u is air velocity of direction X, v and w are air velocities of direction Y and Z, respectively, and T is air temperature. Eq. (1) is integrated on the spatial control volumes, and through discretizing in the definite grids Table 2 Boundary condition of existent stations in CFD simulation Sections Section G Section U (estimated) Section H Passageway of the trainstopping side:exit1 Passageway of the non-train stopping side:exit2 Air velocity (m/s) Air flow (m 3 /s) The exit of train F Table 3 Boundary condition of rebuilt stations in CFD simulation (a) Heat load Persons Lamps (on the top of platform) Heat load 44 kw/ person Advertisement lamps (lateral wall of platform) Heat emitted from train 13 W/m 2 15 kw/station 320 kw (acceleration) and 200 kw (heat from applying brake) (b) Air velocity Supply air (lengthways) Supply air (breadthways) Under the platform vents Air velocity (m/s) (c) Boundary conditions of air velocity Boundary conditions of air velocity (m/s) (d) Temperature Temperature ( C) The entrance of train The exit of train Passageway of the train-stopping side Train walls (expert for excluding top and bottom of train) Outdoor air (summer) Supply air (summer) Passageway of the non-train-stopping side Tunnel air Enclosure walls

5 478 F.-D. Yuan, S.-J. You / Tunnelling and Underground Space Technology 22 (2007) Table 4 Settlement of existent station ventilation CFD simulation Variable Pressure Kinetic k e Discretization Body force weighted Second order upwind Second order upwind Second order upwind Relaxation coefficient Solution Pressure AMG a Kinetic AMG k AMG e AMG Cycle type V-type Flexible Flexible Flexible a AMG is algebra multi-grid. Table 5 Settlement of new station ventilation CFD simulation Variable Pressure Temperature Kinetic k e Discretization Body force weighted Second order upwind First order upwind First order upwind First order upwind Relaxation coefficient Solution Pressure AMG Temperature AMG Kinetic AMG k AMG e AMG Cycle type V-type Flexible Flexible Flexible Flexible a p / ¼ X a nb / nb þ b ð2þ nb In Eq. (2), nb denotes neighbor grids; a p and a nb are the coefficients of / and / nb, respectively, b is the source item. (2) Tables 4 and 5 shows solution, cycle type and convergence criterion and relaxation coefficient in CFD simulation of the existent station ventilation and the new station Simulation and discussion Used the boundary conditions measured in Southwest Station, the air velocity of subway side-platform station was simulated in AIRPAK. Fig. 3 is the air velocity comparison of simulation and measurement in section A E, which shows that the two-equation model and the preceding simplifications are acceptable in predicting flow at the station. Although the computed velocity at section C and Fig. 3. Air velocity of section A E simulation and measurement. E is not equal to the values of the field measurement, whose error is about m/s, the differences are acceptable and the overall variance trend of the velocity along the platform tallies with the measurement. The simulation result of air velocity of 1.7 m height above platform of existent station can be seen in Fig. 4. Fig. 4 shows that the majority of piston winds pour out from the passageway and the air velocity of the zone after the passageway is lower, which is below 0.4 m/s. The maximum air velocity at 1.7 m height above platform is the passageway of the train-stopping side, which is about 1.89 m/s. The simulation results indicate that it is applicable to use two-equation turbulence model to predict time-averaged flow and temperature distribution at subway station so the veracity is reliable. Fig. 5 is the simulation result, which is the original design of air velocity at 1.7 m height above platform of new station. It is shown from Fig. 5 that at the training-stopping side of platform the supply air does not reach the zone between the two exits, which is for the passenger to wait for the train, and pour out from the passageway because of the leakage of passageway. The air velocity between two passageways, which is below 0.4 m/s, is a little low so that the Dead Zone is formed. The simulation also shows that the maximum velocity exists at the exits, which agrees with the references and experience. Fig. 6 is the temperature distribution at 1.7 m height above platform of the new station, which is used to simulate for original design ventilation mode. It shows that the temperature distribution is imbalanced for the leakage of exits at the training-stop side of platform, and at each end of platform, the temperature should be approximately between 24 C and 26 C while the temperature is between 32 C and34 C because the heat away from the train can not be cooled and removed by the cold supply air in time, even at the x = 61 m point, the temperature reaches above 40 C, which makes people feel uncomfortable. The simulation result of temperature and air velocity of original design at the lengthways center section is shown in

6 F.-D. Yuan, S.-J. You / Tunnelling and Underground Space Technology 22 (2007) Fig. 4. Simulation result of air velocity at 1.7 m height above platform of existent station. Fig. 5. Simulation result of air velocity at 1.7 m height above platform of new station (original design). Fig. 6. Simulation result of temperature at 1.7 m height above platform of new station (original design). Fig. 7, which shows that the distribution of temperature and air velocity is not balanceable and the zone between two exits is Dead Zone. From the simulation of original design, the following can be seen: (1) The ventilation mode that the feasibility research report design for the side-platform station of subway makes the distribution of temperature and air velocity unbalanced, so the passengers will feel uncomfortable at the middle of platform.

7 480 F.-D. Yuan, S.-J. You / Tunnelling and Underground Space Technology 22 (2007) Fig. 7. Simulation result of temperature and air velocity at the lengthways center section (original design). (2) The air velocity is too high at some position of platform and the passengers feel easily the change of air velocity, whose scope is between 0.4 m/s and 2.5 m/s, and feel uncomfortable when they go to exits from the middle of platform. (3) The efficiency of ventilation is not as good as expectation and the supply air from jet fans cannot reach the middle of platform adequately and part of them will flow away from the passageways. Because of the unbalanced distribution of temperature and air velocity, original design s ventilation mode was optimized. The exhaust vents are as same as that of original ones. The flow of supplied air remains the same and under the platform there are tubes of supplied fresh air. Also there are some vertical tubes fetched out by platform. As a result, the air is supplied widthways and reasonably the flow of supplied air is delivered in balance, which eliminates the unbalanced distribution of temperature and air velocity on the platform. On the other hand the breadthways supplied air prevents the heat brought by train from diffusing onto the platform, and the heat can be vented in time from the exhaust vents under the platform. At the same time the fresh air from the breadthways vents passes through the passengers, takes away heat and humidity, and the efficiency of ventilation is quite satisfactory. To validate the optimization ventilation mode, the simulation adopted the same boundary conditions and set up with original design s CFD simulation. The simulation results are showed in Figs. 8 and 9. Fig. 8 is the air velocity distribution at 1.7 m height above platform of new station, which is simulated for optimization design ventilation mode. It shows that the distribution of air velocity is good enough and balanced. The breadthways supplied air makes the Dead Zone disappeared, and the maximum air velocity is only about 1.67 m/s at 1.7 m height above platform of new station, which makes it difficult for passengers to feel windy. Fig. 9 is the simulation result of temperature and air velocity of original design at the lengthways center section, which shows that the Dead Zone disappears at the lengthways center section. And the breadthways supplied air prevents the heat, which is from the air conditioning condenser and the friction produced by train-stopping, from diffusing to the platform, so the temperature is about below 28 C. Compared with the ventilation mode in original design, the optimization ventilation mode has several advantages as follows: (1) The supply air is delivered to every zone in a balanced way and the distribution of temperature and air velocity of side-platform is also balanced. (2) The air velocity is balanced and the maximum is only about 1.67 m/s. Fig. 8. Simulation result of air velocity at 1.7 m height above platform of new station (optimization design).

8 F.-D. Yuan, S.-J. You / Tunnelling and Underground Space Technology 22 (2007) Fig. 9. Simulation result of temperature and air velocity at the lengthways center section (optimization design). (3) The supplied air removes the heat of platform better and the whole platform s temperature is lower than that of the original design, which can lower temperature standard and can decrease the flow of supply air to save energy. (4) The supplied air from breadthways vents also well prevents the hot contaminated train-driven air from spreading onto the platform, and on the other hand the fresh air from the breadthways vents passes through the passengers, takes away heat and humidity, collects and is exhausted by the under-platform vents. (5) The ventilation efficiency is quite satisfactory. 4. Conclusions The following can be concluded from this study: The air flow in platform of subway is a complex threedimensional transient turbulence. Simplification of the air flow to steady process and presumption of the transient velocity to the time-averaged velocity are applicable to simulate the distribution of temperature and air velocity of subway platform in the pulling-in cycle. It is applicable to use two-equation turbulence model to predict time-averaged velocity and temperature distribution at subway station. The simulation results are influenced greatly by the boundary conditions such as boundary velocity and temperature. The optimization ventilation mode has more advantages than the original design s. The breadthways ventilation is suggested when the height of platform is too low to have ceiling ducts. It makes the distribution of temperature and air velocity balanced. Before a design is accepted it is very necessary to evaluate and optimize it by such techniques as CFD, etc. It will help a lot to build a reliable, effective and economical system. Acknowledgement This project was supported by the funding generated by the Tianjin Municipal Science and Technology Commission of China (Project No: ). References Allocca, C., Chen, Q.Y., Glicksman, L.R., Design analysis of singlesided natural ventilation. Energy and Buildings 35, Betta, V., Cascetta, F., Labruna, P., Palombo, A., A numerical approach for air velocity predictions in front of exhaust flange slot openings. Building and Environment 39, Carvel, R.O., Beard, A.N., Jowitt, P.W., The influence of longitudinal ventilation systems on fires in tunnels. Tunnelling and Underground Space Technology 16, Casale, E., The automation of the aeraulic response in the case of a fire in a tunnel-first concrete answers. In: Proceedings of Claiming the Underground Space, pp Chow, W.K., Li, J.S.M., Safety requirement and regulations reviews on ventilation and fire for tunnels in the Hong Kong Special Administration Region. Tunnelling and Underground Space Technology 14, Gan, G.H., Riffat, S.B., CFD modelling of air flow and thermal performance of an atrium integrated with photovoltaics. Building and Environment 39, Gao, P.Z., Liu, S.L., Chow, W.K., Fong, N.K., Large eddy simulations for studying tunnel smoke ventilation. Tunnelling and Underground Space Technology 19, Gehrke, P.J., Stacev, C.H.B., Agnew, N.D., Rail tunnel temperature stratification and implications for train and tunnel ventilation design. In: Proceedings of 11th International Symposium on Aerodynamics and Ventilation of Vehicle Tunnels, pp Karimipanah, T., Awbi, H.B., Theoretical and experimental investigation of impinging jet ventilation and comparison with wall displacement ventilation. Building and Environment 37, Katolidoy, J., Jicha, M., Eulerian-Lagrangian model for traffic dynamics and its impact on operational ventilation of road tunnel. In: Proceedings of 11th International Symposium on Aerodynamics and Ventilation of Vehicle Tunnels, pp Li, J.S.M., Chow, W.K., Numerical studies on performance evaluation of tunnel ventilation safety systems. Tunnelling and Underground Space Technology 18, Lowndes, Ian S., Crossley, Amanda J., Yang, Zhi Yuan, The ventilation and climate modeling of rapid development tunnel drivages. Tunnelling and Underground Space Technology 19, Luo, S., Roux, B., Modeling of the HESCO nozzle diffuser used in IEA Annex-20 experiment test room. Building and Environment 39, Modic, J., Fire simulation in road tunnels. Tunnelling and Underground Space Technology 18, Moureh, J., Flick, D., Wall air-jet characteristics and airflow patterns within a slot ventilated enclosure. International Journal of Thermal Sciences 42, Papakonstantinou, K.A., Kiranoudis, C.T., Markatos, N.C., Computational analysis of thermal comfort: the case of the archaeological museum of Athens. Applied Mathematical Modelling 24,

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