Longitudinal ventilation systems are commonly installed in new tunnels. In this paper, based on the similarity law, the scale model with a view to different conditions is carried out to study the effectiveness of twin-tunnel complementary ventilation system. The system can offer enough amount of fresh air to meet requirement of driving safety by using longitudinal ventilation without ventilation shaft. Field measurements were also performed to validate the numerical model. Results reveal that particle concentration distribution is influenced by the distance from air interchange cross-passages to uphill tunnel inlet (
To provide sufficient fresh air and dilute toxic gases from vehicles, mechanical ventilation systems with jet fans or shafts are often employed [
Ventilation of resident buildings plays an important role in providing better indoor air quality (IAQ) and thermal comfort [
In this paper, CFD simulations were conducted under different conditions (AIAA, 1998) [
A substantial number of tunnel projects have been constructed in complex geological area as western development strategies have been implemented in China [
Dabieshan Tunnel.
The twin-tunnel complementary ventilation system has two-air interchange cross-passages to connect the two tunnels, which divide the tunnels into 6 sections, and the distance between two-air interchange cross-passages is 100m, far less than the length of tunnel. Section
Twin-tunnel complementary ventilation system.
Where
Field measurement was carried out to reveal the characteristics of air flow and particle concentration distribution in the twin-tunnels with twin-tunnel complementary ventilation system. There are a total of eight cross-sections to be tested as shown in Figure
Field measurement scheme: (a) the location of monitored cross-sections, (b) detail of the cross-sections VII-VII and VIII-VIII, and (c) detail of the cross-sections I-I, II-II, III-III, IV-IV, V-V, and VI-VI.
Field measurement: (a) testing preparation in 2# air interchange cross-passage and (b) testing progress in tunnel.
The computational fluid dynamics (CFD) software ANSYS Fluent (15.0) was used to simulate the flow field and pollution transport of the twin-tunnel complementary ventilation system. Fluent software has been applied to solve 3-D continuity, momentum, turbulence kinetic energy, turbulence energy dissipation rate, and pollutant transport equations in steady and incompressible condition. A standard
The tunnel investigated is a twin-tunnel tunnel, the uphill tunnel length is 4910m, and the downhill tunnel length is 4908m. To reduce the element number of model and reduce the computation cost and achieve an accurate solution, the reduced scale numerical simulation model was obtained from the full scale one by means of Euler scaling method, which preserves geometrical, kinematic and dynamic similitude, and similarities of the initial and boundary condition.
In the reduced scale numerical simulation model, the shape was similarity to the full scale model, the cross-sectional scaling ratio of 1/1 (
Euler scaling method is based on the Euler number preservation and the Euler number is defined as
During the process of researching fluid motion, fluid flow of both the reduced scale numerical simulation model and full scale model must be kinematical similarity and dynamic similarity; thus, the velocity of each section in the reduced scale numerical simulation model should be as same as them in the full scale model and the velocity is scaled as
Introducing (
The reduced scale of twin-tunnel complementary ventilation system to be modeled consists of twin parallel tunnels with the length 900m including 100m section 2 and 100 m section 5, width 12m and height 8m, and the connecting transverse ducts with the width 5 m and height 6.35 m. For grid generation process, a multizone grid approach was applied. Structured mesh was used over the entire computational domain except for air interchange cross-passage zones. Unstructured mesh was applied for air interchange cross-passage zones. Independence mesh tests were carried out with four different mesh sizes to achieve optimal grid for the computational domain. The mesh sizes analyzed were shown in Table
Mesh types and sizes.
Mesh | Mesh size in the longitudinal direction(m) | Mesh size on the cross-section of tunnel(m) | Total cells |
---|---|---|---|
A | 2 | 1 | 121937 |
B | 1.5 | 0.8 | 296037 |
C | 1 | 0.5 | 583586 |
D | 0.5 | 0.5 | 813981 |
Mesh analysis.
Geometry and meshing of the numerical model.
The no-slip stationary wall boundary condition was used for solid walls of the tunnel and the connecting ducts, the inlet, and outlet gage pressure boundary condition (
The levels of factors in number simulation.
Factor | Levers of factor | |||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
| 2400 | 2880 | 3120 | 3360 | 3600 | 3840 |
| 125 | 150 | 175 | 200 | 225 | 250 |
| 75 | 100 | 125 | 150 | 175 | — |
For validation of the computational model, the experimental measurement data of Dabieshan tunnel ventilation system in section 2.1 is used. In the tunnel ventilation, according to (
Correlation of average air velocities between CFD value and field measurement.
Correlation of average particle concentration between CFD value and field measurement.
The performance of the twin-tunnel complementary ventilation and longitudinal ventilation in same traffic condition was investigated. The fresh air introduced to the twin-tunnel by each type of ventilation system is equal, the simulation case is listed in Table
Simulation case.
Ventilation system | Fresh air volume(m3/s) | | | | |
---|---|---|---|---|---|
Uphill tunnel | Downhill tunnel | ||||
Twin-tunnel complementary ventilation | 300 | 300 | 200 | 125 | 3360 |
Longitudinal ventilation | 300 | 300 | 0 | 169 | NO |
Particle concentration contours (m−1) at horizontal planes y = 1.5m for tunnel with twin-tunnel complementary ventilation, (a) there is flow in air interchange cross-passages, and (b) there is no flow in air interchange cross-passages.
Figure
Particle concentration profiles variation for various air interchange cross-passages positions: (a) the particle concentration in uphill tunnel outlet, (b) the particle concentration in uphill tunnel outlet, and (c) the particle concentration ratio of uphill tunnel outlet and downhill tunnel outlet.
To further analyze the relation between
Figure
Particle concentration profiles variation for various interchanged air volume: (a) the particle concentration in uphill tunnel outlet, (b) the particle concentration in uphill tunnel outlet, and (c) the particle concentration ratio of uphill tunnel outlet and downhill tunnel outlet.
As discussed above, the parameters, including the distance between uphill tunnel inlet to air interchange cross-passage
Factors and levels of orthogonal experiment.
Factor | Levers of factor | |||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | ||
A | | 150 | 175 | 200 | 225 | 250 |
B | | 2880 | 3120 | 3360 | 3600 | 3840 |
C | | 75 | 100 | 125 | 150 | 175 |
Orthogonal table and test results.
Factor | ||||||
---|---|---|---|---|---|---|
NO. | A | D | B | E | C | |
1 | 175 | 1 | 312 | 1 | 100 | 1.045 |
2 | 175 | 2 | 336 | 2 | 125 | 0.992 |
3 | 175 | 3 | 360 | 3 | 150 | 0.945 |
4 | 175 | 4 | 384 | 4 | 175 | 0.902 |
5 | 200 | 1 | 336 | 3 | 175 | 1.019 |
6 | 200 | 2 | 312 | 4 | 150 | 1.093 |
7 | 200 | 3 | 384 | 1 | 125 | 0.690 |
8 | 200 | 4 | 360 | 2 | 100 | 0.730 |
9 | 225 | 1 | 360 | 4 | 125 | 0.727 |
10 | 225 | 2 | 384 | 3 | 100 | 0.579 |
11 | 225 | 3 | 312 | 2 | 175 | 1.065 |
12 | 225 | 4 | 336 | 1 | 150 | 0.888 |
13 | 250 | 1 | 384 | 2 | 150 | 0.622 |
14 | 250 | 2 | 360 | 1 | 175 | 0.761 |
15 | 250 | 3 | 336 | 4 | 100 | 0.744 |
16 | 250 | 4 | 312 | 3 | 125 | 0.913 |
| ||||||
K1 | 3.884 | 3.413 | 4.116 | 3.384 | 3.098 | T = 13.715 |
K2 | 3.532 | 3.425 | 3.643 | 3.409 | 3.322 | Q = 12.164 |
K3 | 3.260 | 3.444 | 3.162 | 3.456 | 3.548 | P = 11.757 |
K4 | 3.040 | 3.433 | 2.794 | 3.466 | 3.747 | |
R | 0.844 | 0.031 | 1.322 | 0.083 | 0.649 | |
| 0.099 | 0.000 | 0.248 | 0.001 | 0.059 |
The sum of squared deviations caused by each factor
The degree of freedom corresponding to sum of squared deviations of one column
Values of mean square.
| | | | | |
---|---|---|---|---|---|
0.033 | 0.000 | 0.083 | 0.000 | 0.020 | 0.000 |
Contrast table of significance.
Source of difference | | | | | F0.05 | Significance |
---|---|---|---|---|---|---|
A | 0.0994 | 3 | 0.0331 | 155.878 | 6.39 | II |
B | 0.2480 | 3 | 0.0827 | 389.032 | 6.39 | I |
C | 0.0590 | 3 | 0.0197 | 92.557 | 6.39 | III |
| 0.0013 | 6 | 0.0017 | |||
Total | 0.4076 | 15 |
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors would like to acknowledge the financial support for this work provided by the China Railway Siyuan Survey and Design Group R&D Program (2017k81-1), the Special Fund for Basic Scientific Research of Central Colleges of Chang’an University (nos. 310821172004, 310821153312, 310821165011, 310821151018, and 310821173312), the Special Fund for Scientific Research Project of Shaanxi Provincial Education Department (2013JK0958), and the Project on Social Development of Shaanxi Provincial Science and Technology Department (nos. 2018SF-382, 2018SF-378).