Flow and sound fields of scaled high-speed trains with different coach numbers running in long tunnel

Abstract

Segregated incompressible large eddy simulation and acoustic perturbation equations were used to obtain the flow field and sound field of 1:25 scale trains with three, six and eight coaches in a long tunnel, and the aerodynamic results were verified by wind tunnel test with the same scale two-coach train model. Time-averaged drag coefficients of the head coach of three trains are similar, but at the tail coach of the multi-group trains it is much larger than that of the three-coach train. The eight-coach train presents the largest increment from the head coach to the tail coach in the standard deviation (STD) of aerodynamic force coefficients: 0.0110 for drag coefficient (C _d), 0.0198 for lift coefficient (C _l) and 0.0371 for side coefficient (C _s). Total sound pressure level at the bottom of multi-group trains presents a significant streamwise increase, which is different from the three-coach train. Tunnel walls affect the acoustic distribution at the bottom, only after the coach number reaches a certain value, and the streamwise increase in the sound pressure fluctuation of multi-group trains is strengthened by coach number. Fourier transform of the turbulent and sound pressures presents that coach number has little influence on the peak frequencies, but increases the sound pressure level values at the tail bogie cavities. Furthermore, different from the turbulent pressure, the first two sound pressure proper orthogonal decomposition (POD) modes in the bogie cavities contain 90% of the total energy, and the spatial distributions indicate that the acoustic distributions in the head and tail bogies are not related to coach number.

Keywords

Flow and sound fields / Scaled high-speed trains / Different coach numbers / Long tunnel / Proper orthogonal decomposition

References

[1.] Shin CH, Park WG. Numerical study of flow characteristics of the high-speed train entering into a tunnel. Mech Res Commun, 2003, 30(4): 287-296,

[2.] Ko YY, Chen CH, Hoe IT, et al.. Field measurements of aerodynamic pressures in tunnels induced by high speed trains. J Wind Eng Ind Aerodyn, 2012, 100(1): 19-29,

[3.] Seo SI, Park CS, Min OK. A study on fluctuating pressure load on high speed train passing through tunnels. J Mech Sci Technol, 2006, 20(4): 482-493,

[4.] Li ZW, Yang MZ, Huang S, et al.. A new method to measure the aerodynamic drag of high-speed trains passing through tunnels. J Wind Eng Ind Aerodyn, 2017, 171: 110-120,

[5.] Li X, Wu Z, Yang J, et al.. Experimental study on transient pressure induced by high-speed train passing through an underground station with adjoining tunnels. J Wind Eng Ind Aerodyn, 2022, 224: 104984,

[6.] Xie P, Peng Y, Wang T, et al.. Aural comfort prediction method for high-speed trains under complex tunnel environments. Transp Res Part D Transp Environ, 2020, 81: 102284,

[7.] Qin D, Li T, Wang H, et al.. A fast approach for predicting aerodynamic noise sources of high-speed train running in tunnel. CMES-Comput Model Eng Sci, 2022, 130(3): 1371-1386

[8.] Diedrichs B, Krajnović S, Berg M. On the aerodynamics of car body vibrations of high-speed trains cruising inside tunnels. Eng Appl Comput Fluid Mech, 2008, 2(1): 51-75

[9.] Li X, Shang T, Han L, et al.. Numerical study on aerodynamic noises and characteristics of the high-speed train in the open air and tunnel environment. J Vibroeng, 2017, 19(4): 3113-3128,

[10.] Tan XM, Liu HF, Yang ZG, et al.. Characteristics and mechanism analysis of aerodynamic noise sources for high-speed train in tunnel. Complexity, 2018, 12: 1-20,

[11.] Östh J, Kaiser E, Krajnović S, et al.. Cluster-based reduced-order modelling of the flow in the wake of a high speed train. J Wind Eng Ind Aerodyn, 2015, 145: 327-338,

[12.] Ferrari S, Hu Y, Martinuzzi RJ, et al (2017) Visualizing vortex clusters in the wake of a high-speed train. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, pp 683–688

[13.] Li XB, Chen G, Liang XF, et al.. Research on spectral estimation parameters for application of spectral proper orthogonal decomposition in train wake flows. Phys Fluids, 2021, 33(12): 125103,

[14.] National Railway Administration of the People’s Republic of China (2017). Code for design of railway tunnel. TB 10003–2016

[15.] Nicoud F, Ducros F. Subgrid-scale stress modelling based on the square of the velocity gradient tensor. Flow Turbul Combust, 1999, 62: 183-200,

[16.] Ewert R, Schröder W. Acoustic perturbation equations based on flow decomposition via source filtering. J Comput Phys, 2003, 188(2): 365-398,

[17.] Menter FR. Two-equation eddy-viscosity turbulence modeling for engineering applications. AIAA J, 1994, 32(8): 1598-1605,

[18.] Ouyang MH, Chen S, Li QL. Analysis of unsteady flow field and near-field aerodynamic noise of scale high-speed trains in long tunnel. Appl Acoust, 2023, 205: 109261,

[19.] Ouyang MH, Chen S, Li QL, et al.. Numerical investigation on aerodynamic forces and flow patterns of high-speed trains from open air into long tunnel. J Wind Eng Ind Aerodyn, 2022, 220: 105142,

[20.] Sun ZX, Song JJ, An YR. Numerical simulation of aerodynamic noise generated by high speed trains. Eng Appl Comput Fluid Mech, 2012, 6(2): 173-185

[21.] Berkooz G, Holmes P, Lumley JL. The proper orthogonal decomposition in the analysis of turbulent flows. Annu Rev Fluid Mech, 1993, 25: 539-575,

Funding

the National Natural Science Foundation of China(52072267))