Prediction Model for the Environmental Noise Distribution of High-Speed Maglev Trains Using a Segmented Line Source Approach
Abstract
:Featured Application
Abstract
1. Introduction
2. Analysis of High-Speed Maglev Noise Source Distribution
2.1. Numerical Simulation Model of the Flow Field
2.2. On-Track Experiment
2.3. Analysis of Simulation and Experimental Results
3. Construction of the Noise Prediction Model
3.1. Finite-Length Incoherent Moving Line Source Model
3.2. Equivalent Source Strength
3.3. Prediction Model and Parameters
4. Model Verification and Noise Prediction
5. Conclusions
- (1)
- Flow Field Analysis: Incorporating air compressibility effects, the LES method was employed to conduct numerical simulations of the external flow field surrounding a Shanghai Maglev train. The analysis revealed distinct three-dimensional vortical structure distributions with regions of elevated vorticity magnitude concentrated at the vehicle surface and wake zone. Flow separation was observed at the streamlined shoulder of the trailing car, whereas the wake region exhibited a complex array of multiscale vortices with varying intensities, dominated by a pair of counter-rotating ribbon-like trailing vortices. The vortex was the main sound source of fluid flow, and the noise energy was mainly concentrated in the turbulent movement area, such as near the surface of the vehicle body and the wake area. Therefore, the five parts of the turbulent movement around the train were taken as the five-segment line sources.
- (2)
- Model Validation: The results of a five-segment UFL-IMLS equivalent model derived from the flow simulation data demonstrated strong agreement with the on-site measurements. The maximum absolute deviations in the equivalent sound levels across the monitoring points were constrained to 1.3 dB(A), satisfying the requirements of engineering predictions.
- (3)
- High-Speed Extrapolation: The validated predictions were extended to 600 km/h operation, generating lateral attenuation curves of equivalent continuous A-weighted SPLs and time-domain profiles and providing critical reference information for ultra-high-speed Maglev noise assessment. The time-domain curves of each velocity had similar trends, and the time-domain curve peaks became sharper with an increase in velocity. The curve trends of equivalent continuous A-weighted SPLs with the lateral distance were the same at each speed, and at 600 km/h, the equivalent continuous A-weighted SPLs decreased by 11.8 dB as the logarithm of the lateral distance doubles.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
LES | Large eddy simulation |
SEL | Sound exposure level |
SPL | Sound pressure level |
UFL-IMLS | Uniform finite-length incoherent moving line source |
SST | Shear stress transport |
SIMPLE | Semi-implicit pressure-linked equations |
WALE | Wall-adapting local eddy |
FFT | Fast Fourier transform |
CPB | Constant percentage bandwidth |
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Train Operating Speed (km/h) | Lw1 | Lw2 | Lw3 | Lw4 | Lw5 |
---|---|---|---|---|---|
235 | 76.3 | 108.2 | 110.1 | 111.5 | 100.5 |
300 | 83.6 | 110.8 | 114.7 | 115.3 | 107.3 |
430 | 94.6 | 115.4 | 120.9 | 120.8 | 118.2 |
Train Operating Speed (km/h) | Measured LAeq,E | Predicted LAeq,P | LAeq Error |Δ| |
235 | 87.6 | 87.9 | 0.2 |
300 | 91.8 | 92.2 | 0.4 |
430 | 98.1 | 98.6 | 0.5 |
Temperature (°C) | Relative Humidity (%) | Octave-Band Center Frequency (Hz) | |||||
---|---|---|---|---|---|---|---|
125 | 250 | 500 | 1000 | 2000 | 4000 | ||
25 | 50 | 3.99 × 10−4 | 1.32 × 10−3 | 3.23 × 10−3 | 5.68 × 10−3 | 1.02 × 10−2 | 2.57 × 10−2 |
25 | 60 | 3.40 × 10−4 | 1.18 × 10−3 | 3.18 × 10−3 | 5.96 × 10−3 | 1.02 × 10−2 | 2.32 × 10−2 |
25 | 70 | 2.96 × 10−4 | 1.06 × 10−3 | 3.08 × 10−3 | 6.19 × 10−3 | 1.04 × 10−2 | 2.19 × 10−2 |
35 | 60 | 2.57 × 10−4 | 9.77 × 10−4 | 3.32 × 10−3 | 8.45 × 10−3 | 1.51 × 10−2 | 2.58 × 10−2 |
15 | 60 | 4.26 × 10−4 | 1.18 × 10−3 | 2.31 × 10−3 | 4.06 × 10−3 | 9.5 × 10−3 | 3.03 × 10−2 |
0 | 60 | 4.01 × 10−4 | 7.79 × 10−4 | 1.78 × 10−3 | 5.50 × 10−3 | 1.93 × 10−2 | 6.33 × 10−2 |
–10 | 60 | 3.60 × 10−4 | 9.69 × 10−4 | 3.23 × 10−3 | 1.09 × 10−2 | 2.96 × 10−2 | 5.35 × 10−2 |
rr/rd | ≈1 | ≈1.4 | ≈2 | >2.5 |
Cg,r,i | 3 | 2 | 1 | 0 |
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Cheng, S.; Ge, J.; Ju, L.; Chen, Y. Prediction Model for the Environmental Noise Distribution of High-Speed Maglev Trains Using a Segmented Line Source Approach. Appl. Sci. 2025, 15, 4184. https://doi.org/10.3390/app15084184
Cheng S, Ge J, Ju L, Chen Y. Prediction Model for the Environmental Noise Distribution of High-Speed Maglev Trains Using a Segmented Line Source Approach. Applied Sciences. 2025; 15(8):4184. https://doi.org/10.3390/app15084184
Chicago/Turabian StyleCheng, Shiquan, Jianmin Ge, Longhua Ju, and Yuhao Chen. 2025. "Prediction Model for the Environmental Noise Distribution of High-Speed Maglev Trains Using a Segmented Line Source Approach" Applied Sciences 15, no. 8: 4184. https://doi.org/10.3390/app15084184
APA StyleCheng, S., Ge, J., Ju, L., & Chen, Y. (2025). Prediction Model for the Environmental Noise Distribution of High-Speed Maglev Trains Using a Segmented Line Source Approach. Applied Sciences, 15(8), 4184. https://doi.org/10.3390/app15084184