Identification of Ballast Fouling Status and Mechanized Cleaning Efficiency Using FDTD Method
Abstract
:1. Introduction
- This paper proposes a novel algorithm for generating random irregular polygon (RIP) particles to simulate railway ballast particles, addressing the issue of uniform and unrealistic particle shapes. In addition, an efficient algorithm is presented to generate the ballast layer by collision detection (CD) of a large number of particles, and then the particle size distribution is controlled to simulate the gradation of the ballast. The generated geological models can represent different levels of ballast fouling and the cleaning efficiency of ballast.
- Using the FDTD algorithm in forward simulation, the numerical simulation results accurately determine and identify the differences between the ballast layers with different ballast fouling and the efficiency of mechanized ballast cleaning process by integrating the energy curve, Hilbert transform energy [19,20,21], and S-transform time-frequency analysis [22,23].
- Finally, experiments were conducted on a section of a high-speed railway line in southern China with screened ballast. By comparing the results of the GPR forward simulation and the experimental data, we found that the simulation results were consistent with the measurements, indicating the accuracy and reliability of the proposed model.
- The remainder of this paper is organized as follows. Section 2 presents the materials, basic algorithms, and principles of data analysis used in this study. Section 3 provides an analysis of the simulation results for different levels of fouling and before and after ballast cleaning models. Section 4 describes the experimental content designed to compare the simulation results presented in Section 3. Finally, Section 5 summarizes the main contributions of this study.
2. Materials and Methods
2.1. Materials Preparation and Characterization
2.2. 2D RIP Ballast Modeling Algorithm
2.3. Hilbert Transform Energy
2.4. Time-Frequency Analysis of S-Transform
3. Modeling and Simulation Result Analysis
3.1. Modeling and Simulation Analysis of Ballast Fouling
3.2. Model and Simulation Analysis of Mechanized Ballast Cleaning on Railway Ballast
4. Experimental and Analysis
5. Conclusions
- Based on the proposed method, this study employs the energy integration curve, Hilbert energy transform, and S-transform approaches to evaluate and provide a reasonable analysis of the simulated results on different ballast conditions. It can effectively distinguish the railway ballast with different levels of fouling and mechanized ballast cleaning process. The conclusions of the simulation analysis are consistent with experimental data from a high-speed railway line in southern China, demonstrating the reliability and accuracy of the proposed model.
- As the fouling increases, the finely fragmented particles in the ballast layer tend to be more abundant and the energy on the integration curve also increases. In the Hilbert transform energy, the energy distribution is more concentrated. In the S-transform time-frequency results, the attenuation rate is faster with increasing depth. Conversely, with the clean ballast, the opposite effect is observed.
- This method is proposed can accurately reproduce the railway ballast model with high precision. Furthermore, the proposed research method accurately constructs models of different levels of fouling and mechanized ballast cleaning efficiency of the railway ballast, which has great potential application value in the detection and elimination of hidden dangers on actual railway lines.
- Nevertheless, the ballast layer model proposed in this paper is still a two-dimensional structure compared to the actual three-dimensional model situation there are still some differences. The relative permittivity of the ballast is only considered as a homogeneous and isotropic medium for the simulation and more environmental effects on the fouling of the ballast bed have not been considered. To address these flaws, further studies will expand the model to a three-dimensional structure and take into account the variation of relative permittivity and some natural climatic effects such as rainfall.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
GPR | Ground Penetrating Radar |
FDTD | Finite-Difference Time-Domain |
RIP | Random Irregular Polygons |
CD | Collision Detection |
Appendix A. Relative Dielectric Constant Test Method
Appendix B. Data Processing
- (1)
- Background elimination. The average value of each segment of the traces was subtracted to complete this stage, which simply included removing the background mean (Figure A2b,e).
- (2)
- Automatic control of gain. Radar echoes steadily lose energy as propagation depth increases. In order to increase the energy of radar echoes from deep reflectors, we routinely utilized automated gain control (Figure A3b).
- (3)
- Railway sleeper interference removal. After the background is removed, the sleeper interference intercepts the position of the data row where the sleeper is located to make it 0 (Figure A2c,f).
- (4)
- Data normalization process. Divide all of the variables to be compared by the highest value of these values to normalize the comparison (Figure 9).
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Particle Size | Fouling Level | ||
---|---|---|---|
Clean | Moderately Fouled | Highly Fouled | |
1.0–2.5 mm | 2.2% | 3.8% | 5.3% |
2.5–5.5 mm | 3.1% | 4.9% | 7.0% |
5.0–10.0 mm | 5.2% | 7.3% | 9.8% |
10.0–16.0 mm | 9.3% | 13.7% | 16.9% |
16.0–25.0 mm | 14.9% | 20.0% | 26.6% |
25.0–35.0 mm | 32.0% | 34.7% | 41.1% |
35.0–45.0 mm | 54.2% | 55.8% | 58.3% |
45.0–56.0 mm | 79.0% | 81.3% | 83.1% |
56.0–63.0 mm | 95.1% | 96.0% | 97.5% |
Model Level | Dielectric Constant | Permeability (H/m) | Conductivity (S/m) |
---|---|---|---|
Air layer | 1 | 1 | 1 |
Ballast layer | 6 | 1 | 0.01 |
Sand cushion layer | 12 | 1 | 0.001 |
Subgrade layer | 15 | 1 | 0.01 |
Reference | Modeling Algorithm | Particle Shape | Number of Particles |
---|---|---|---|
[12,13] | random sequential adsorption | circle | many |
[14,15,16] | random sequential adsorption | circle | many |
[16] | discrete element algorithm | Irregular 3D polygon | few |
This paper | RIP & CD algorithm | Irregular 2D polygon | many |
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Li, B.; Peng, Z.; Wang, S.; Guo, L. Identification of Ballast Fouling Status and Mechanized Cleaning Efficiency Using FDTD Method. Remote Sens. 2023, 15, 3437. https://doi.org/10.3390/rs15133437
Li B, Peng Z, Wang S, Guo L. Identification of Ballast Fouling Status and Mechanized Cleaning Efficiency Using FDTD Method. Remote Sensing. 2023; 15(13):3437. https://doi.org/10.3390/rs15133437
Chicago/Turabian StyleLi, Bo, Zhan Peng, Shilei Wang, and Linyan Guo. 2023. "Identification of Ballast Fouling Status and Mechanized Cleaning Efficiency Using FDTD Method" Remote Sensing 15, no. 13: 3437. https://doi.org/10.3390/rs15133437