# Experimental and Numerical Investigation on Ballast Flight from Perspective of Individual Particles
## 2 Materials and Methods  
### Accurate Capture, Reconstruction, and Analysis of Ballast Features  
The granular particles in the ballast bed have different sizes and shapes, which have a significant influence on the dynamic characteristics of the ballast bed [30]. The image recognition technology for obtaining particle features has been widely used in civil engineering in recent years [31; 32; 33]. Nevertheless, the previous research paid more attention to the mechanical properties of the ballast aggregate based on the image recognition method, which ignored the local morphological characteristics of particles, because the local features of individual particles had little effect on the mechanical property of the ballast aggregate. The traditional approach of image recognition is relatively time consuming as well. This paper aims to investigate the aerodynamic characteristics of individual ballast particles under wind load, which were influenced by the local particle features significantly. The 3D scanning platform was set up to capture the ballast features rapidly based on our previous research [34] in detail, as shown in Figure 1. It can capture the morphological particle characteristics such as the shape, size,angularity, and surface texture. Information on the ballast features was obtained in the format of a Standard Template Library (STL) by scanning a particle from double views.  
Five hundred ballast particles with different sizes and shapes were selected randomly from a high-speed railway line, and their profiles were recorded by the 3D scanning system. An in-house program was developed using the graphic analysis algorithm to identify and extract the dimensions of the long axis, middle axis, and short axis of the ballast by obtaining the outline projection with three particle views. Then, the shape parameters such as the volume, surface area, acicular index, and flakiness index were calculated automatically based on MATLAB. The method of the accurate capture, reconstruction, and analysis of ballast features provides a technical support to analyze the movement characteristics of ballast particles in a wind tunnel experiment and to formulate an accurate CFD numerical simulation models.  
The classification of particle shape was based on the relationship between the dimensions of the ballast axis, as listed in Table 1, proposed by [PERSON] [35], [PERSON], and [PERSON] [36], and most widespread around the world. The 500 ballast samples were divided into four groups based on the approach. Group A comprised the ballast particles with the spherical shape, while Group B was ellipsoid, Group C was oblate, and Group D was prolate. Then, 18 ballast particles were selected and numbered in each group according to the mass. The distribution gradient of particle mass in each group was similar; in other words, the mass of the ballast with the same number was similar between groups. For example, the particle mass of A15, B15, C15, and D15 was all about 150 g.  
Figure 2 shows the ballast samples with the serial numbers of the four groups. The information of ballast features was calculated to form a database based on the automatic analysis algorithm. The four representative ballast samples are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Shape** & **Classification Standard** \\ \hline A & Spherical & \(\
icefrac{{b}}{{a}}>2/3\), \(c/b>2/3\) \\ B & Ellipsoid & \(\
icefrac{{b}}{{a}}<2/3\), \(c/b>2/3\) \\ C & Oblate & \(\
icefrac{{b}}{{a}}>2/3\), \(c/b<2/3\) \\ D & Prolate & \(\
icefrac{{b}}{{a}}<2/3\), \(c/b<2/3\) \\ \hline \hline \end{tabular} a, b, and c represent the long axis, middle axis, and short axis dimensions of the ballast particles, respectively.  
\end{table}
Table 1: Grouping scheme of ballast shape.  
Figure 1: Three-dimensional (3D) scanning platform of ballast particles.