# Parameter Calibration of Discrete Element Model of Wine Lees Particles
## 3 Results and Discussion
### PB Experimental Design
#### 3.1.2 Analysis of PB Test Results  
In this study, Design-Expert was utilized to conduct an ANOVA analysis of the PB test results (refer to Table 5). The correlation coefficients of the PB test indicate that the model can account for 93.3% of the experimental variance, as evidenced by a coefficient of determination R\({}^{2}\) value of 0.9330, demonstrating a high degree of fit with the actual data. Additionally, R\({}^{2}\)adj = 0.8158, which is in close proximity to R\({}^{2}\), indicating a strong correlation; and the coefficient of variation (CV) is relatively small at 9.8%, suggesting greater precision at 7.7831. In conclusion, these findings suggest that the model exhibits high reliability and can effectively elucidate the impact of factors on response values.  
Based on the findings presented in Table 5 and Figure 10, it is evident that both the particle-particle recovery coefficient and the particle-steel plate recovery coefficient have a significant impact on the angle of repose of the lees particles. In contrast, the other contact parameters exhibit a smaller influence. Notably, the particle-particle recovery coefficient exerts a greater effect on the angle of repose compared to the particle-steel plate recovery coefficient. This can be attributed to the fact that, during the stacking process of bulk materials, only the bottom contact particles interact with the steel plate, whereas there are more interactions among lees particles.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**No.** & \(\mathbf{X_{0}}\) & \(\mathbf{X_{1}}\) & \(\mathbf{V_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & \(\mathbf{V_{2}}\) & \(\mathbf{X_{4}}\) & \(\mathbf{X_{5}}\) & \(\mathbf{V_{3}}\) & \(\mathbf{X_{6}}\) & \(\mathbf{V_{4}}\) & **Resting Angle\({}^{c}\)** \\ \hline
1 & 0.1 & 0.4 & \(-1\) & 1 & 0.02 & 1 & 0.8 & 0.5 & \(-1\) & 0.01 & 1 & 27.0197 \\
2 & 0.05 & 0.4 & \(-1\) & 0.5 & 0.02 & 1 & 0.4 & 1 & 1 & 0.02 & \(1\) & 28.5799 \\
3 & 0.1 & 0.4 & 1 & 1 & 0.02 & \(-1\) & 0.4 & 1 & \(-1\) & 0.02 & \(-1\) & 28.679 \\
4 & 0.05 & 0.8 & \(-1\) & 1 & 0.01 & \(-1\) & 0.8 & 1 & \(-1\) & 0.02 & \(1\) & 13.5499 \\
5 & 0.1 & 0.8 & \(-1\) & 0.5 & 0.02 & \(-1\) & 0.8 & 1 & 1 & 0.01 & \(-1\) & 14.7664 \\
6 & 0.1 & 0.4 & 1 & 0.5 & 0.01 & \(-1\) & 0.8 & 0.5 & 1 & 0.02 & \(1\) & 25.4851 \\
7 & 0.05 & 0.4 & \(-1\) & 0.5 & 0.01 & \(-1\) & 0.4 & 0.5 & \(-1\) & 0.01 & \(-1\) & 27.2393 \\
8 & 0.05 & 0.8 & 1 & 1 & 0.02 & \(-1\) & 0.4 & 0.5 & 1 & 0.01 & \(1\) & 22.2007 \\
9 & 0.05 & 0.8 & 1 & 0.5 & 0.02 & 1 & 0.8 & 0.5 & \(-1\) & 0.02 & \(-1\) & 17.5636 \\
10 & 0.05 & 0.4 & 1 & 1 & 0.01 & 1 & 0.8 & 1 & 1 & 0.01 & \(-1\) & 25.9859 \\
11 & 0.1 & 0.8 & \(-1\) & 1 & 0.01 & 1 & 0.4 & 0.5 & 1 & 0.02 & \(-1\) & 21.5922 \\
12 & 0.1 & 0.8 & 1 & 0.5 & 0.01 & 1 & 0.4 & 1 & \(-1\) & 0.01 & 1 & 22.6013 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PB test design and results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Effect** & \begin{tabular}{c} **Sum of Mean** \\ **Squares** \\ \end{tabular} & \begin{tabular}{c} **Impact** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{c} **F** \\ \end{tabular} &
\begin{tabular}{c} **\(\mathbf{p}\)** \\ **Ranking** \\ \end{tabular} \\ \hline \(X_{0}\) & 0.84 & 2.10 & 0.70 & 0.42 & 0.55 & 4 \\ \(X_{1}\) & \(-8.45\) & 214.33 & 70.97 & 42.38 & 0.00 & 1 \\ \(X_{2}\) & 0.47 & 0.65 & 0.22 & 0.13 & 0.74 & 6 \\ \(X_{3}\) & 0.39 & 0.46 & 0.15 & 0.09 & 0.78 & 7 \\ \(X_{4}\) & \(-4.42\) & 58.62 & 19.41 & 11.59 & 0.03 & 2 \\ \(X_{5}\) & \(-1.16\) & 4.01 & 1.33 & 0.79 & 0.42 &