## 3 Results and Discussion
### Discussion  
Zhendao 18 rice was selected as the cleaning plant in this paper. The key areas of the flow field in the cleaning chamber were divided, and the airflow analysis within the regions was carried out based on the knowledge base and the post-processing reasoning mechanism. Finally, the airflow distribution characteristics of the six regions in the cleaning chamber were obtained. The parameters of Zhendao 18 rice in the knowledge base are as follows: the minimum and maximum values of plump grain suspension velocity are 6.6 m/s and 10.1 m/s, the minimum and maximum values of shrunken grain suspension velocity are 1.5 m/s and 2 m/s, the minimum and maximum values of short stem suspension velocity are 2.3 m/s and 5.7 m/s, the minimum and maximum values of long stem suspension velocity are 5.2 m/s and 7.5 m/s, and the minimum and maximum values of residues suspensions velocity are 1.2 m/s and 2.9 m/s, respectively. The reasoning mechanism based on rules and examples was used to judge the wind velocity and analyze result data in the cleaning chamber, as it is shown below. For the analysis of the velocity field dataset near the z = \(-\)0.05 mm cross-section, it can be seen from the result file data named fluent3_9_all.csv that above the upper-sieve surface, the estimated wind velocity before the sieve is 4.8580 m/s, and the wind velocity is lower. The estimated wind velocity in the sieve is 2.3251 m/s, and the wind velocity is lower. The wind velocity assessed at the sieve tail is 4.0239 m/s, and the wind velocity is higher. The wind velocity before the sieve is greater than the wind velocity in the sieve, the wind velocity in the sieve is lower than the wind velocity after the sieve, and the wind velocity before the sieve is less than 1.5 times the wind velocity in the sieve. The estimated wind velocity of the flow under the lower-sieve surface is 6.0693 m/s, and the wind velocity does not affect the crop particles falling into the grain collection area. In summary, the airflow distribution on the upper-sieve surface is reasonable. The wind velocity at the front of the upper sieve is lower, which can easily cause low cleaning efficiency. The wind velocity in the middle of the upper sieve is lower, which can easily cause high impurity content. The wind velocity at the back of the upper sieve is higher, which can easily cause an increase in the cleaning loss rate. There exists eddy current in the area near the inlet of the cleaning chamber. The estimated regional wind speed is about 0.95 m/s, which is not conducive to material transfer. There exists a hidden danger that materials may be affected by the eddy current, which causes accumulation and reduces the cleaning efficiency; considering that the regional wind velocity and eddy current are small, the hidden danger can be ignored as appropriate. In the area near the grain collection site in the cleaning chamber, there does not exist eddy current, and the regional assessed wind speed is about 1.1 m/s, which does not affect the grain collection work. [PERSON] obtained the distribution of air velocity in the cleaning chamber on the 50 mm plane under the sieve surface through the analyses of simulation and test results [4]. As it is shown, the distribution of air velocity in the cleaning chamber is more uniform at the \begin{table}
\begin{tabular}{c c c c} \hline  & **Model Identification** & **Manual Marking** & **Error** \\  & **Coordinates (m)** & **Coordinates (m)** & **Error** \\ \hline
1 & (0.12557, 0.41514) & (0.12673, 0.41875) & (0.92\%, 0.86\%) \\
2 & (0.95780, 0.41309) & (0.96078, 0.41875) & (0.31\%, 1.35\%) \\
3 & (0.12993, 0.35823) & (0.12988, 0.35041) & (0.04\%, 2.23\%) \\
4 & (0.96176, 0.35908) & (0.98225, 0.35041) & (2.08\%, 2.47\%) \\ \hline \end{tabular} Note: The coordinate origin of model recognition is consistent with manual marking coordinates, that is, the coordinate origin of the model (the coordinate origin is in the lower-left corner of the model).  
\end{table}