## 4 Results of Flow Characteristics Analysis  
### Experimental Results Using the Flow Visualization Method  
The results of the flow information extracted using the laser flow visualization method are described below. This study defines the main area of 9 \(\times\) 16 size as A(i,j). Figure 6 shows the partition area for extracting flow information. The red square is partition area A(4,5). Then, each main area A(i,j) is divided into sub-areas of 9 \(\times\) 9 size as a(i,j). Figure 7 shows the divided sub-area. In sub-area a(i,j), RGB values are extracted from nine white dots, as shown in Figure 7. A green laser was used to extract the G-value from RGB data. A total of 729 G-values were extracted from A(4,6) and 26,244 G-values were extracted from the flow field to be analyzed. Figure 5: Schematic of the RGB color model. Figure 6: Flow information of the laser cross section and position of A(4,6) (red square) through flow visualization experiment: (**a**) the first image of the flow visualization experiment video; (**b**) the second image of the flow visualization experiment video. The G-value from the output image was extracted with the maximum G-value among the extracted G-values, defined as a flow object. Figure 8 shows the distribution of G-values in the partition area A(4,6). A higher G-value means brighter green. About 61% of the G-values are between 7 and 11, with a maximum value of 30. Next, the contrast and background noise of the image were removed, and a flow object was selected. A flow object is a region with the highest G-value. Therefore, in this study, the range of Gpeak values was set to the top 0.5%. Figure 9 shows the flow object at A(4,6) using the G-value. The location of each Gpeak is indicated by a red circle. In the first image (a), the flow objects are defined as Gpeak,1-1 and Gpeak,2-1, while in the second image (b), the flow objects are defined as Gpeak,1-2, and Gpeak,2-2, respectively. Image (a) shows 2 points where the G-value is included in the top  
Figure 8: Distribution bar graph of the G-value in A(4,6). Figure 7: Additional partition image and G-value extraction point at A(4,6): (a) the first image of the flow visualization experiment video; (b) the second image of the flow visualization experiment video. 0.5%, while in image (b), the two flow objects move to the next position, making it necessary to track the movement of two flow objects. Table 2 shows the location of G\({}_{\text{peak}}\) at A(4,6). The G\({}_{\text{peak,1-1}}\) of the first flow object is 30. G\({}_{\text{peak,1-2}}\) in the next image is 27. The second flow object has G\({}_{\text{peak,2-1}}\) to be 27, and G\({}_{\text{peak,2-2}}\) as 26. The maximum value changed is 3 as the flow object moves to the next image. Each G\({}_{\text{peak}}\) point is the internal point of the flow object. The frame unit changes as the internal point moves. Therefore, the G\({}_{\text{peak}}\) point can be defined as the movement of the flow object. In this paper, the internal point of the flow object is defined as Obj\({}_{\text{n-m}}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Flow Object (Obj\({}_{\text{n-m}}\))** & **X-Y Coordinates** & **G\({}_{\text{peak}}\) Value** & **Coordinates Image** \\ \hline \hline Obj\({}_{\text{1-1}}\) & (2.159, \(-\)5.305) & 30 & \\ \hline \hline Obj\({}_{\text{1-2}}\) & (5.359, \(-\)2.584) & 27 & \\ \hline Obj\({}_{\text{2-1}}\) & (3.008, \(-\)7.649) & 27 & \\ \hline \hline Obj\({}_{\text{2-2}}\) & (6.497, \(-\)4.899) & 26 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Flow object location information and G-value. Figure 9: Three-dimensional graph describing the location and movement of the G\({}_{\text{peak}}\) in A(4,6): (a) G\({}_{\text{peak}}\) location information in the first image; (b) G\({}_{\text{peak}}\) location information in the second image. \(\rm G_{peak}\) relatively accurately. As a result, the error rate is low because flow information for tracking is abundant. Next, the results in the recirculation area are shown in (b).