## 3 Experimental Results and Analysis
### Results of Full Feature Modeling  
Figure 4 presents the predicted heat transfer coefficients from different models under two channel shapes. Figure 4a shows the performance of different models in the straight channel modeling. By comparing the RMSE, MAE, and R\({}^{2}\) metrics, the following conclusions can be drawn: In the straight channel modeling, the RMSE values of the RF and SVM models are relatively high, at 1305.83 and 1490.82, respectively, indicating that these two models have relatively large prediction errors. The ANN model has the lowest RMSE value, at 1259.36, demonstrating better prediction accuracy. In the straight channel modeling, the ANN model has the highest R\({}^{2}\) value, at 0.891799, indicating that this model \begin{table}
\begin{tabular}{c c c c c c} \hline
**Flow Channel** & **The Number of** & & & & \\
**Shape** & **Hidden Layers** & **Proportion** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline \multirow{8}{*}{Straight-through flow channel} & & 8:2 & 0.811366 (\(\pm\)0.023) & 1684.46 & 220.96 \\  & 1 & 7:3 & 0.888352 (\(\pm\)0.015) & 1279.26 & 212.50 \\  & & 6:4 & 0.872762 (\(\pm\)0.018) & 1334.39 & 206.80 \\  & & 8:2 & 0.833303 (\(\pm\)0.021) & 1583.49 & 134.61 \\  & 2 & 7:3 & 0.888936 (\(\pm\)0.016) & 1275.91 & 123.90 \\  & & 6:4 & 0.880276 (\(\pm\)0.017) & 1294.39 & 100.16 \\  & & 8:2 & 0.825920 (\(\pm\)0.022) & 1618.17 & 140.13 \\  & 3 & 7:3 & 0.891799 (\(\pm\)0.014) & 1259.36 & 128.88 \\  & & 6:4 & 0.877902 (\(\pm\)0.019) & 1307.16 & 95.89 \\  & & 8:2 & 0.971331 (\(\pm\)0.012) & 177.025 & 99.994 \\  & 1 & 7:3 & 0.972071 (\(\pm\)0.011) & 190.571 & 115.218 \\  & & 6:4 & 0.965544 (\(\pm\)0.013) & 225.748 & 119.630 \\  & & 8:2 & 0.992383 (\(\pm\)0.008) & 91.245 & 36.105 \\   Hexagonal flow channel & 2 & 7:3 & 0.995199 (\(\pm\)0.007) & 79.016 & 36.672 \\  & & 6:4 & 0.992690 (\(\pm\)0.009) & 103.981 & 43.366 \\  & & 8:2 & 0.993313 (\(\pm\)0.008) & 93.246 & 41.798 \\  & 3 & 7:3 & 0.994084 (\(\pm\)0.007) & 80.419 & 35.863 \\  & & 6:4 & 0.991977 (\(\pm\)0.009) & 108.933 & 47.613 \\ \hline \end{tabular}
\end{table} 
Table 2: Heat transfer coefficient prediction results of the artificial neural network under different partitioning conditions (8:2, 7:3, 6:4) and different network layer numbers (1, 2, 3). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Flow Channel** & **Feature Selection** & **Model** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline \multirow{8}{*}{Straight-through flow channel} & & RF & 0.878151 (\(\pm\)0.015) & 1305.83 & 150.24 \\  & & SVM & 0.841181 (\(\pm\)0.020) & 1490.82 & 118.37 \\  & None & CATBOOST & 0.857127 (\(\pm\)0.018) & 1426.64 & 135.54 \\  & & XGBOOST & 0.878457 (\(\pm\)0.016) & 1304.19 & 148.45 \\  & & ANN & 0.891799 (\(\pm\)0.014) & 1259.36 & 128.88 \\  & & RF & 0.880556 (\(\pm\)0.017) & 1292.86 & 162.91 \\  & & SVM & 0.857282 (\(\pm\)0.019) & 1413.23 & 123.74 \\  & & CATBOOST & 0.875124 (\(\pm\)0.016) & 1292.53 & 117.41 \\  & & XGBOOST & 0.880421 (\(\pm\)0.015) & 1293.61 & 187.34 \\  & & ANN & 0.882849 (\(\pm\)0.014) & 1280.41 & 144.92 \\  & & RF & 0.983842 (\(\pm\)0.008) & 144.95 & 62.19 \\  & & SVM & 0.988545 (\(\pm\)0.007) & 122.05 & 35.28 \\  & & CATBOOST & 0.991751 (\(\pm\)0.006) & 114.13 & 49.47 \\  & & XGBOOST & 0.975712 (\(\pm\)0.009) & 177.72 & 71.64 \\ Hexagonal flow channel & & ANN & 0.995199 (\(\pm\)0.007) & 79.02 & 36.67 \\  & & RF & 0.985837 (\(\pm\)0.008) & 135.71 & 58.59 \\  & & SVM & 0.990887 (\(\pm\)0.007) & 108.86 & 82.31 \\  & & CATBOOST & 0.992613 (\(\pm\)0.006) & 98.12 & 51.55 \\  & & XGBOOST & 0.966728 (\(\pm\)0.010) & 208.04 & 95.96 \\  & & ANN & 0.991593 (\(\pm\)0.007) & 104.55 & 53.81 \\ \hline \end{tabular}
\end{table} 
Table 3: The modeling results of two different flow channel models before and after the optimization feature. has the best data fitting degree. In contrast, the R\({}^{2}\) value of the SVM model is the lowest, at 0.841181, showing relatively poor fitting performance.