## 3 Results and Discussion
### Statistical Analysis of Factor Effects on CR  
Figure 3 presents a Pareto chart that graphically confirms the statistical significance of each factor--temperature (A), flow velocity (C), and their interaction (A-C) on the CR. The chart uses standardized effects to show the influence of these factors, with the red dashed line representing the Bonferroni-corrected threshold. Any factor or interaction exceeding this line is considered statistically significant within the 95% confidence interval (\(\alpha\) = 0.05) and must be included in the regression model. The chart illustrates that temperature (A),  
Figure 2: CR values obtained from LPR tests for the factor combinations shown in Table 3.  
flow velocity (C), and their interaction (A\(\cdot\)C) are the most significant factors influencing CR. Other factors such as pH or interactions did not exceed the significance threshold and are therefore not considered important in this study. This indicates that the CR of carbon steel is primarily driven by the interaction between temperature and flow velocity, rather than by the independent effect of each factor alone. ANOVA is a statistical method used to compare two or more groups by examining the variance within and between groups, determined by the total mean and the differences in mean between groups. It facilitates the assessment of each independent variable's significant influence on the corrosion rate, which is essential for validating the effectiveness of the regression model. In ANOVA, the mean of square (MS) is calculated by dividing the sum of squares (SS) by the degrees of freedom (DF). The F-value is defined as the ratio of the MS to the error mean square, and it reflects the influence of a factor within the model. A high F-value indicates a stronger influence on the response variable. The \(p\)-value is the most important characteristic in ANOVA, as it determines whether a factor has a significant effect. At a 95% confidence level (\(\alpha\) = 0.05), a \(p\)-value less than 0.05 indicates that the factor is statistically significant. The ANOVA results (Table 4) showed that the overall regression model is highly significant, with an F-value of 373.81 and \(p\) < 0.001, indicating that the included factors explain a substantial portion of the variability in CR. Among the individual factors, temperature was found to have a significant effect on CR (F = 24.58, \(p\) < 0.001), while flow velocity alone was not significant (F = 0.54, \(p\) = 0.47). However, the interaction between temperature and flow velocity was highly significant (F = 94.89, \(p\) < 0.001), confirming that the effect of temperature on CR is heavily dependent on the flow velocity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **DF** & **Adj. SS** & **Adj. MS** & **F-Value** & \(p\)**-Value** \\ \hline Regression & 3 & 52,479.7 & 17,493.2 & 373.81 & 0.000 \\ Temp. & 1 & 1150.3 & 1150.3 & 24.58 & 0.000 \\ Velocity & 1 & 25.3 & 25.3 & 0.54 & 0.47 \\ Temp \(\times\) Velocity & 1 & 4440.4 & 4440.4 & 94.89 & 0.000 \\ Error & 23 & 1076.3 & 46.8 & & \\ Sum & 26 & 53,556.0 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ANOVA for significant terms in regression model. Figure 3: Pareto chart of the standardized effects for significant factors in 95% confidence level. Figure 4 illustrates the interaction effect between temperature and flow velocity, demonstrating that both factors accelerate the CR when combined. At a flow velocity of 1.6 m/s, the rapid removal of corrosion products from the metal surface enhanced the oxygen supply, which further accelerated corrosion [20]. This effect became particularly pronounced when the temperature reached 40 \({}^{\circ}\)C, accelerating the CR due to the synergistic interaction between high temperature and fast flow velocity. On the other hand, pH did not result in a significant difference in CR as a level change. This suggests that carbon steel exhibits relatively stable corrosion characteristics in seawater at pH levels within the range of 6 to 8.