## 3 Results and Discussion
### Evaluation Under Simulated Operating Conditions  
The model undergoes practical validation through tests simulating real-world conditions in cooling seawater pipes within a ship engine room. These simulations account for dynamic factors such as engine load, regional sea surface temperature, and energy-saving operations, reflecting the complex environment in which corrosion monitoring is required. The test protocol followed the same procedure described in Section 2.3. The simulation consisted of 15 sequential steps, with temperature and flow velocity adjusted according to the factor levels used in this study. Figure 9 illustrates the progression \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Temp** & **Flow Velocity** & **CR\({}_{\text{model}}\) (PI), MPY** & **CR\({}_{\text{com}}\), MPY** & **CR\({}_{\text{Pstat}}\), MPY** \\ \hline Test 1 & 15 & 0.4 & 48 (33–63) & 38 & 45 \\ Test 2 & 30 & 1.2 & 119 (104–133) & 127 & 131 \\ Test 3 & 20 & 1.6 & 104 (89–119) & 113 & 102 \\ Test 4 & 35 & 0.27 & 73 (58–88) & 65 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of predicted CR with experimental results from commercial LPR sensor and potentiostat for validation. of CR predictions over time as these operating conditions change. Test coupons were used to measure actual metal loss at five major points throughout the simulation, providing a benchmark for validating the model's accuracy. The detailed results for each stage are presented in Table 6.  
The data in Table 6 confirm that the model's predictions closely correspond to the measured CR from the test coupons. For instance, at Step 10, the predicted CR was 112 MPY, while the actual measurement was 110, indicating strong agreement. However, at lower flow velocities, such as in Step 12, the model showed a slight overestimation (89 predicted vs. 85 measured). This pattern suggests that the model may benefit from additional data at intermediate factor levels to identify non-linear trends more accurately. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Step**} & \multicolumn{3}{c}{**Factor**} & \multirow{2}{*}{**CR\({}_{\text{comm}}\), MPY**} & \multirow{2}{*}{**CR\({}_{\text{model}}\), MPY**} \\ \cline{2-2} \cline{5-5}  & **Temperature**, \({}^{\circ}\)**C** & & & **Flow Velocity, m/s** \\ \hline
1 & 25 & 1 & 65 \(\pm\) 1 & 93 \\
2 & 25 & 0.5 & 46 \(\pm\) 7 & 70 \\
3 & 35 & 1.3 & 141 \(\pm\) 5 & 140 \\
4 & 30 & 0.7 & 70 \(\pm\) 8 & 90 \\
5 & 20 & 1.2 & 78 \(\pm\) 5 & 88 \\
6 & 10 & 1.2 & 50 \(\pm\) 7 & 57 \\
7 & 25 & 1 & 79 \(\pm\) 4 & 93 \\
8 & 25 & 0.4 & 56 \(\pm\) 7 & 65 \\
9 & 25 & 1.3 & 84 \(\pm\) 8 & 108 \\
10 & 35 & 1.3 & 124 \(\pm\) 8 & 140 \\
11 & 10 & 1.3 & 57 \(\pm\) 11 & 59 \\
12 & 15 & 1.6 & 71 \(\pm\) 5 & 85 \\
13 & 35 & 1 & 89 \(\pm\) 16 & 120 \\
14 & 20 & 1.5 & 87 \(\pm\) 3 & 99 \\
15 & 35 & 1.6 & 170 \(\pm\) 17 & 159 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Scenario-based comparison of CR predictions from the regression model and measurements from the commercial LPR sensor. Factor combinations used in the scenario were excluded from the training data. Figure 9: CR predictions and measurements across sequential operating conditions with varying temperature and flow velocity. In Step 15, the CR increased steadily over time, but this rise was not due to corrosion acceleration. Instead, it resulted from probe contamination caused by prolonged exposure to the harsh environment. This observation emphasizes the necessity of considering sensor fouling in long-term corrosion monitoring, as contamination can lead to gradual shifts in MPY readings and affect accuracy [10; 11]. Figure 10 shows the cumulative general corrosion depth over the simulation period, derived from the CR predictions. The results demonstrate that the model effectively tracks the trends in CR under varying conditions.