## 6 Numerical Model Calibration
### Calibration Based on Temperature  
To calibrate the numerical model according to temperature values obtained both experimentally and numerically, the average values from all 27 thermocouples were used throughout the simulation. These average values are presented in Figure 17 (black dashed lines). It is observed that the trends are similar and the general tendency is maintained. The mean error between the two graphs was also calculated, resulting in approximately 17.79%. This value, considering the number of thermocouples, the size of the stand, the duration of the experiment, and the thermal load, is deemed acceptable. The analysis of individual mean errors, shown in Table 2, reveals that out of the 27 thermocouples used, 17 recorded a mean error below 10%, 5 had values between 10% and the overall mean error value (17.79%), and 5 exceeded this threshold. These results indicate a satisfactory agreement between simulated and experimental values for the majority of measurement points, with errors under 3% in some cases. Only a limited number of thermocouples exhibited significant deviations, with errors exceeding 20%. Excluding from the analysis the four thermocouples that exhibited errors over 40% (outliers) would substantially reduce the corrected mean error from 17.79% to 6.9%. This revised mean error value further confirms that the prediction of data obtained from the simulation is acceptable and supports the conclusion drawn from HRR analysis regarding the validity of the numerical model. The corrected averages of the temperatures monitored during the experiment (solid red line) and the simulation (solid green line) are presented in Figure 17.  
Figure 17: Comparison of the corrected average temperatures resulting from the experiment and simulation. The substantial reduction in the mean error underscores the importance of addressing outliers to enhance the alignment between experimental and simulated data. This refinement not only validates the numerical model but also strengthens confidence in its predictive capabilities for future fire behavior simulations under similar conditions. To capture the vertical temperature profile in a consistent and protected location, we instrumented two vertical thermocouple trees, each consisting of five sensors placed at heights of 0.6 m, 1.1 m, 1.8 m, 2.5 m, and 2.9 m, on the left wall (\(X=0.10\) m) of the enclosure. This wall was selected because it had no openings, ensuring uniform exposure to the developing hot gas layer. In contrast, the other walls either had too few sensors or featured non-uniform placements, which made them less suitable for extracting reliable vertical gradients. The results presented in Figure 18 clearly illustrate the expected vertical stratification of temperature, with values increasing consistently with height. This gradient is especially evident in the generalized burning phase, where the upper thermocouples register significantly higher temperatures compared to the lower ones. The numerical model reproduces this stratification well, particularly in the upper layers (2.5 m and 2.9 m), where thermal conditions are more stable and dominated by buoyancy-driven flow. To evaluate the model's predictive accuracy, we focused on two key fire stages: the active burning phase (320-700 s) and the generalized burning phase (700-1280 s). As shown in Figure 18, the model closely matches the experimental data during the active phase, capturing both the vertical temperature increase and maintaining low Mean Relative Errors across all sensor heights. In the generalized burning phase, discrepancies become more evident, particularly at lower heights (0.6 m and 1.1 m), where the model tends to underestimate temperatures--likely due to complex mixing and descending cooler air near the floor. However, in the upper layers (2.5 m and 2.9 m), the model continues to show good agreement with measured values, reflecting a reliable prediction of the hot gas layer behavior.