## 3 Results and Analysis
### Predictive Performance of Fire Models for Physical Quantities  
Figure 7 shows the predicted smoke concentrations from both FDS and CFAST, corresponding to the simulations performed on a compartmentalized area including fire and non-fire spaces (Comp.). The accuracy of the predictions was evaluated using a slow fire. As indicated by Figure 7a, the smoke concentration predictions based on distance differed between the two fire models. It can be seen that as time passes, the smoke concentration predicted by CFAST appears to overpredict compared to the results from FDS. However, in this study, we are interested in the smoke concentration predicted at the beginning of the fire from the perspective of validating the prediction performance of smoke detectors activated at the beginning of the fire. The calculated smoke concentration at the moment of smoke detector activation is 0.16 m\({}^{-1}\), and the smoke concentration at the beginning of the fire is shown in Figure 7b. The results show that the FDS tends to underpredict in the early stages of the fire, but the range of smoke concentrations at which the detectors trigger is very similar. This difference is attributed to the energy and mass conservation principle--particularly in cases where hallways are partitioned, causing mass transfer between adjacent compartments. However, it is necessary to perform additional analysis by applying various configurations of the computational areas before interpreting these results as signs of superior predictive capability in engineering applications. The raw data for the gas temperature and velocity predicted at the locations of the heat detectors are presented in Figure 8 as a physical quantity applied to predict the activation time of the heat detectors. This is the result of a computational domain (Comp.+5W) where the fire room and corridor geometry is set as a single space, as the flow rate is an essential consideration in the rule of thumb for predicting smoke detector activation times in CFAST. As shown in Figure 8a, CFAST significantly overpredicted the temperature near the fire compared with FDS. However, as the distance from the fire increased, the temperature predictions of FDS and CFAST became remarkably similar. Figure 8b shows the predicted velocities at the heat detector locations at an interval of 2.4 m starting from a distance of 3.0 m from the fire centerline. Overall, FDS predicted higher velocities than CFAST. In this context, the CFAST empirical correlation for predicting velocities only provides a prediction for \(r/H\) below 4.0. Here, the distances of 10.2 and 12.6 m from the fire exceeded the \(r/H\) of 4.0; nonetheless, the velocities were appropriately predicted using this methodology. Figure 6: Comparison of the activation times of smoke and heat detectors according to the design of the computational domain in CFAST. Figure 9 presents the results for the gas temperatures and velocities predicted by FDS and CFAST at the locations of the heat detectors. As shown in Figure 9a, except for the locations close to the fire source, where the predicted temperatures of CFAST were relatively high, most of the predictions aligned closely with the FDS results. In addition, as shown in Figure 9b, CFAST tended to underestimate the velocities. These results indicate the limitations of CFAST in predicting velocities using empirical correlations compared with FDS, which divides the physical conditions of each cell in a three-dimensional grid as functions of time with respect to mass, momentum, and energy. Furthermore, the results at 10.2 and 12.6 m from the fire in CFAST suggest that the interpretation of velocities using the same empirical correlation remains unchanged even when the distance (\(r/H\)) from the fire exceeds the applied range of 4.0. Figure 8: Comparison of temperatures and velocities over time at the detector location predicted by FDS and CFAST. Figure 7: Comparison of smoke densities at the detector location predicted using FDS and CFAST.