Influence of Seasonal HVAC Operation on Fire Detection System Performance in Summer and Winter
Article information
Abstract
This study investigated the response characteristics of heat and smoke detectors using fire detection system (FDS) simulations, focusing on the effects of a ceiling-mounted heating, ventilation, and air conditioning (HVAC) system under different seasonal airflow conditions. The compartment dimensions were set to 2.0 × 3.2 × 2.0 m3, and a ceiling-mounted HVAC unit with four-directional air supply was installed at the center of the ceiling. A 0.5 × 0.5 m2 fire source was located at the corner farthest from the entrance. Simulations were conducted for a total of six conditions, combining three airflow rates: maximum at 30.0 m3/min, medium at 25.0 m3/min, and minimum at 20.0 m3/min, under two seasonal conditions: winter with a temperature of 0.8℃ and relative humidity of 58.5%, and summer with a temperature of 28.5℃ and relative humidity of 74.0%. The results showed that airflow rate significantly influences the detection response time of heat detectors, with larger variations observed in the summer than in the winter. In contrast, the influence on smoke detectors was negligible. Under high airflow conditions, the supplied air from the HVAC system prevented combustion products from reaching the vicinity of the detector, leading to increased fire detection time. These findings indicate that in areas adjacent to HVAC systems, smoke detectors are more effective than heat detectors in early fire detection.
1. Introduction
In modern society, heating, ventilation, and air conditioning (HVAC) systems have become essential equipment for maintaining a comfortable indoor environment. A strong supply of airflow and seasonal cooling and heating operations can alter airflow patterns near the ceiling, potentially affecting the transport behavior of heat and smoke, and consequently, the performance of fire detectors and sprinkler systems. In particular, fire detectors and sprinkler heads mounted on the ceiling play a critical role in early fire response. Interference from HVAC-induced airflow can therefore significantly impact fire safety [1,2].
Airflow generated by air conditioning systems can disturb smoke flow and thermal plumes produced during a fire, as reported by Choi and Lee [1], thereby affecting the response characteristics of fire detectors. Article 7 of the National Fire Safety Code for Automatic Fire Detection Systems and Visual Alarm Devices (NFPC 203) [3] stipulates that detectors, except for differential distributed types, must be installed at least 1.5 m away from indoor air inlets. However, air conditioners are not classified as indoor air inlets under this regulation. Choi [4] suggested that ceiling-mounted HVAC airflow can collide with ceiling jet flows generated by a fire, leading to dilution of combustion products and changes in their velocity, direction, and temperature. This interaction can delay the response time of fire detectors and cause potential safety issues in indoor spaces. Accordingly, the study analyzed the influence of ceiling HVAC airflow on fire detector response. Numerous studies have been conducted on the interaction between fire detectors and HVAC airflow, including investigations into the influence of air conditioning systems on fire detectors [1], experimental and numerical studies on the device characteristics of smoke and heat detectors [5], and analyses of detector response characteristics according to combustible material type [6]. However, studies comparing the response characteristics of heat and smoke detectors under ceiling HVAC airflow conditions across different seasons remain limited. In this study, the fire detection system (FDS) performance of heat and smoke detectors in a compartment fire was examined to assess whether appropriate detection characteristics can be maintained under seasonal conditions.
2. Numerical Methods and Conditions
The compartment dimensions were set to 2.0 × 3.2 × 2.0 m3, and a ceiling-mounted HVAC unit with four-directional air supply was installed at the center of the ceiling. The opening size was set to 0.8 × 2.0 m2. A fire source measuring 0.5 × 0.5 m2 was placed at a corner distant from the entrance, and simulations were conducted for the compartment configuration shown in Figures 1 and 2.
Floor plan of the experimental compartment.
Front view of the experimental compartment.
The ceiling-mounted HVAC unit was modeled based on the specifications of the Samsung AC130BN4PBH1. According to the manufacturer's specifications, the airflow rate was categorized into three levels: maximum, medium, and minimum. Based on this classification, the maximum airflow rate was set to 30.0 m3/min, the medium airflow rate to 25.0 m3/min, and the minimum airflow rate to 20.0 m3/min. The air supply outlet area was 0.6 × 0.1 m2. Dividing each airflow rate by the outlet area of 0.06 m2 yielded corresponding air velocities of 8.33 m/s, 6.95 m/s, and 5.55 m/s, respectively. Since cooling and heating operations occur in different seasons, indoor and outdoor temperature and humidity conditions were determined based on climate statistics from the Korea Meteorological Administration [7]. Winter conditions were defined based on average meteorological data for January and February, with a temperature of 0.8 and a relative humidity of 58.5%. Summer conditions were defined based on average data for July and August, with a temperature of 28.5 ℃ and a relative humidity of 74.0%. The HVAC setpoint temperatures were selected according to legally regulated environmental management standards, 25 ℃ for the summer and 24 ℃ for the winter.
For the heat detector, the response time index was set to 50.0 to ensure rapid initial responsiveness, and ACTIVATION_TEMPERATURE was set to 68.0 ℃. For the smoke detector, ACTIVATION_OBSCURATION was set to 3.24 %/m based on the Heskestad model, and the characteristic length was specified as 1.8 m.
The fire source was assumed to be an upholstered chair. The soot yield of the combustible material was set to 0.07, as proposed by Robbins and Wade [8]. To simulate the fire behavior of this combustible material, methane was used as the fuel. The maximum heat release rate was set to 2000 kW based on the experimental results reported by Kim and Lilley [9]. The fire source area was set to 0.25 m2, resulting in a corresponding heat release rate per unit area (HRRPUA) of 8000 kW/m2. Fire growth rates are commonly classified into four categories: slow, medium, fast, and ultra-fast. In this study, the fire growth coefficient a was set to 0.0466 kW/s2, as reported by Darkhanbat et al. [10]. Under this condition, the time required to reach steady state was 207 s. The numerical analysis conditions comprised a combination of two seasonal conditions, summer and winter, and three airflow levels, high, medium, and low, resulting in a total of six simulation cases, as summarized in Table 1. Based on the results of these six simulations, the combined effects of season and airflow rateas key HVAC environment variableson the performance of heat and smoke detectors were analyzed. In accordance with the analytical framework outlined in Table 2, the activation times of the fire detectors were evaluated through a systematic analysis of the simulation results.
Fire Simulation Scenarios
Analytical Focus of Each Section
3. Results and Discussion
3.1. Grid sensitivity analysis
Among the six simulation conditions, the case corresponding to the summer season with high airflow of 30 m3/min was selected for grid sensitivity analysis. Three grid configurations were considered: a uniform grid size of 5 cm, 10 cm, and a hybrid configuration in which a background grid of 10 cm was used, while regions influenced by the HVAC airflow were refined to a grid size of 5 cm. Figure 3 presents the time-dependent temperature measured at the heat detector, and Figure 4 shows the time-dependent obscuration. Overall, both temperature and obscuration tended to be predicted at lower levels as the grid size decreased. When a 10 cm grid was applied, the accuracy of the results was reduced in regions with complex flow phenomena, such as near the combustion interface and the smoke layer boundary, owing to insufficient spatial resolution. Applying a uniform 5 cm grid improved accuracy; however, the increased number of grid cells led to a substantial increase in computational cost. In this study, we adopted a hybrid grid configuration to maintain result accuracy while keeping the computational cost at a practical level, discretizing regions affected by HVAC airflow using a 5 cm grid and modeling the remaining regions using a 10 cm grid. The detection time obtained using this hybrid grid configuration differed by approximately 30% compared with that obtained using a uniform 5 cm grid over the entire domain.
Temperature of thermosensitive element depending on grid size.
Measured obscuration depending on grid size.
3.2. Analysis of fire detector activation time according to season
3.2.1. Seasonal analysis of heat detector detection time
For seasonal comparison, the airflow rate was fixed at the medium level. As shown in Figure 5, the detection time of the heat detector in summer was 78.1 s, whereas the corresponding detection time in winter was 76.6 s. Detection in summer was delayed by approximately 1.5 s compared to winter. In summer, the supply air temperature was 25 ℃ and the ambient temperature was 28.5 ℃, resulting in a temperature difference of 3.5 ℃. In winter, the supply air temperature was 24 ℃ and the ambient temperature was 0.8 ℃, yielding a temperature difference of 23.2 ℃. Despite this substantial difference in thermal conditions, the difference in heat detector activation time was limited to only 1.5 s. This indicates that the influence of seasonal HVAC operation on the activation time of the heat detectors is minimal. This behavior is attributed to the HVAC airflow preventing the combustion products generated by the fire plume from effectively reaching the vicinity of the heat detector.
Detection time of heat detector under medium airflow condition.
3.2.2. Seasonal analysis of smoke detector detection time
As in the heat detector analysis, the airflow rate was fixed at the medium level for seasonal comparison. As shown in Figure 6, the detection time of the smoke detector in summer was 11.5 s, whereas detection in winter began at 10.5 s, indicating a 1.0 s detection delay in summer. In this case, the difference in detector activation time was also limited to 1.0 s, demonstrating minimal seasonal effects. Under winter conditions, the lower compartment temperature results in higher buoyancy of the hot thermal plume, causing it to rise more rapidly and to a greater height. Consequently, when the HVAC system operates at the same airflow rate, smoke transport is enhanced in winter, leading to a faster response of the smoke detector compared with summer conditions.
Detection time of smoke detector under medium airflow condition.
3.3. Analysis of fire detector activation time according to airflow rate
3.3.1. Analysis of heat detector detection time according to airflow rate
To analyze differences in heat detector activation time as a function of airflow rate, the medium airflow condition was selected as the reference, and relative differences in detection time were expressed as percentages, as shown in Figure 7. Under summer conditions with high airflow, the heat detector activation time was 94.1 s, representing a delay of 20.5% compared to the medium airflow reference time of 78.1 s. Under summer conditions with low airflow, the heat detector responded at 64.8 s, corresponding to a reduction of 17.0% relative to the medium airflow condition. These results indicate a proportional relationship in which the detection time of the heat detector increases as the airflow rate increases and decreases as the airflow rate decreases. This trend arises because the low-temperature air supplied by the air conditioning system interferes with fire detection. The temperature distributions at 63.6 s under different airflow conditions are shown in Figure 8. Under high airflow conditions, the supplied air enhanced mixing within the compartment, resulting in a more uniform temperature distribution. This indicates that buoyancy-driven flow from the fire plume is weakened by the HVAC airflow. Consequently, relatively low-temperature air remains near the detector, leading to an increase in fire detection time.
Detection time under various airflow conditions in summer.
Comparison of temperature distribution in the compartment under three distinct airflow conditions in summer (time: 63.6 s) for flow rates of (a) 30.0 m3/min; (b) 25.0 m3/min; (c) 20.0 m3/min.
Figure 9 presents the percentage differences in detection time according to airflow rate under winter conditions, using the medium airflow case as a reference. As in the summer cases, detection time in winter was delayed under high airflow conditions. Specifically, under winter–high airflow conditions, the heat detector activation time was 88.3 s, which is 15.3% longer than the medium airflow reference time of 76.6 s. Under low airflow conditions during the winter, the heat detector responded at 72.7 s, representing a reduction of 5.1% compared with the medium airflow case. These results confirm that regardless of season, the detection time of heat detectors increases as the airflow rate increases and decreases as the airflow rate decreases. The increase in fire detection time under high airflow conditions in winter is attributed to the same mechanism observed in the summer, as illustrated in Figure 10.
Detection time under various airflow conditions in winter.
Comparison of temperature distribution in the compartment under three distinct airflow conditions in winter (time: 72.0 s) for flow rates of (a) 30.0 m3/min; (b) 25.0 m3/min; (c) 20.0 m3/min.
3.3.2. Analysis of smoke detector detection time according to airflow rate
The fire detection times for all simulated conditions are summarized in Table 3. Overall, smoke detectors exhibited substantially faster detection times than heat detectors. In addition, the influence of season and airflow rate on smoke detector performance was less pronounced. This behavior is attributed to the accumulation of relatively large amounts of smoke in the upper layer of the compartment. In this study, the soot yield value used in the simulations was relatively high for the compartment size, resulting in rapid filling of the compartment with smoke. Under fire conditions characterized by substantial smoke generation, smoke detectors are therefore expected to exhibit superior fire detection performance regardless of season, HVAC operation, or airflow rate. However, under real fire conditions, smoke generation during the early stage of a fire is often limited. As a result, the present simulations may underestimate actual fire detection times compared with those under real-world conditions.
Comparison of Detection Time for Heat and Smoke Detectors
4. Conclusion
This study quantitatively analyzed the effects of ceiling-mounted HVAC systems on the response characteristics of heat and smoke detectors during compartment fires using FDS simulations. The principal findings regarding the comparative response characteristics of heat and smoke detectors under ceiling HVAC airflow conditions are summarized as follows:
1) The grid sensitivity analysis showed that as the grid size decreased from 10 to 5 cm, both the link temperature of the heat detector and obscuration values of the smoke detector decreased. To balance computational efficiency and accuracy, the numerical analysis was conducted using a hybrid grid configuration, in which regions influenced by HVAC airflow were discretized using a 5 cm grid, whereas the remaining regions were modeled using a 10 cm grid.
2) Differences in fire detector response time according to season were not significant. However, slightly faster responses were observed under winter conditions. Because the temperature difference between the heating supply air and the compartment interior in winter is substantially greater than that in summer, heat and smoke detectors responded more rapidly during winter fires than during summer fires. The detection time of smoke detectors was considerably shorter than that of heat detectors. Incorporating soot yield values representative of real fire conditions is expected to improve the accuracy of fire detection time predictions under actual fire scenarios.
3) Analysis of fire detector activation time according to airflow rate showed that detection time increased and decreased in parallel with airflow rate. For heat detectors, a clear proportional relationship with airflow rate was observed. In contrast, the influence of airflow rate on smoke detector performance was minimal compared to its effect on heat detector performance. Detection time may vary depending on factors such as the relative positioning of fire detectors and HVAC units and the setpoint temperature. Therefore, further studies are required to systematically examine these effects.
Notes
Author Contributions
Jun-O Sim.; writing—original draft preparation, conceptualization, formal analysis, Taehoon Kim.; writing—review and editing, conceptualization, supervision.
Conflicts of Interest
The authors declare no conflict of interest.