3.1. Experimental Data
Table 4 details the mean and maximum values of the pollutants and the maximum and minimum values of oxygen, based on data recorded downstream of the drift every minute for the five tests studied. CO
2 values below 1% were not detected due to the sensitivity of the measuring equipment used.
Results from the previous table show almost constant oxygen levels in all cases, except when there was a very small opening of the air inlet door or it was closed, between 0% and 4%, lowering the oxygen level to 16% in these cases. Furthermore, it coincided with the highest levels of CO, NOx, and SO2. Based on the experiments, it can be seen that an abrupt reduction in the air supply had a much greater impact on the variation of pollutant concentration than an increase in the fuel load. The CO2 measurements only give an indicative value, since the sensor used was not entirely suitable for the kind of test performed due to a lack of sensitivity of the device, in the lower range.
As mentioned in
Section 2.3, the most representative fire scenarios are displayed in
Figure 5,
Figure 6,
Figure 7,
Figure 8 and
Figure 9, detailing the evolution of the pollutants, temperature, airflow, and pressure as a function of time and for each of the five main scenarios analyzed.
The dry temperature did not present any change with respect to the outside temperature except in Scenario 5, the test with the biggest fire load, having a small variation over time. On the other hand, the airflow was significantly affected by the fire evolution over time when the door was fully open. There was a variation between 12.5 m3/s and 20 m3/s for a load of 180 L of fuel, while, for loads of 360 and 540 L, the flow increased to 30 m3/s, representing a 60% increase for E3 scenario and between 150 and 200% for E4 and E5. This variation occurred after 960 s from the beginning of the fire in E3, while it happened at 450 and 420 s for E4 and E5, respectively. This phenomenon is due to a natural pull of the ventilation in the same direction of the flow due to a temperature increase once the fire reaches its maximum development. Then, as the fire loses power, the additional airflow is gradually reduced until it reaches the initial situation. In addition, it was also observed that it acted as a resistance during a brief period of time at an early stage of the fire, reducing the airflow.
In the case of pollutants generated by the fire, scenarios E1 and E2 followed a similar trend, while scenarios E3 to E5 had a different common pattern. In the first case, there was a reduction in oxygen as a consequence of a very important increase in the concentrations of CO, NOx, SO2, and CO2 900 and 1650 s after the start of the fire for trials E1 and E2, respectively. This difference is mainly given by the airflow variation in the intake. In the E1 test, the airflow was very low, 1.93 m3/s, increasing the pollutants due to incomplete combustion in an earlier stage. In the case of E2, as the airflow was higher, 5.1 m3/s, the incomplete combustion phenomenon appeared once the fire was fully developed.
On the other hand, trials E3, E4, and E5 showed a common behavior, increasing the concentration of pollutants once the fire reached its maximum power, with a stable emission period and finally decreasing the concentration as the fire faded. The only difference between scenarios was the pollutant concentration, showing a direct relationship with the fuel load, and the time to reach the maximum level of emissions (1080, 480, and 450 s, respectively).
Three differentiated fire phases can be defined based on the experiments: (1) growth, with a small generation of pollutants, which increased over time; (2) maximum power of the fire, with a plateau with a variable duration depending on each test, with high concentration of pollutants; (3) decrease and end of the fire, with a progressive reduction of the pollutants concentration over time.
The temperature evolution throughout the drift is shown below as a function of time for the five scenarios analyzed.
Figure 10,
Figure 11,
Figure 12,
Figure 13 and
Figure 14 gather these scenarios, distinguishing the lower and upper parts of the drift at 0.5 and 2 m, respectively.
The temperature evolution displayed a similar trend: a rise in temperature followed by a stable period of maximum temperature and then a progressive decrease as the fire faded. However, there was an important divergence if it was compared the period from which the temperature increased because of the fire. The temperature increase was recorded earlier than pollutants in E1 and E2, while, in E3 and E4, the opposite occurred and E5 showed an almost identical start for temperature and pollutants.
On the other hand, a phenomenon already mentioned in the previous figures was observed upon analyzing the behavior of the flow. Shortly after reaching the maximum fire power, there was a decrease in the temperature until it reached a stable level. This was due to the fire acting as a resistance in the ventilation circuit at the beginning, as can be seen in the sensors close to the fire: S2, S3, and S4. This phenomenon indicates that there was an advance of the fumes in the opposite direction of the flow during this period, i.e., a roll-back [
8], being more pronounced in the case of lower initial airflow. Regarding the maximum temperatures reached, there was a notable difference between lower and upper parts of the drift, showing a 60–320% difference in temperature for the same cross-section.
The outcomes obtained are very important to know potential hazardous scenarios in drifts from the production level, with similar sections and airflows, and where an important quantity of equipment is placed. The knowledge of temperatures and pollutant concentration over time can be used to apply safety and corrective measures.
3.2. FDS Results
Results obtained in the experiments and the FDS model are detailed and compared below. The five scenarios depending on the fuel load and door opening are gathered in
Table 5 and
Table 6. As previously mentioned, there were three temperature control points at different heights in each section (P1: 2 m, P2: 1 m, P3: 0.5 m).
The mean percentage deviations between experimental and simulated values were as follows: E1, 27%; E2, 35%; E3, 24%; E4, 22%; E5, 20%. An accuracy was reached similar to other studies using CFD software to analyze the ventilation behavior in full-scale underground mine conditions [
16]. The FDS simulation presented more adjustment problems with the actual values when the airflow was very low (E1 and E2). The correlation of the mean values by section between experimental data and simulations is shown in
Figure 15 and
Figure 16.
The FDS adjustment showed similar patterns between E1 and E2 and between E3, E4, and E5. In the first case, it showed a good experimental–FDS fit in the pre-fire zone, while the simulated values exceeded the fire temperatures in the zone after the fuel load, with a tendency to converge when the fire faded. On the other hand, trials E3, E4, and E5 showed a very good fit up to the area just after the fire, where the temperature was underestimated (S4 at 5 m) and then slightly overestimated (S5 at 10 m), following the same trend throughout the drift. Overall, the correlation between the experimental and simulated mean values was better when there was more airflow and higher firepower.
Further research stated some variations in the results with newer FDS versions [
27], but consistent with experimental data in both cases [
27]. Moreover, the performance and accuracy of the FDS version used in the study was validated in previous studies comparing simulations and experimental data [
25], which is in accordance with the outcomes achieved in this study. Results are adequate to study fire situations in underground mining, but further research should be done to be fully applicable for tunneling and other large closed spaces, specifically increasing the accuracy of the simulations with a smaller grid cell and examining the immediate area around the fire.