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Within the TF-project SiVi the BAM occupies itself interdisciplinary with the safety of traffic infrastructures considering extremely severe damage events. Therefore, a scenario of fire inside a traffic tunnel induced by a gas cloud explosion is chosen as representative. Additionally, a dangerous goods transporter shall be involved in the scenario.
As further aspect which shall be investigated is the use of liquefied natural gas (LNG) as fuel support in transport vehicle like trucks in future. The main component of LNG is methane as a flammable gas. To transport a sufficient amount of fuel the methane is cooled down at -161°C, its boiling point. In liquid phase LNG is compressed 600 times more than in gas phase.
The issue of department 2.4 is to identify the hazards for people and the tunnel structure located in the surrounding whilst a potential release of LNG out of a leaking tank occurs. Therefore, the temporal and spatial distribution of the gas cloud within the tunnel structure shall be recorded.
Basis for this will be experiments in 1:1 at BAM test rig TTS as well as numerical analysis within ANSYS CFX. On top ignition tests of the distributed gas cloud in a down scaled model allow statements on the emerging maximum pressure and temperature.
Methodologies on fire risk analysis in road tunnels consider numerous factors affecting risks (risk indicators) and express the results by risk measures. But only few comprehensive studies on effects of risk indicators on risk measures are available. For this reason, this study quantifies the effects and highlights the most important risk indicators with the aim to Support further developments in risk analysis. Therefore, a system model of a road tunnel was developed to determine the risk measures.
The system model can be divided into three parts: the fire part connected to the fire model Fire Dynamics Simulator (FDS); the evacuation part connected to the evacuation model FDS+Evac; and the frequency part connected to a model to calculate the frequency of fires. This study shows that the parts of the system model (and their most important risk indicators) affect the risk measures in the following order: first, fire part (maximum heat release rate); second, evacuation part (maximum preevacuation time); and, third, frequency part (specific frequency of fire). The plausibility of These results is discussed with view to experiences from experimental studies and past fire incidents.
Conclusively, further research can focus on these most important risk indicators with the aim to optimise risk analysis.
Methodologies on fire risk analysis in road tunnels consider numerous factors affecting risks (risk indicators) and express the results by risk measures. But only few comprehensive studies on effects of risk indicators on risk measures are available. For this reason, this study quantifies the effects and highlights the most important risk indicators with the aim to Support further developments in risk analysis. Therefore, a system model of a road tunnel was developed to determine the risk measures.
The system model can be divided into three parts: the fire part connected to the fire model Fire Dynamics Simulator (FDS); the evacuation part connected to the evacuation model FDS+Evac; and the frequency part connected to a model to calculate the frequency of fires. This study shows that the parts of the system model (and their most important risk indicators) affect the risk measures in the following order: first, fire part (maximum heat release rate); second, evacuation part (maximum preevacuation time); and, third, frequency part (specific frequency of fire). The plausibility of These results is discussed with view to experiences from experimental studies and past fire incidents.
Conclusively, further research can focus on these most important risk indicators with the aim to optimise risk analysis.