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A probabilistic approach for finite element analysis (FEA) for tunnel linings exposed to the nominal fire is presented. The probabilistic FEA accounted for the uncertainties distributions tied to the conductivity and specific heat as well as of the compressive strength, tensile strength, Young’s modulus, and ultimate strain in compression. To get an understanding on the influence of different probability density functions on the distribution of maximum displacements of the tunnel lining, a sensitivity analysis was performed. Four sets of FEAs were carried out with different probability distributions of the conductivity, the specific heat, and the compressive strength of the concrete, respectively. An experimental design based on a Latin Hypercube Sampling algorithm was performed to define the input parameters which describe each analysis case. A reliability analysis was executed considering a limit state function based on the temperature-dependent ultimate strain. The results show that, depending on the distribution adopted, the standard deviation of the maximum displacements can vary up to 47,4% of the minimum standard deviation. The large standard deviation is associated with the possibility of a greater displacement and, hence, to a structure more vulnerable to fire.
Probabilistic thermo-Mechanical analysis of a concrete tunnel lining subject to fire The probability distributions of the parameters related to the thermal analysis was considered in order to study the variability of the results and to carry out a reliability analysis. This assessment considered as random variables the thermo-mechanical properties of the concrete, the maximum heat release rate (HRR), the duration of the period of maximum HRR, the convective coefficient, the emissivity at the surface exposed to the fire, the air velocity within the tunnel, and the initial fire radius. The temperature-time curve was described by a correlation. An experimental design based on a Latin Hypercube Sampling algorithm was performed to define the input parameters to each analysis case. The definition of a limit state function based on the punctual strain status has permitted to carry out a reliability analysis.
Vor dem Hintergrund der Zurückziehung der DIN 18230 Teil 2 zur Bestimmung des m-Faktors von Materialien für die Brandlastbewertung im Industriebau müssen neue Wege gefunden werden, wie das Abbrandverhalten alternativ quantifiziert werden kann. Der Beitrag fasst die Entstehung und die Entwicklung der Bestimmungsweise von Abbrandfaktoren zusammen und gibt Ausblick auf eine neue Möglichkeit zur Bewertung von Brandlasten, die Verbrennungseffizienz.
Lithium-ion batteries are a key technology to achieve the goals of limiting climate change due to the important role as traction technology for Electric Vehicles and in stationary energy storage systems. Over(dis) charge, mechanical damage due to accidents or thermal abuse such as fires can initiate an accelerated self-heating process of the batteries, called thermal runaway. A thermal runaway can propagate from cell to cell within a larger assembly of cells such as modules or battery packs and can cause rapid heat and toxic gas emissions. The resulting battery fire can spread to adjacent facilities, e.g. other cars in underground car parks or to a whole building in case of a large stationary energy storage.
For proof of fire protection requirements or to design suitable fire protection systems, Computational Fluid Dynamic (CFD) simulations are getting more and more important. The aim of CFD fire simulations is to predict the global hazards of a fire to its surroundings, that is mainly characterized by the release of heat and smoke and its spread in the fire environment. There are many numerical investigations of lithium-ion batteries in the literature. One class of models is used to simulate the charge and discharge process of lithium-ion batteries and to predict the temperature or voltage evolution inside the battery. On the other hand, there are models describing batteries under abuse conditions to predict the consequences of a thermal runaway event to the local environment, like the temperatures inside a battery or at the battery surface. Henriksen et al. use a generic battery gas mixture to simulate an explosion of vented gases from a Lithium Iron Phosphate battery and compare experimental results for the explosion pressure and the position of the flame front to the outcomes of a simulation with Xifoam. Larsson et al. used a combination of CFD simulations with FDS and thermal model with COMSOL to predict the temperature development of neighboring cells in a thermal runaway propagation. Truchot et al. use a design Heat Release Rate (HRR) curve for a battery based on experimental measurements to build up an overall HRR curve for a truck loaded with 100 lithium-ion batteries. This summed up HRR and corresponding smoke production curve is then used as an input for a simulation of a truck fire in a tunnel with Fire Dynamics Simulator (FDS). The pre-definition of the HRR curve is a frequently used method in fire engineering. It has the disadvantage, that the heat release cannot be influenced by physical processes, such as changed ventilation conditions or extinguishing measures. In this paper, a model is presented that determines the release of heat and gases based on the thermal runaway mechanisms of the battery, which can be used in CFD fire simulations with focus on prediction of fire hazards to nearby environment.