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Computation of Quantitative Fire Risks Using Two-Model Monte Carlo Simulation and CFD Fire Model

机译:使用双模型Monte Carlo仿真和CFD火模型计算定量火灾风险

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Quantitative fire risk analysis requires integration of uncertainties and variability of the input data to computation of the fire consequences. In industrial environments with complicated geometries and fire loads, the consequence assessment must usually be carried out by fire simulation based on Computational Fluid Dynamics (CFD). Yet, integration of the probabilistic description of the input data and the computationally demanding CFD is a challenging task. We present a new Two-Model Monte Carlo (TMMC) technique enabling to perform Monte Carlo simulation in conjunction with computationally expensive tools like CFD. In TMMC, two models of different accuracy are used to simulate the same problem so that during the simulation, the input-distribution random space is covered by the less accurate but faster model and in a post-processing phase, a correction is made based on a smaller number of simulations using the more accurate model. The technique is validated using artificial cases and applied to assessment of fire-induced failure probabilities and time available for fire fighting in a nuclear power plant (NPP) electronics room using the FDS code. Finally, implementation of the TMMC results to NPP PSA systems is addressed using a classification approach of the NPP rooms according to their fire and risk characteristics.
机译:定量火灾风险分析需要将输入数据的不确定性和可变性集成到消防后果的计算。在具有复杂几何和火灾载荷的工业环境中,通常必须通过基于计算流体动力学(CFD)的火灾模拟来进行后果评估。然而,集成输入数据的概率描述和计算要求的CFD是一个具有挑战性的任务。我们提出了一种新的两种模型Monte Carlo(TMMC)技术,可以与CFD等计算昂贵的工具一起执行Monte Carlo仿真。在TMMC中,两种不同的精度模型用于模拟相同的问题,以便在仿真过程中,输入分布随机空间被较低的准确性但更快的模型和后处理阶段覆盖,基于校正使用更准确的模型较少的仿真。使用人工病例验证了该技术,并应用于使用FDS代码在核电厂(NPP)电子室中的消防失败概率和时间可用于评估火灾诱导的失效概率和时间。最后,根据其火灾和风险特征,使用NPP房间的分类方法来解决TMMC结果对NPP PSA系统的实现。

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