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Prediction of smoke filling in large volumes by means of data assimilation-based numerical simulations

机译:通过基于数据同化的数值模拟预测大量烟气

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摘要

The concept of numerical simulations for real-time Numerical Fire Forecasting is illustrated for the case of natural smoke filling of a large-scale atrium in case of fire. The numerical simulations are performed within the Inverse Zone Modelling framework. The technique consists of assimilating collected data for a certain parameter, in casu the smoke layer height, into the zone model in order to estimate an unknown of the problem ('model invariant'), mainly the fire heat release rate. A forecast in terms of evolution of smoke level and temperature can then be produced. Because zone model calculations are very fast, positive lead times of several minutes are obtained. The developed model produces reliable forecasts for the cases considered. Equally important, the robustness of the technique is illustrated: the sensitivity of the results to the 'initial guess' of the model invariants is small (i.e. the method converges easily); one model invariant is sufficient to obtain reliable predictions for smoke layer height evolution; the data assimilation window length does not affect the results significantly. The method automatically provides a different value for the plume entrainment constant, depending on the position of the fire (in the middle of the atrium or in a corner).
机译:对于大面积中庭发生火灾时自然烟雾填充的情况,说明了用于实时数字火灾预测的数值模拟概念。数值模拟是在反向区域建模框架内执行的。该技术包括将烟雾层高度中某个参数的收集数据同化到区域模型中,以便估计问题的未知数(“模型不变式”),主要是火的放热率。然后可以产生关于烟雾水平和温度演变的预测。因为区域模型的计算非常快,所以获得了几分钟的正提前期。所开发的模型可以为所考虑的案例提供可靠的预测。同样重要的是,该技术的鲁棒性得到了说明:结果对模型不变量的“初始猜测”的敏感性很小(即该方法易于收敛);一个模型不变性足以获得烟层高度演变的可靠预测;数据同化窗口的长度不会显着影响结果。该方法根据火焰的位置(在中庭的中间或角落)自动为烟流夹带常数提供不同的值。

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