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首页> 外文会议>International conference on nuclear engineering >INVESTIGATION OF FLAMMABILITY OF HYDROGEN GASES WITH DILUENT GASES UNDER SEVERE ACCIDENT CONDITIONS USING CNFT MODEL
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INVESTIGATION OF FLAMMABILITY OF HYDROGEN GASES WITH DILUENT GASES UNDER SEVERE ACCIDENT CONDITIONS USING CNFT MODEL

机译：使用CNFT模型研究在严重事故条件下氢气与稀释气的易燃性

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After the Fukushima Daiichi accident, predicting lower flammability limits (LFL) as a part of hydrogen risk analysis has become an ever important task. Although many experimental studies have been conducted extensively, the LFL results for mixtures abided by the severe accident conditions are still lacking. The objective of this study is to develop a calculated non-adiabatic flame temperature (CNFT) model, which facilitates to predict the LFL of hydrogen mixtures. This model considers heat loss due to radiative heat transfer from flame to ambient environment during flame propagation. The model shows better agreement with experimental results for various mixtures than previous model, which predicts the LFL through a calculated adiabatic flame temperature. . Especially, prediction accuracy for H_2-air-steam mixture and mixtures at elevated initial temperature is improved substantially. Thus it is worth to evaluate the applicability of the CNFT model in the hydrogen risk analysis during severe accident. The postulated hydrogen risk in the current Optimized Power Reactor 1000 MWe (OPR1000) under Station Blackout (SBO) scenario was investigated with MELCOR 1.8.6 code. As a result, it was observed that uncertainty of hydrogen risk calculated with the MELCOR default model can be reduced by the CNFT model. This study suggests that the developed CNFT model can enhance reliability of severe accident analysis related to the flammability of hydrogen mixtures.
机译：福岛第一核电站事故发生后，预测较低的可燃极限（LFL）作为氢风险分析的一部分已成为一项日益重要的任务。尽管已经进行了许多实验研究，但是仍然缺乏针对严重事故情况所遵循的混合物的LFL结果。这项研究的目的是建立一个计算的非绝热火焰温度（CNFT）模型，该模型有助于预测氢混合物的LFL。该模型考虑了由于火焰传播过程中从火焰到周围环境的辐射热传递而造成的热量损失。与以前的模型相比，该模型显示出与各种混合物的实验结果更好的一致性，后者可以通过计算出的绝热火焰温度来预测LFL。 。尤其是，H 2-空气-蒸汽混合物和在升高的初始温度下的混合物的预测准确性被大大提高。因此，有必要评估CNFT模型在严重事故期间氢气风险分析中的适用性。使用MELCOR 1.8.6代码研究了在电站停电（SBO）情况下当前优化的反应堆1000 MWe（OPR1000）中假定的氢气风险。结果，观察到通过CNFT模型可以减少用MELCOR默认模型计算出的氢气风险的不确定性。这项研究表明，开发的CNFT模型可以增强与氢混合物的可燃性相关的严重事故分析的可靠性。

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