Investigation of Flammability of Hydrogen Gases With Diluent Gases Under Severe Accident Conditions Using CNFT Model

J. Jeon, N. Kim, Won Jun Choi, Taeseok Kim, S. J. Kim
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引用次数: 1

Abstract

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 H2-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.
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用CNFT模型研究严重事故条件下氢气与稀释气体的可燃性
福岛第一核电站事故发生后,作为氢风险分析的一部分,可燃性下限预测已成为一项重要的任务。虽然已经进行了大量的实验研究,但对于严重事故条件下的混合料的LFL结果仍然缺乏。本研究的目的是建立一个计算的非绝热火焰温度(CNFT)模型,以便于预测氢混合物的LFL。该模型考虑了火焰在传播过程中向周围环境的辐射传热所造成的热损失。该模型与以往通过计算绝热火焰温度来预测燃尽的模型相比,在不同混合情况下与实验结果吻合较好。特别是对h2 -空气-蒸汽混合物和初始温度升高的混合物的预测精度有了很大的提高。因此,评价CNFT模型在严重事故氢风险分析中的适用性是有价值的。采用MELCOR 1.8.6程序对当前1000mwe优化功率堆(OPR1000)在电站停电(SBO)情况下的氢风险进行了研究。结果表明,CNFT模型可以降低MELCOR默认模型计算的氢风险的不确定性。研究表明,所建立的CNFT模型可以提高氢混合物可燃性严重事故分析的可靠性。
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