基于贝叶斯估计的核电厂安全壳内压概率安全评估

Probabilistic Safety Assessment of Containment Structure under Internal Pressure Based on Bayesian Estimation

  • 摘要: 核电厂安全壳的内压易损性评估多采用简化的对数正态分布模型,缺乏严格的理论分析。本文基于贝叶斯理论提出一种适用于核电厂安全壳的内压易损性评估方法。该方法首先建立了安全壳在内压作用下的确定需求模型,并通过添加修正项对需求模型进行修正,然后利用贝叶斯估计逐步对修正项进行筛选,从而获得准确的概率需求模型,最终通过建立安全壳极限状态方程给出易损性评估结果。在此基础上,引入内压概率模型实现了安全壳概率安全评估,并与采用传统易损性评估的概率安全评估结果进行了对比。结果表明,安全壳的总失效概率随着混凝土损伤面积比的增大而逐渐减小,采用贝叶斯估计方法获得的安全壳总失效概率均值大于传统易损性评估方法,而变异系数小于传统易损性评估方法。本文方法可为计算核电厂安全壳在内压下的概率安全评估提供更为精准和保守的结果。

     

    Abstract: The probabilistic safety assessment of nuclear containment under internal pressure is the key evaluation content for guaranteeing nuclear power plant safety, and the fragility assessment of the containment under internal pressure is the most crucial step in the probabilistic safety assessment of nuclear containment. At present, the internal pressure fragility assessment of the containment mostly adopts the simplified lognormal distribution model, which lacks a rigorous theoretical analysis. There are multiple uncertainty issues affecting structural responses of nuclear containment in the fragility analysis and the results of the fragility analysis should be on the basis of a rigorous capacity-demand model. Meanwhile, load parameters (i.e. internal pressure) are usually used as key information to assess the internal pressure failure probability of containment. However, damage and failure of containment are more intensively correlated to structural response parameters (e.g. structural acceleration, structural displacement, and damage indices) compared with load parameters. For this reason, this paper proposed an internal pressure fragility assessment method based on Bayesian theory for nuclear containment and a form of damage ratio was used as a parameter for fragility assessment of containment. The damage ratio was realized by defining the ratio of the damage area to the full area of the containment concrete. Specifically, the fragility assessment method first established a deterministic demand model for the containment under internal pressure, and then corrected the demand model by adding correction terms. After that, Bayesian estimation was used to gradually filter the correction terms, resulting in an accurate probabilistic demand model. Finally, a fragility assessment result was obtained by establishing a limit state equation for the containment. On this basis, the probabilistic model under internal pressure was introduced to realize the probabilistic safety assessment of the containment, and the results of probabilistic safety assessment were compared with those of conventional fragility assessment. The results show that the cumulative containment failure probability (CCFP) of the containment decreases gradually with the increase of the damage ratio and the coefficient of variation of the CCFP is obviously smaller than that of the conventional fragility assessment method. In this paper, Bayesian estimation is introduced for probabilistic safety assessment of internal pressure in containment, and the damage ratio is used as a key parameter for fragility assessment of containment, which can provide more accurate and conservative results for probabilistic safety assessment of internal pressure in containment.

     

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