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Joint optimization of safety barriers for enhancing business continuity of nuclear power plants against steam generator tube ruptures accidents

机译:加强汽发管管破裂事故核电厂业务连续性的安全障碍联合优化

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In nuclear power plants (NPPs), different types of safety barriers are designed to ensure the safe and continuous operation of the NPP against disruptive events. These safety barriers, although designed to operate in different phases of the accidents evolution, are often optimized separately, without considering their collective effects on preventing disruptions and quickly recovering from the disruptions. This paper develops a joint optimization model for synthetically optimizing safety barriers of different natures, including prevention, mitigation, emergency and recovery barriers to enhance the business continuity of the NPP, considering the threat of steam generator tube rupture (SGTR) accidents. The joint optimization is guided by a business continuity metric called expected business continuity value (EBCV). A physics-of-failure model is developed to describe the crack growth process of the steam generator tube and to model the effect of the prevention barriers, i.e., periodical inspection of the crack length. An event tree model is developed to describe the evolution of the SGTR-initiated accident and to model the effect of the mitigation and emergency barriers. Recovery measures are also considered via a widely-used logarithmic function model. A mixed-integer genetic algorithm (MIGA) is used to obtain optimal solutions of the joint optimization model. The results show that the developed joint optimization model can achieve better performance in terms of business continuity, compared to the conventional methods that optimize the safety barriers separately.
机译:在核电站(NPPS)中,不同类型的安全屏障旨在确保NPP对破坏事件的安全和连续运行。这些安全障碍虽然旨在以事故演变的不同阶段操作,但通常单独优化,而不考虑他们对预防破坏并快速从中断恢复的集体影响。本文开发了合成优化不同自然的安全屏障的联合优化模型,包括预防,缓解,应急和恢复障碍,以提高NPP的业务连续性,考虑到蒸汽发生器管破裂(SGTR)事故的威胁。联合优化由名为预期业务连续性值(EBCV)的业务连续性度量指导。开发了一种物理学模型以描述蒸汽发生器管的裂纹生长过程,并模拟防止屏障的效果,即对裂缝长度的期刊检查。开发了一个事件树模型来描述SGTR启动事故的演变,并模拟缓解和应急障碍的效果。还通过广泛使用的对数函数模型考虑恢复措施。混合整数遗传算法(MIGA)用于获得联合优化模型的最佳解决方案。结果表明,与单独优化安全屏障的传统方法相比,开发的联合优化模型可以在业务连续性方面实现更好的性能。

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