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Test interval optimization of safety systems of nuclear power plant using fuzzy-genetic approach

机译:基于模糊遗传算法的核电厂安全系统测试区间优化

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Probabilistic safety assessment (PSA) is the most effective and efficient tool for safety and risk management in nuclear power plants (NPP). PSA studies not only evaluate risk/safety of systems but also their results are very useful in safe, economical and effective design and operation of NPPs. The latter application is popularly known as "Risk-Informed Decision Making". Evaluation of technical specifications is one such important application of Risk-Informed decision making. Deciding test interval (TI), one of the important technical specifications, with the given resources and risk effectiveness is an optimization problem. Uncertainty is inherently present in the availability parameters such as failure rate and repair time due to the limitation in assessing these parameters precisely. This paper presents a solution to test interval optimization problem with uncertain parameters in the model with fuzzy-genetic approach along with a case of application from a safety system of Indian pressurized heavy water reactor (PHWR).
机译:概率安全评估(PSA)是用于核电厂(NPP)安全和风险管理的最有效和高效的工具。 PSA研究不仅评估系统的风险/安全性,而且其结果对于核电厂的安全,经济和有效设计和运行也非常有用。后者的应用程序通常被称为“风险决策”。对技术规范的评估是基于风险的决策制定的重要应用之一。在给定资源和风险有效性的情况下,确定测试间隔(TI)是重要的技术规范之一,这是一个优化问题。由于精确评估这些参数的局限性,可用性参数(例如故障率和维修时间)固有地存在不确定性。本文提出了一种用模糊遗传方法对模型中不确定参数的区间优化问题进行测试的解决方案,并以印度加压重水堆(PHWR)安全系统的应用为例。

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