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The purpose of this study is to investigate the potential of Binary Decision Diagram (BDD) quantification methods to evaluate the current set of large linked fault trees used in nuclear power plant probabilistic risk assessments. The need for a BDD quantification method stems from desire to eliminate the simplifications that are inherent in the large linked fault tree quantification. These simplifications include the use of the rare event approximation, the use of truncation limits, and the simplified treatment of success terms. As part of a broader initiative to develop the next generation of the logic modeling tools, the BDD approach to the evaluation of large linked fault tree models plays a significant role. The successful development and application of BDD quantification methods can reduce the simplifications in the model thereby reducing resources associated with the model development, use, and maintenance at the same providing a more accurate estimate.
机译:本研究的目的是研究二元决策图(BDD)量化方法的潜力,以评估核电厂概率风险评估中使用的当前大型连接故障树木。对BDD定量方法的需求源于希望消除大链接故障树量化中固有的简化。这些简化包括使用罕见的事件近似,使用截断限制,以及成功术语的简化处理。作为开发下一代逻辑建模工具的更广泛主动的一部分,BDD方法对大型链接故障树模型的评估起着重要作用。 BDD量化方法的成功开发和应用可以降低模型中的简化,从而减少了与模型开发,使用和维护相关的资源,同样提供更准确的估计。

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