首页> 外文会议>ASME(American Society of Mechanical Engineers) Pressure Vessels and Piping Conference 2007 >APPLICATION OF FAILURE EVENT DATA TO BENCHMARK PROBABILISTIC FRACTURE MECHANICS COMPUTER CODES
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APPLICATION OF FAILURE EVENT DATA TO BENCHMARK PROBABILISTIC FRACTURE MECHANICS COMPUTER CODES

机译:故障事件数据在基准概率断裂力学计算机代码中的应用

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This paper describes an application of data on cracking, leak and rupture events from nuclear power plant operating experience to estimate failure frequencies for piping components that had been previously evaluated using the PRO-LOCA and PRAISE probabilistic fracture mechanics (PFM) computer codes. The calculations had addressed the failure mechanisms of stress corrosion cracking, intergranular stress corrosion cracking and fatigue for materials and operating conditions that were known to have failed components. The first objective was to benchmark the calculations against field experience. A second objective was a review of uncertainties in the treatments of the data from observed failures and in the structural mechanics models. The database PIPExp-2006 was applied to estimate failure frequencies. Because the number of reported failure events was small, there were also statistical uncertainties in the estimates of frequencies. Comparisons of predicted and observed failure frequencies showed that PFM codes correctly predicted relatively high failure probabilities for components that had experienced field failures. However, the predicted frequencies tended to be significantly greater than those estimated from plant operating experience. A review of the PFM models and inputs to the models showed that uncertainties in the calculations were sufficiently large to explain the differences between the predicted and observed failure frequencies.
机译:本文介绍了核电站运行经验中的裂纹,泄漏和破裂事件数据的应用,以估算先前使用PRO-LOCA和PRAISE概率断裂力学(PFM)计算机代码评估过的管道部件的故障频率。该计算已解决了应力腐蚀开裂,晶间应力腐蚀开裂和疲劳的失效机理,适用于已知具有失效组件的材料和工作条件。第一个目标是根据现场经验对计算进行基准测试。第二个目标是对观察到的故障数据处理和结构力学模型中的不确定性进行回顾。数据库PIPExp-2006用于估计故障频率。由于报告的故障事件数量很少,因此在频率估算中也存在统计不确定性。预测故障频率和观察到的故障频率的比较表明,对于经历过现场故障的组件,PFM代码正确地预测了相对较高的故障概率。但是,预测的频率往往明显大于根据工厂运行经验所估计的频率。对PFM模型和模型输入的回顾表明,计算中的不确定性足够大,足以解释预测的和观察到的故障频率之间的差异。

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