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INTEGRATED METHODOLOGY FOR THERMAL-HYDRAULIC CODE UNCERTAINTY ANALYSIS WITH APPLICATION

机译:热工代码不确定性的综合方法学及应用

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This paper discusses an integrated thermal-hydraulic (TH) uncertainty analysis methodology with an application to the Loss-of-Fluid Test (LOFT) test facility large-break loss-of-coolant accident (LBLOCA) transient. The methodology is intended for applications to best-estimate analyses of complex TH codes. The goal is to develop an integrated method to make such codes capable of comprehensively supporting the uncertainty assessment with the ability to handle important accident transients. The proposed methodology considers the TH code structural uncertainties (generally known as model uncertainty) explicitly by treating internal submodel uncertainties and by propagating such model uncertainties in the code calculations, including uncertainties about input parameters. The methodology is probabilis-rntic, using the Bayesian approach for incorporating available evidence in quantifying uncertainties in the TH code predictions. The types of information considered include experimental data, expert opinion, and limited field data, in treating both model and input parameter uncertainties. The code output is further updated through additional Bayesian updating with available experimental data from the integrated test facilities. The methodology uses an efficient Monte Carlo sampling technique for the propagation of uncertainty, in which a modified Wilks' sampling criteria of tolerance limits is used to significantly reduce the number of simulations. This paper describes the key elements of the uncertainty analysis methodology and summarizes its application to the LOFT test facility LBLOCA.
机译:本文讨论了一种综合的热工(TH)不确定性分析方法,并将其应用于流体损失测试(LOFT)测试设施大断裂冷却液损失事故(LBLOCA)瞬态。该方法旨在用于最佳估计复杂TH代码的分析。目标是开发一种集成方法,以使此类代码能够全面支持不确定性评估并具有处理重要事故瞬变的能力。所提出的方法通过处理内部子模型不确定性并通过在代码计算中传播此类模型不确定性(包括有关输入参数的不确定性)来明确考虑TH代码的结构不确定性(通常称为模型不确定性)。该方法是概率性的,使用贝叶斯方法来合并可用的证据以量化TH代码预测中的不确定性。在处理模型和输入参数不确定性时,考虑的信息类型包括实验数据,专家意见和有限的现场数据。通过使用来自集成测试设施的可用实验数据进行的附加贝叶斯更新,可以进一步更新代码输出。该方法使用有效的蒙特卡洛采样技术来传播不确定性,其中使用了修正的Wilks公差极限采样标准来显着减少仿真次数。本文描述了不确定性分析方法的关键要素,并总结了其在LOFT测试设备LBLOCA中的应用。

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