海洋条件下非能动系统高效全局敏感性分析方法研究

Efficient global sensitivity analysis method for passive systems under ocean conditions

非能动系统运行过程中包含大量的不确定性参数,一般通过最佳估算加不确定性分析对其可靠性进行评估,评估过程中一个重要的步骤是参数的敏感性分析,用来识别系统关键参数以降低模型复杂性。传统的全局敏感性分析因其高昂的计算成本难以被应用于复杂的核动力系统中,为提高其分析效率,本研究提出采用低秩近似(Low-Rank Approximations,LRA)方法改进基于蒙特卡罗的Sobol方法,并利用多个基准题验证所提方法的有效性。最后用此方法对某一体化压水堆的非能动余热排出系统进行敏感性分析。结果表明:本文所提方法仅需200次模拟计算,耗时约55 min就能准确识别系统关键参数,且与Sobol方法运行1.0×10^(5)次模拟计算,耗时约19 d所得到的敏感性排序结果一致。因此,本研究建立的高效全局敏感性分析方法可为非能动系统的可靠性分析和设计优化等过程提供有效指导。

[Background]Passive safety system reliability is generally evaluated through best estimation plus uncertainty(BEPU)analysis.An important step in the evaluation process is sensitivity analysis of parameters,which is used to identify key system parameters to reduce the complexity of the model.However,local sensitivity analysis methods based on linear or monotonic assumptions may yield incorrect sensitivity results for complex nuclear power systems.Meanwhile,applying the global sensitivity method is difficult in practical engineering because of its high calculation cost.[Purpose]This study aims to develop an efficient and low-cost global sensitivity analysis method for passive systems under ocean conditions.[Methods]Firstly,the low-rank approximation(LRA)method was employed to improve the Sobol method based on Monte Carlo simulation.The number of unknown coefficients was significantly reduced by using the multivariate-based tensor product.Then,the LRA coefficients were used to calculate the sensitivity index,and the validity of the proposed method was verified by addressing several sensitivity analysis benchmark questions.Finally,taking an integrated pressurized water reactor including the passive residual heat removal system as the object,a simulation program for thermal-hydraulics analysis under ocean conditions was developed. And its sensitivity analysis was conducted using the proposed method. [Results] The results show that the proposed method can accurately identify system key parameters after only 200 simulation calculations taking about 55 min, and the sensitivity ranking results are consistent with those obtained by Sobol method after 1.0×10^(5) simulation calculations taking about 19 d. [Conclusions] The efficient global sensitivity analysis method established in this study can provide effective guidance for reliability analysis and design optimization of passive systems.