To improve the dynamic reliability analysis of complex structures like turbine blisk, moving extremum surrogate modeling strategy (MESMS) is proposed in respect of multi-physics coupling with various dynamics/uncertainties. In this strategy, extremum thought is adopted to handle the dynamic process of input parameters and output response, and the importance sampling (IS) method is utilized to extract efficient samples and improve the efficiency of dynamic reliability estimation, and moving least square (MLS) method is used to select good samples from training samples with local compact support region to establish a precise surrogate model. The dynamic reliability analysis of turbine blisk radial deformation with fluid-thermo-structural interaction is performed to validate the developed MESMS in approximate precision and simulation performance by comparing to other methods. As shown in this study, (i) the reliability degree of turbine blisk is 0.9975 when the allowable value of radial deformation is 2.6856 × 10⁻³ m subject to 3 sigma levels; (ii) the proposed MESMS processes high modeling accuracy and efficiency due to small error; (iii) the MESMS holds high simulation performance in efficiency and accuracy owing to outstanding computational consumption and simulation. These results demonstrate that the MESMS is effective and applicable in structural reliability estimation regarding dynamics and uncertainties. The efforts of this paper provide a useful insight for performing the reliability-based design optimization of complex structures besides turbine blisk.

为了改善涡轮叶轮等复杂结构的动态可靠性分析，针对具有多种动力学/不确定性的多物理场耦合，提出了移动极值替代模型策略（MESMS）。在这种策略中，采用极值思想来处理输入参数和输出响应的动态过程，并采用重要性抽样（IS）方法提取有效样本并提高动态可靠性估计和移动最小二乘（MLS）的效率。该方法用于从具有局部紧支撑区域的训练样本中选择好的样本，建立精确的替代模型。进行了具有流体-热-结构相互作用的涡轮叶轮径向变形的动态可靠性分析，以通过与其他方法进行比较来验证所开发的MESMS的近似精度和仿真性能。如本研究所示，（i）当径向变形的允许值为2.6856×10时，涡轮叶轮的可靠性等级为0.9975。⁻³ m符合3 sigma等级；（ii）由于误差小，建议的MESMS处理建模精度高且效率高；（iii）由于出色的计算量和仿真能力，MESMS在效率和准确性上都具有很高的仿真性能。这些结果表明，MESMS在动力学和不确定性的结构可靠性评估中是有效且适用的。本文的工作为涡轮机叶轮以外的复杂结构的基于可靠性的设计优化提供了有用的见识。