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针对航空发动机适航条款FAR33.75中关于发动机限寿件(ELLP)结构失效概率要求,提出了一种基于Kriging和蒙特卡罗半径外重要抽样(MCROIS)混合的结构概率风险评估方法。该方法针对ELLP高维、小失效概率事件以及极限状态函数为隐式、高度非线性的特点,利用Kriging元模型模拟隐式极限状态函数,然后通过主动学习迭代算法,计算最优点(MPP,最接近设计验算点的样本点),更新实验设计(DOE)并提高Kriging元模型的模拟精度。在此基础上,利用Kriging元模型确定最优抽样半径,构造半径外重要抽样密度函数,在最优抽样半径确定区域进行抽样,通过构造主动学习函数,使样本点更多落在抽样半径确定的球区域附近,加速失效概率计算的收敛,并构建了ELLP风险概率模型,解决了高维、小失效概率事件以及隐式、非线性极限状态函数的发动机结构概率风险评估难题,以某型发动机低压压气机轮盘为应用示例,与传统的蒙特卡罗仿真(MCS)方法进行了对比,验证了该方法的高效率、鲁棒性和仿真精度。
Aiming at the structural failure probability of ELLP in FAR33.75, a structural risk assessment method based on Kriging and Monte Carlo outer important sampling (MCROIS) mixture is proposed. The method is based on Kriging metamodel to simulate the implicit limit state function of ELLP high dimensional and small failure probability events and the limit state function as implicit and highly nonlinear. Then by using the active learning iterative algorithm, the optimal point (MPP) Close to the design checkpoint sample points), update the experimental design (DOE) and improve the accuracy of Kriging metamodel simulation. On this basis, the Kriging metamodel is used to determine the optimal sampling radius, the important sampling density function outside the radius is constructed, and the sampling area is sampled in the optimal sampling radius. By constructing the active learning function, The convergence of the calculation of the failure probability is accelerated, and the ELLP risk probability model is constructed to solve the problem of high-dimensional and small failure probability events and the implicit and nonlinear limit state function of the engine structural probability risk assessment. Taking a certain type of engine low pressure Compressor roulette is an application example, which is compared with the traditional Monte Carlo simulation (MCS) method to verify the method’s high efficiency, robustness and simulation accuracy.