An efficient system reliability analysis method for flap mechanism under random-interval hybrid uncertainties

Reliability analysis of the complex structural system is a popular issue due to complex failure regions and frequently time-consuming simulations. In this work, a new method based on the active learning Kriging model is proposed to solve the problem with both random and interval hybrid uncertainty. The proposed method divides the original variable space based on the proposed adaptive important exploration region (AIER), which approximates only the bounds of the failure domain. In each iteration, the proposed approach selects the optimal training point from the AIER by utilizing a predicted sign error function related to the system indicator function, which can effectively measure the uncertainty of the complex system. Subsequently, the error estimation of the failure probability is used as the convergence criterion to ensure the accuracy and efficiency of reliability analysis. Afterward, the performance of the proposed method is evaluated by investigating four examples, which show the reasonability and superiority of this method. Finally, the multibody dynamics simulation of the flap mechanism is presented, which involves the aerodynamic load analysis and the simulation modeling. Considering the two failure modes of stuck and insufficient accuracy, it is shown that the proposed method greatly reduces the number of calls for the simulation model and ensures the accuracy of the evaluation during the reliability analysis of the flap mechanism.