Purpose: To provide valuable information for scholars to grasp the current situations, hotspots and future development trends of reliability analysis area.Design/methodology/approach: In this paper, recent researches on efficient reliability analysis and applications in complex engineering structures like aeroengine rotor systems are reviewd.Findings: The recent reliability analysis advances of engineering application in aeroengine rotor system are highlighted, it is worth pointing out that the surrogate model methods hold great efficiency and accuracy advantages in the complex reliability analysis of aeroengine rotor system, since its strong computing power can effectively reduce the analysis time consumption and accelerate the development procedures of aeroengine. Moreover, considering the multi-objective, multi-disciplinary, high-dimensionality and time-varying problems are the common problems in various complex engineering fields, the surrogate model methods and its developed methods also have broad application prospects in the future.Originality/value: For the strong demand for efficient reliability design technique, this review paper may help to highlights the benefits of reliability analysis methods not only in academia but also in practical engineering application like aeroengine rotor system.

The creep-fatigue resistance of turbine rotor seriously affects the reliability performance and service lifetime of aircraft engine. Creep-fatigue reliability assessment is an effective measure to quantify the uncertain creep-fatigue damage and evaluate the creep-fatigue reliable life for turbine rotor. To improve the modeling accuracy and simulation efficiency of creep-fatigue reliability assessment, a multi-surrogate collaboration approach (MSCA) is proposed by absorbing the strengths of the proposed dynamic neural network surrogate (DNNS) into distributed collaborative strategy. The creep-fatigue reliability assessment of a typical turbine rotor is regarded as one case to estimate the presented MSCA with respect to the fluctuations of multi-physical variables and the variabilities of multi-model parameters. The assessment results reveal that the creep-fatigue reliable life of turbine rotor under reliability degree of 0.998 7 is 629 cycles, and the fatigue strength coefficient and holding creep time play a leading role on creep-fatigue reliable life since their effect probabilities of 27 % and 19 %, respectively. Comparison of various methods (direct Monte Carlo simulation, response surface, neural network surrogate, DNNS) shows that the presented MSCA holds high efficiency and accuracy in creep-fatigue reliability assessment of turbine rotor.

To improve the computational efficiency and accuracy of reliability‐based fatigue life prediction for complex structure, a time‐varying particle swarm optimization‐ (PSO‐) based general regression neural network (GRNN) surrogate model (called as TV/PSO‐GRNN) is developed. By integrating the proposed space‐filling Latin hypercube sampling technique and PSO‐GRNN regression function, the mathematical model of TV/PSO‐GRNN is studied. The reliability‐based fatigue life prediction framework is illustrated in respect of the TV/PSO‐GRNN surrogate model. Moreover, the reliability‐based fatigue life prediction of an aircraft turbine blisk under multiphysics interaction is performed to validate the TV/PSO‐GRNN model. We obtain the distributional characteristics, reliability degree, and sensitivity degree of fatigue failure cycle, which are useful for the turbine blisk design. By comparing the direct simulation (FE/FV model), RSM, GRNN, PSO‐GRNN, and TV/PSO‐GRNN, we observe that the TV/PSO‐GRNN surrogate model is promising to perform the reliability‐based fatigue life prediction of the turbine blisk and enhance the computational efficiency while ensuring an acceptable computational accuracy. The efforts of this study offer a useful insight for the reliability‐based design optimization of complex structure.