期刊
IEEE ACCESS
卷 8, 期 -, 页码 39861-39874出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2975316
关键词
Reliability; Turbines; Rotors; Training; Computational modeling; Cost function; Mathematical model; Creep-fatigue life; reliability assessment; turbine rotor; surrogate model; neural network
资金
- National Natural Science Foundation of China [51975028, 51575024]
- Academic Excellence Foundation of BUAA [BY1604137]
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 & x0025; and 19 & x0025;, 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.
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