涡轮轴低周疲劳寿命可靠性分析及优化设计方法研究

In order to ensure reliability and increase low cycle fatigue life (LCFL) of turbine shaft under various random uncertainties, the reliability analysis and reliability based design optimization (RBDO) were studied. A parameter platform is established for structure analysis, reliability analysis and RBDO of the LCFL of turbine shaft. Based on this platform, calling structure finite element software and reliability analysis can be automatically realized at different design parameters visited by RBDO iteration. For improving efficiency of reliability analysis greatly, an advanced Monte Carlo simulation combined with adaptive Kriging model (A-MCS-AK) is proposed by strategy of multi-training-point at one updating and candidate sample pool reduction. For two RBDO models of respectively maximizing average expectation of LCFL and minimizing failure probability of LCFL of the turbine shaft, a quasi-sequential decoupling method is presented by combining cooperatively adaptive surrogate. The analysis results of the turbine shaft show that the parameterized platform completes the automatic and orderly transmission of data, and the proposed A-MCS-AK is more efficient than traditional adaptive Kriging combined with MCS (AK-MCS) for reliability analysis. The solutions of two RBDO models of the turbine shaft LCFL show that cooperatively adaptive surrogate strategy can improve the efficiency of solving RBDO under acceptable precision, and the average LCFL and the reliability can be improved simultaneously.