涡轮叶片寿命可靠性优化的类序列解耦法

Quasi-sequence decoupling method for life reliability optimization of turbine blades

针对含气膜孔冷却涡轮叶片多模式寿命可靠性优化设计效率与精度兼顾困难的问题,提出了基于自适应克里金代理模型的类序列解耦可靠性优化方法。该方法中,可靠性约束里的极限状态面代理模型构建过程会随着设计参数的搜索迭代而实时更新,并严格保证每个优化步下的代理精度和可行域判断的准确性,因此收敛快、稳健性强。代理模型在扩展空间中协作代理并共用训练样本点,实时自学习训练目标函数和约束函数的代理模型至收敛,在保证代理精度的同时显著提高优化效率。此外,基于所提方法开发了一体化、自动化的寿命可靠性优化集成仿真软件,并完成了涡轮叶片寿命可靠性优化设计的工程化验证,该结果验证了所提方法的高效性及工程适应性。

It was difficult to balance the efficiency and accuracy of multi-mode life RBDO(reliability-based design optimization)of turbine blades with film holes in the presence of random uncertainty,a quasi-sequence decoupling method of RBDO based on adaptive Kriging surrogate model was proposed.The construction process of the limit state surface surrogate model in reliability constraint was updated in real time with the search iteration of the design parameters,and the surrogate model strictly ensured the accuracy of surrogate model and feasible region judgment in each iteration step.The proposed method avoided updating the limit state surface in non-access domain of design parameters,so that obtaining a high convergence speed and strong robustness.The embedded real-time update strategy builds a cooperative surrogate model in the extended space and shares training sample points,and adaptively trains the Kriging model of the objective function until convergence,so that it is able to ensure the surrogate accuracy and significantly improve the optimization efficiency.In addition,an integrated and automatic simulation system for life reliability optimization is developed,which verifies the high efficiency and engineering feasibility of the proposed method and software in the turbine blade life RBDO problem.