Dynamic Meta-Modeling Method to Assess Stochastic Flutter Behavior in Turbomachinery

With increasing design demands of turbomachinery, stochastic flutter behavior has become more prominent and even appears a hazard to reliability and safety. Stochastic flutter assessment is an effective measure to quantify the failure risk and improve aeroelastic stability. However, for complex turbomachinery with multiple dynamic influencing factors (i.e., aeroengine compressor with time-variant loads), the stochastic flutter assessment is hard to be achieved effectively, since large deviations and inefficient computing will be incurred no matter considering influencing factors at a certain instant or the whole time domain. To improve the assessing efficiency and accuracy of stochastic flutter behavior, a dynamic meta-modeling approach (termed BA-DWTR) is presented with the integration of bat algorithm (BA) and dynamic wavelet tube regression (DWTR). The stochastic flutter assessment of a typical compressor blade is considered as one case to evaluate the proposed approach with respect to condition variabilities and load fluctuations. The evaluation results reveal that the compressor blade has 0.95% probability to induce flutter failure when operating 100% rotative rate at t = 170 s. The total temperature at rotor inlet and dynamic operating loads (vibrating frequency and rotative rate) are the primary sensitive parameters on flutter failure probability. By method comparisons, the presented approach is validated to possess high-accuracy and high-efficiency in assessing the stochastic flutter behavior for turbomachinery.