Active Kriging-Based Adaptive Importance Sampling for Reliability and Sensitivity Analyses of Stator Blade Regulator

The reliability and sensitivity analyses of stator blade regulator usually involve complex characteristics like highnonlinearity,multi-failure regions,and small failure probability,which brings in unacceptable computing efficiency and accuracy of the current analysismethods.In this case,by fitting the implicit limit state function(LSF)with active Kriging(AK)model and reducing candidate sample poolwith adaptive importance sampling(AIS),a novel AK-AIS method is proposed.Herein,theAKmodel andMarkov chainMonte Carlo(MCMC)are first established to identify the most probable failure region(s)(MPFRs),and the adaptive kernel density estimation(AKDE)importance sampling function is constructed to select the candidate samples.With the best samples sequentially attained in the reduced candidate samples and employed to update the Kriging-fitted LSF,the failure probability and sensitivity indices are acquired at a lower cost.The proposed method is verified by twomulti-failure numerical examples,and then applied to the reliability and sensitivity analyses of a typical stator blade regulator.Withmethods comparison,the proposed AK-AIS is proven to hold the computing advantages on accuracy and efficiency in complex reliability and sensitivity analysis problems.

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.Bymethod comparisons,the presented approach is validated to possess high-accuracy and highefficiency in assessing the stochastic flutter behavior for turbomachinery.

Macro-phenomenological high-strain-rate elastic fracture model for ice-impact simulations

The ice impact can cause a severe damage to an aircraft’s exposed structure,thus,requiring its prevention.The numerical simulation represents an effective method to overcome this challenge.The establishment of the ice material model is critical.However,ice is not a common structural material and exhibits an extremely complex material behavior.The material models of ice reported so far are not able to accurately simulate the ice behavior at high strain rates.This study proposes a novel high-precision macro-phenomenological elastic fracture model based on the brittle behavior of ice at high strain rates.The developed model has been compared with five reported models by using the smoothed particle hydrodynamics method so as to simulate the ice-impact process with respect to the impact speeds and ice shapes.The important metrics and phenomena(impact force history,deformation and fragmentation of the ice projectile and deflection of the target)were compared with the experimental data reported in the literature.The findings obtained from the developed model are observed to be most consistent with the experimental data,which demonstrates that the model represents the basic physics and phenomena governing the ice impact at high strain rates.The developed model includes a relatively fewer number of material parameters.Further,the used parameters have a clear physical meaning and can be directly obtained through experiments.Moreover,no adjustment of any material parameter is needed,and the consumption duration is also acceptable.These advantages indicate that the developed model is suitable for simulating the iceimpact process and can be applied for the anti-ice impact design in aviation.

Extremum selection method of random variable for nonlinear dynamic reliability analysis of turbine blade deformation

To effectively select random variable in nonlinear dynamic reliability analysis,the extremum selection method(ESM)is proposed.Firstly,the basic idea was introduced and the mathematical model was established for the ESM.The nonlinear dynamic reliability analysis of turbine blade radial deformation was taken as an example to verify the ESM.The results show that the analysis precision of the ESM is 99.972%,which is almost kept consistent with that of the Monte Carlo method;moreover,the computing time of the ESM is shorter than that of the traditional method.Hence,it is demonstrated that the ESM is able to save calculation time and improve the computational efficiency while keeping the calculation precision for nonlinear dynamic reliability analysis.The present study provides a method to enhance the nonlinear dynamic reliability analysis in selecting the random variables and offers a way to design structure and machine in future work.