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| 1 | Distributed Collaborative Response Surface Method for Mechanical Dynamic Assembly Reliability Design显示文摘Because of the randomness of many impact factors influencing the dynamic assembly relationship of complex machinery,the reliability analysis of dynamic assembly relationship needs to be accomplished considering the randomness from a probabilistic perspective.To improve the accuracy and efficiency of dynamic assembly relationship reliability analysis,the mechanical dynamic assembly reliability(MDAR)theory and a distributed collaborative response surface method(DCRSM)are proposed.The mathematic model of DCRSM is established based on the quadratic response surface function,and verified by the assembly relationship reliability analysis of aeroengine high pressure turbine(HPT)blade-tip radial running clearance(BTRRC).Through the comparison of the DCRSM,traditional response surface method(RSM)and Monte Carlo Method(MCM),the results show that the DCRSM is not able to accomplish the computational task which is impossible for the other methods when the number of simulation is more than 100 000times,but also the computational precision for the DCRSM is basically consistent with the MCM and improved by 0.40~4.63%to the RSM,furthermore,the computational efficiency of DCRSM is up to about 188 times of the MCM and 55 times of the RSM under10000 times simulations.The DCRSM is demonstrated to be a feasible and effective approach for markedly improving the computational efficiency and accuracy of MDAR analysis.Thus,the proposed research provides the promising theory and method for the MDAR design and optimization,and opens a novel research direction of probabilistic analysis for developing the high-performance and high-reliability of aeroengine. | BAI Guangchen FEI Chengwei | 2013 | Chinese Journal of Mechanical Engineering2013，26，6： | 23 |
| 2 | New Neural Network Response Surface Methods for Reliability Analysis显示文摘This article presents two new kinds of artificial neural network (ANN) response surface methods (RSMs): the ANN RSM based on early stopping technique (ANNRSM-1), and the ANN RSM based on regularization theory (ANNRSM-2). The following improvements are made to the conventional ANN RSM (ANNRSM-0): 1) by monitoring the validation error during the training process, ANNRSM-1 determines the early stopping point and the training stopping point, and the weight vector at the early stopping point, which corresponds to the ANN model with the optimal generalization, is finally returned as the training result; 2) according to the regularization theory, ANNRSM-2 modifies the conventional training performance function by adding to it the sum of squares of the network weights, so the network weights are forced to have smaller values while the training error decreases. Tests show that the performance of ANN RSM becomes much better due to the above-mentioned improvements: first, ANNRSM-1 and ANNRSM-2 approximate to the limit state function (LSF) more accurately than ANNRSM-0; second, the estimated failure probabilities given by ANNRSM-1 and ANNRSM-2 have smaller errors than that obtained by ANNRSM-0; third, compared with ANNRSM-0, ANNRSM-1 and ANNRSM-2 require much fewer data samples to achieve stable failure probability results. | REN Yuan BAI Guangchen | 2011 | Chinese Journal of Aeronautics2011，24，1： | 18 |
| 3 | Application of Improved Hybrid Interface Substructural Component Modal Synthesis Method in Vibration Characteristics of Mistuned Blisk显示文摘The large and complex structures are divided into hundreds of thousands or millions degrees of freedom(DOF) when they are calculated which will spend a lot of time and the efficiency will be extremely low. The classical component modal synthesis method(CMSM) are used extensively, but for many structures in the engineering of high-rise buildings, aerospace systemic engineerings, marine oil platforms etc, a large amount of calculation is still needed. An improved hybrid interface substructural component modal synthesis method(HISCMSM) is proposed. The parametric model of the mistuned blisk is built by the improved HISCMSM. The double coordinating conditions of the displacement and the force are introduced to ensure the computational accuracy. Compared with the overall structure finite element model method(FEMM), the computational time is shortened by 23.86%–31.56% and the modal deviation is 0.002%–0.157% which meets the requirement of the computational accuracy. It is faster 4.46%–10.57% than the classical HISCMSM. So the improved HISCMSM is better than the classical HISCMSM and the overall structure FEMM. Meanwhile, the frequency and the modal shape are researched, considering the factors including rotational speed, gas temperature and geometry size. The strong localization phenomenon of the modal shape's the maximum displacement and the maximum stress is observed in the second frequency band and it is the most sensitive in the frequency veering. But the localization phenomenon is relatively weak in 1st and the 3d frequency band. The localization of the modal shape is more serious under the condition of the geometric dimensioning mistuned. An improved HISCMSM is proposed, the computational efficiency of the mistuned blisk can be increased observably by this method. | BAI Bin BAI Guangchen LI Chao | 2014 | Chinese Journal of Mechanical Engineering2014，27，6： | 5 |
| 4 | Extremum selection method of random variable for nonlinear dynamic reliability analysis of turbine blade deformation显示文摘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. | Chengwei Fein Guangchen Bai | 2012 | Propulsion and Power Research2012，1，1： | 3 |
| 5 | Application of least squaressupport vector machine for regression to reliability analysis显示文摘 | Guo Zhiwei Bai Guangchen | 2009 | Chinese Journal of Aeronautics2009，22，： | 1 |
| 6 | Determination of optimal SVM parameters by using GA/PSO显示文摘 | Pen Yuan Bai Guangchen | 2010 | Journal of Computers2010，5，8： | 1 |
| 7 | Macro-phenomenological high-strain-rate elastic fracture model for ice-impact simulations显示文摘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. | Jiang FAN Qinghao YUAN Fulei JING Guangchen BAI Xiuli SHEN | 2023 | Chinese Journal of Aeronautics2023，36，3： | 0 |
| 8 | Active Kriging-Based Adaptive Importance Sampling for Reliability and Sensitivity Analyses of Stator Blade Regulator显示文摘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. | Hong Zhang Lukai Song Guangchen Bai | 2023 | Computer Modeling in Engineering & Sciences2023，，3： | 0 |
| 9 | Dynamic Meta-Modeling Method to Assess Stochastic Flutter Behavior in Turbomachinery显示文摘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. | Bowei Wang Wenzhong Tang Lukai Song Guangchen Bai | 2022 | Computer Modeling in Engineering & Sciences2022，，10： | 0 |
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