Distributed Collaborative Response Surface Method for Mechanical Dynamic Assembly Reliability Design

作     者：BAI Guangchen FEI Chengwei 

作者机构：School of Energy and Power Engineering Beihang University 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2013年第26卷第6期

页      面：1160-1168页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

基　　金：supported by National Natural Science Foundation of China(Grant Nos.51175017,51245027) Innovation Foundation of Beihang University for PhD Graduates,China(Grant No.YWF-12-RBYJ008) Research Fund for the Doctoral Program of Higher Education of China(Grant No.20111102110011) 

主　　题：machinery dynamic assembly reliability analysis distributed collaborative response surface method,blade-tip radial running clearance 

摘      要：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 100 000 times, 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 under 10000 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.