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机构地区:[1]School of Energy and Power Engineering,Beihang University,Beijing,100191,China [2]Department of Mechanical Engineering,The Hong Kong Polytechnic University,Kowloon,Hong Kong,999077,China [3]Research Institute of Aero-Engine,Beihang University,Beijing,100191,China
出 处:《Computer Modeling in Engineering & Sciences》2024年第7期665-684,共20页工程与科学中的计算机建模(英文)
基 金:supported by the National Natural Science Foundation of China under Grant(Number:52105136);the Hong Kong Scholar program under Grant(Number:XJ2022013)China Postdoctoral Science Foundation under Grant(Number:2021M690290);Academic Excellence Foundation of BUAA under Grant(Number:BY2004103).
摘 要:Fatigue reliability-based design optimization of aeroengine structures involves multiple repeated calculations of reliability degree and large-scale calls of implicit high-nonlinearity limit state function,leading to the traditional direct Monte Claro and surrogate methods prone to unacceptable computing efficiency and accuracy.In this case,by fusing the random subspace strategy and weight allocation technology into bagging ensemble theory,a random forest(RF)model is presented to enhance the computing efficiency of reliability degree;moreover,by embedding the RF model into multilevel optimization model,an efficient RF-assisted fatigue reliability-based design optimization framework is developed.Regarding the low-cycle fatigue reliability-based design optimization of aeroengine turbine disc as a case,the effectiveness of the presented framework is validated.The reliabilitybased design optimization results exhibit that the proposed framework holds high computing accuracy and computing efficiency.The current efforts shed a light on the theory/method development of reliability-based design optimization of complex engineering structures.
关 键 词:Random forest reliability-based design optimization ensemble learning machine learning
分 类 号:V23[航空宇航科学与技术—航空宇航推进理论与工程]
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