Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures
作者机构：School of Energy and Power EngineeringBeihang UniversityBeijing100191China Department of Mechanical EngineeringThe Hong Kong Polytechnic UniversityKowloonHong Kong999077China Research Institute of Aero-EngineBeihang UniversityBeijing100191China
出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)
年 卷 期：2024年第140卷第7期
页 面：665-684页
核心收录：
学科分类：082502[工学-航空宇航推进理论与工程] 08[工学] 0825[工学-航空宇航科学与技术]
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)
主 题：Random forest reliability-based design optimization ensemble learning machine learning
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 *** 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 *** the low-cycle fatigue reliability-based design optimization of aeroengine turbine disc as a case,the effectiveness of the presented framework is *** reliabilitybased design optimization results exhibit that the proposed framework holds high computing accuracy and computing *** current efforts shed a light on the theory/method development of reliability-based design optimization of complex engineering structures.
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