Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures

作     者：Xue-Qin Li Lu-Kai Song 

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：08[工学] 082502[工学-航空宇航推进理论与工程] 0825[工学-航空宇航科学与技术] 0701[理学-数学] 

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 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.