TY  - EJOU
AU  - Li, Xue-Qin 
AU  - Song, Lu-Kai 

TI  - Random Forest-Based Fatigue Reliability-Based Design Optimization for Aeroengine Structures
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 140
IS  - 1
SN  - 1526-1506

AB  - 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 reliability-based 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.
KW  - Random forest; reliability-based design optimization; ensemble learning; machine learning

DO  - 10.32604/cmes.2024.048445