Open Access

ARTICLE

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

Xue-Qin Li¹, Lu-Kai Song^2,3,*

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

* Corresponding Author: Lu-Kai Song. Email:

(This article belongs to the Special Issue: Computer-Aided Uncertainty Modeling and Reliability Evaluation for Complex Engineering Structures)

Computer Modeling in Engineering & Sciences 2024, 140(1), 665-684. https://doi.org/10.32604/cmes.2024.048445

Received 08 December 2023; Accepted 23 February 2024; Issue published 16 April 2024

Abstract

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.

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Keywords

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