Estimation of stress intensity factor for surface cracks in the firtree groove structure of turbine disk using pool-based active learning with Gaussian Process Regression
Xiaojun Yan 2,3,4
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Research Institute of Aero-Engine, Beihang University, Beijing, China
School of Energy and Power Engineering, Beihang University, Beijing, China
Beijing Key Laboratory of Aero-Engine Structure and Strength, Beijing, China
National Key Laboratory of Science and Technology on Aero-Engine Aero-thermodynamics, Beijing, China
Submission date: 2023-08-08
Final revision date: 2023-09-08
Acceptance date: 2023-10-31
Online publication date: 2024-01-12
Publication date: 2024-01-31
Corresponding author
Dawei Huang   

School of Energy and Power Engineering, Beihang University, China
Journal of Theoretical and Applied Mechanics 2024;62(1):89-101
Calculation of the stress intensity factor K is a crucial and difficult task in linear elastic fracture mechanics. With the capacity to solve complex input-output problems of an underlying system, machine learning is especially useful in the calculation of K. However, when faced with complex systems, such as the firtree groove structure of a turbine disk, the dataconsuming issue has always been a thorny problem in K-solutions combined with machine learning studies for a long time. In this paper, a novel K-solution method called PA-GPR (Pool-based Active learning with Gaussian Process Regression) for the calculation of the stress intensity factor for surface cracks in the firtree groove structure of a turbine disk is proposed. Using the pool-based active learning strategy, the proposed K-solution method could make the GPR model have a great regression performance with a few samples required. In the pool-based active learning strategy analysis, the learning function based on greedy sampling is proposed to select samples with a high contribution to the training of the GPR model. The calculation of K for a semi-elliptical surface crack in the firtree groove structure is evaluated to verify the accuracy and effectiveness of the proposed method. The results show that this novel method is accurate, time-saving and effective.
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