Machine learning-based probabilistic fatigue assessment of turbine bladed disks under multisource uncertainties
International Journal of Structural Integrity
ISSN: 1757-9864
Article publication date: 7 November 2023
Issue publication date: 14 November 2023
Abstract
Purpose
The multisource uncertainties, including material dispersion, load fluctuation and geometrical tolerance, have crucial effects on fatigue performance of turbine bladed disks. In view of the aim of this paper, it is essential to develop an advanced approach to efficiently quantify their influences and evaluate the fatigue life of turbine bladed disks.
Design/methodology/approach
In this study, a novel combined machine learning strategy is performed to fatigue assessment of turbine bladed disks. Proposed model consists of two modeling phases in terms of response surface method (RSM) and support vector regression (SVR), namely RSM-SVR. Two different input sets obtained from basic variables were used as the inputs of RSM, then the predicted results by RSM in first phase is used as inputs of SVR model by using a group data-handling strategy. By this way, the nonlinear flexibility of SVR inputs is improved and RSM-SVR model presents the high-tendency and efficiency characteristics.
Findings
The accuracy and tendency of the RSM-SVR model, applied to the fatigue life estimation of turbine bladed disks, are validated. The results indicate that the proposed model is capable of accurately simulating the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction strategy compared to RSM and SVR for fatigue analysis of complex structures.
Originality/value
The results indicate that the proposed model is capable of accurately simulate the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction compared to RSM and SVRE for fatigue analysis of turbine bladed disk.
Keywords
Acknowledgements
Financial support of the National Natural Science Foundation of China (No. 11972110), Sichuan Science and Technology Program (No. 2022JDJQ0024 and 2023YFG0166), and University of Zabol (IR-UOZ-GR-2117) are acknowledged.
Citation
Zhu, S.-P., Niu, X., Keshtegar, B., Luo, C. and Bagheri, M. (2023), "Machine learning-based probabilistic fatigue assessment of turbine bladed disks under multisource uncertainties", International Journal of Structural Integrity, Vol. 14 No. 6, pp. 1000-1024. https://doi.org/10.1108/IJSI-06-2023-0048
Publisher
:Emerald Publishing Limited
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