Modeling of fatigue fracture under stationary stochastic loading conditions

Kostreva, Michael M.; Talybly, Latif; Viktorova, Irina

doi:10.1016/j.amc.2006.06.078

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…, 2017). Recently, relevant studies have been conducted to develop probabilistic models to evaluate fatigue problems in engineering (Kostreva et al ., 2007; Figiel and Kamiński, 2009; Narayanan, 2020). Hu et al .…”

Section: Introductionmentioning

confidence: 99%

“…Fatigue life of engineering components varies significantly depending on multisource uncertainties, thus effective probabilistic methods are highly expected to be developed (Wang et al, 2017;You et al, 2017). Recently, relevant studies have been conducted to develop probabilistic models to evaluate fatigue problems in engineering (Kostreva et al, 2007;Figiel and Kami nski, 2009;Narayanan, 2020). Hu et al (2012Hu et al ( , 2016 raised a probabilistic analysis aiming to fatigue crack growth of nickel-based alloy GH4133B, where the reliability analysis on the crack growth rate is accomplished with a combined approach of response surface and Monte Carlo simulation method.…”

Section: Introductionmentioning

confidence: 99%

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Machine learning-based probabilistic fatigue assessment of turbine bladed disks under multisource uncertainties

Zhu,

Niu,

Keshtegar

et al. 2023

IJSI

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PurposeThe 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/approachIn 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.FindingsThe 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/valueThe 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.

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