Computational framework for multiaxial fatigue life prediction of compressor discs considering notch effects. (15th October 2018)

• Record Type: Journal Article
• Authors: Liao, Ding; Zhu, Shun-Peng; Correia, José A.F.O.; De Jesus, Abílio M.P.; Calçada, Rui
• Is Part Of: Engineering fracture mechanics. Volume 202(2018)
• Page Start: 423
• Page End: 435
• Publication Date: 2018-10-15
• Subjects: Multiaxial fatigue -- Critical plane -- Notch -- Stress gradient -- Life prediction; Fracture mechanics -- Periodicals; Rupture, Mécanique de la -- Périodiques; Fracture mechanics; Periodicals; 620.112605
• DOI: 10.1016/j.engfracmech.2018.08.009
• Languages: English
• ISSNs: 0013-7944

Abstract

Highlights: A stress gradient-based factor for notch effect correction under multiaxial stress states. A critical plane damage parameter considering stress gradient correction. A computational framework for multiaxial fatigue life prediction considering notch effects. Proposed solution yields accurate correlations of both smooth/notched specimens fatigue data. Abstract: Aero engine components like compressor discs normally operate under harsh conditions like complex multiaxial stress states. Notch effect is often critical for structural integrity assessment in virtue of complex structure and discontinuities. According to the notch effect under cyclic loadings, a computational framework for multiaxial fatigue analysis of compressor discs is established by coupling finite element (FE) simulation of stress gradient with Fatemi-Socie (FS) criterion. Specifically, a notch support extension method accounting for stress gradient effect is elaborated through elasto-plastic FE analysis, which can be determined for fatigue life prediction of arbitrary shaped components. Experimental fatigue data for smooth and notched specimens of TC4 and GH4169 alloys demonstrated the appropriateness of the proposed computational approach. The applicability and performance of the prediction model to a compressor blade-disc attachment subjected to field spectra is presented. Results show that testing effect can be significantly reduced by using this framework with acceptable prediction accuracy.