A probabilistic framework to define the design stress and acceptable defects under combined-cycle fatigue conditions

Patriarca, L.; Beretta, S.; Foletti, S.; Riva, Andrea; Parodi, S.

doi:10.1016/j.engfracmech.2019.106784

Cited by 21 publications

(3 citation statements)

References 22 publications

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“…Kitagawa and Takahashi recommended in Reference [29] that the long life fatigue strength σ LLF can be related to a dedicated crack length a, respectively to equivalent defect size, which can be defined as equivalent circle diameter (ECD) or by the equivalent edge length of a square ( √ area), see Equation (3). The sound applicability of the model from Kitagawa and Takahashi has been proven in several studies, see References [3,4,7,[30][31][32][33][34][35][36].…”

Section: Introductionmentioning

confidence: 96%

Validation Study on the Statistical Size Effect in Cast Aluminium

Oberreiter

Pomberger

Leitner

et al. 2020

Metals

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Imperfections due to the manufacturing process can significantly affect the local fatigue strength of the bulk material in cast aluminium alloys. Most components possess several sections of varying microstructure, whereat each of them may inherit a different highly-stressed volume (HSV). Even in cases of homogeneous local casting conditions, the statistical distribution parameters of failure causing defect sizes change significantly, since for a larger highly-stressed volume the probability for enlarged critical defects gets elevated. This impact of differing highly-stressed volume is commonly referred as statistical size effect. In this paper, the study of the statistical size effect on cast material considering partial highly-stressed volumes is based on the comparison of a reference volume V 0 and an arbitrary enlarged, but disconnected volume V α utilizing another specimen geometry. Thus, the behaviour of disconnected highly-stressed volumes within one component in terms of fatigue strength and resulting defect distributions can be assessed. The experimental results show that doubling of the highly-stressed volume leads to a decrease in fatigue strength of 5% and shifts the defect distribution towards larger defect sizes. The highly-stressed volume is numerically determined whereat the applicable element size is gained by a parametric study. Finally, the validation with a prior developed fatigue strength assessment model by R. Aigner et al. leads to a conservative fatigue design with a deviation of only about 0.3% for cast aluminium alloy.

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Section: Introductionmentioning

confidence: 96%

Validation Study on the Statistical Size Effect in Cast Aluminium

Oberreiter

Pomberger

Leitner

et al. 2020

Metals

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“…As key components of aero-engines, turbine blades working in actual operation conditions are always subjected to complex cyclic loads including high cycle fatigue (HCF) load mainly generated by high-frequency vibration of airflow and low cycle fatigue (LCF) load induced by heavy centrifugal force together with temperature load during the ground-air-ground mission cycles. [1][2][3][4] Hence, turbine blades suffer from (i) LCF damage due to the highamplitude and low-frequency loads, (ii) HCF damage caused by HCF loads with small amplitude and large frequency, and (iii) the interaction damage between HCF/LCF loads. The integration of HCF and LCF, known as the combined high and low cycle fatigue (CCF), is the primary damage mode during real operation conditions instead of the single LCF or HCF, [5][6][7] which poses threats to structural reliability and safety of aeroengines.…”

Section: Introductionmentioning

confidence: 99%

A modified nonlinear cumulative damage model for combined high and low cycle fatigue life prediction

Yue,

Li,

Dong

et al. 2024

Fatigue Fract Eng Mat Struct

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Aero‐engine turbine blades are subject to combined high and low cycle fatigue (CCF) loadings during operations. In this paper, a modified nonlinear cumulative damage model is developed based on linear damage accumulation theory and static toughness exhaustion to predict the CCF life of turbine blades and common blade materials. An interaction factor is created to reflect the integrated effect of high cycle fatigue and low cycle fatigue (HCF‐LCF). Available test data from TC4 alloy and other two alloy materials, together with two turbine blades are utilized to validate the robustness and accuracy of the proposed approach. Comparative results demonstrate that the proposed model holds better performance in life predictions under CCF loadings.

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“…Conversely, if ∆K th,lc is independently retrievable through dedicated experiment, such information can act as prior knowledge, thus being extremely beneficial to achieve even more precise estimation of EH's parameters. It is important to highlight that, fatigue data of defective materials incorporates relevant epistemic and aleatoric uncertainty, thus leading to scattered fatigue response [7][8][9][10][11][12][13] . Hence, ∆K th,lc and ∆σ w urge to be probabilistically determined to account for these uncertainties.…”

Section: Introductionmentioning

confidence: 99%

B-FADE: Bayesian-FAtigue moDel Estimator in Python and its Application to the Probabilistic Estimation of El Haddad Curves

Tognan,

Salvati

2024

Preprint

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Accurate calibration of semi-empirical fatigue models against experimental evidence is a critical step for achieving reliable predictions. Amongst many semi-empirical fatigue models, El Haddad’s (EH) curve is widely exploited to characterise the fatigue endurance limit of defect-laden and cracked metallic alloys. A few deterministic computational models exist in this respect, however, they lack a robust probabilistic perspective and their implementation code is not publicly accessible. The authors of the present work have recently exploited Maximum a Posteriori (MAP) to robustly and probabilistically estimate EH's curves, even in case of data scarcity or incomplete datasets, combining experimental evidence and prior knowledge taken from literature. Whilst the implementation scheme was published, the associated code was not made available. Hereby, the authors present B-FADE, a flexible open-source Python package, aimed at releasing the implementation of the MAP approach with improvements, as well as several pre- and post-processing utilities to facilitate its deployment. The package is conferred with a sufficient level of abstraction, thus turning out to be easily extensible to future implementation of other relevant fatigue models.

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A probabilistic framework to define the design stress and acceptable defects under combined-cycle fatigue conditions

Cited by 21 publications

References 22 publications

Validation Study on the Statistical Size Effect in Cast Aluminium

Validation Study on the Statistical Size Effect in Cast Aluminium

A modified nonlinear cumulative damage model for combined high and low cycle fatigue life prediction

B-FADE: Bayesian-FAtigue moDel Estimator in Python and its Application to the Probabilistic Estimation of El Haddad Curves

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