Fatigue design curve under LCF as well as combined LCF and HCF regime at 923 K in a type 316LN stainless steel

Sarkar, Aritra; Nagesha, A.

doi:10.1111/ffe.13010

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…With this method, runout specimens, neglected if the unknown parameters of the Coffin Manson and Morrow model (Equation 1) are estimated only by applying the least square method, 9 are considered, preventing the loss of the important information they contain. Indeed, even if the strain‐based approaches are generally adopted for the analysis of strain‐controlled LCF fatigue tests, they also properly work for experimental data covering the LCF‐HCF life range 20,21,29 and thus with possible runout specimens. Figure 1 shows a flow chart of the described methodology.…”

Section: Strain Life Approach: Methodsmentioning

confidence: 99%

LCF‐HCF strain–life model: Statistical distribution and design curves based on the maximum likelihood principle

Tridello

Paolino

2023

Fatigue Fract Eng Mat Struct

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In the paper, the statistical distribution of the total strain and the fatigue life in the low cycle fatigue (LCF)–high cycle fatigue (HCF) life range is analytically derived starting from the Coffin–Manson and Morrow model. The maximum likelihood principle is exploited for parameter estimation, thus allowing to consider both failures and runout specimens. A straightforward procedure for the estimation of the design curves based on the likelihood ratio confidence lower bound has been also developed. The proposed model has been validated with literature datasets obtained by testing steel and aluminum alloys, proving its effectiveness and representing a reliable alternative to the currently adopted literature models.

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Section: Strain Life Approach: Methodsmentioning

confidence: 99%

LCF‐HCF strain–life model: Statistical distribution and design curves based on the maximum likelihood principle

Tridello

Paolino

2023

Fatigue Fract Eng Mat Struct

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“…Figure 1 depicts the load spectrum applicable to turbine shaft fatigue test, showcasing the operational loading conditions including axial force F, torque T, and bending moment M. It is well-established that the fatigue life of materials or structures under CCF loading is substantially reduced compared to that under equivalent pure LCF or HCF loading. 35 Consequently, in the fatigue life assessment of turbine shafts, it is imperative to account for the interaction between HCF and LCF. The primary parameters defining the CCF load spectrum are the loading frequency ratio of HCF to LCF and the stress amplitude ratio of HCF to LCF 14,15,21 :…”

Section: The Framework For Fatigue Life Prediction Of Turbine Shaftsmentioning

confidence: 99%

A reliability analysis and optimization method for a turbine shaft under combined high and low cycle fatigue loading

Bai,

Zeng,

Huang

et al. 2024

Quality & Reliability Eng

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The combined high and low cycle fatigue (CCF) loading condition and random uncertainty exert a considerable impact on the design of turbine shafts. To enhance the fatigue life and reliability, this research proposes a CCF reliability analysis and optimization method for turbine shafts. A CCF fatigue reliability analysis framework is established, which focuses on the quantification of CCF loading characteristics and random uncertainty. The consideration of CCF loading characteristics contain the loading frequency ratio of high cycle fatigue (HCF) to low cycle fatigue (LCF), the stress amplitude ratio of HCF to LCF, as well as their interaction. The consideration of random uncertainty contains material, geometry and load, and a surrogate model‐based method is introduced to improve the quantification efficiency. Through the validation by comparing with experimental data and traditional methods, the proposed method is with higher accuracy and efficiency. By integrating the proposed fatigue reliability analysis method with design optimization, optimal design values for the turbine shaft were identified. This method theoretically extends the shaft's CCF life and provides practical engineering guidance for its reliability analysis and design.

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“…Yue et al (2021) developed a life prediction method to consider the interaction of CCF without any additional material constants and proposed a new nonlinear damage accumulation model. Sarkar and Nagesha (2019) investigated the effect of different damage modes in a type 316LN austenitic stainless steel. They (Sarkar et al , 2017) presented a method toward developing a fatigue design curve under CCF loading based on the obtained data.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic fatigue life prediction of an aero-engine turbine shaft

Huang

et al. 2022

AEAT

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Purpose Aero-engine components endure combined high and low cycle fatigue (CCF) loading during service, which has attracted more research attention in recent years. This study aims to construct a new framework for the prediction of probabilistic fatigue life and reliability evaluation of an aero-engine turbine shaft under CCF loading if considering the material uncertainty. Design/methodology/approach To study the CCF failure of the aero-engine turbine shaft, a CCF test is carried out. An improved damage accumulation model is first introduced to predict the CCF life and present high prediction accuracy in the CCF loading situation based on the test. Then, the probabilistic fatigue life of the turbine shaft is predicted based on the finite element analysis and Monte Carlo analysis, where the material uncertainty is taken into account. At last, the reliability evaluation of the turbine shaft is conducted by stress-strength interference models based on an improved damage accumulation model. Findings The results indicate that predictions agree well with the tested data. The improved damage accumulation model can accurately predict the CCF life because of interaction damage between low cycle fatigue loading and high cycle fatigue loading. As a result, a framework is available for accurate probabilistic fatigue life prediction and reliability evaluation. Practical implications The proposed framework and the presented testing in this study show high efficiency on probabilistic CCF fatigue life prediction and can provide technical support for fatigue optimization of the turbine shaft. Originality/value The novelty of this work is that CCF loading and material uncertainty are considered in probabilistic fatigue life prediction.

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Fatigue design curve under LCF as well as combined LCF and HCF regime at 923 K in a type 316LN stainless steel

Cited by 10 publications

References 17 publications

LCF‐HCF strain–life model: Statistical distribution and design curves based on the maximum likelihood principle

LCF‐HCF strain–life model: Statistical distribution and design curves based on the maximum likelihood principle

A reliability analysis and optimization method for a turbine shaft under combined high and low cycle fatigue loading

Probabilistic fatigue life prediction of an aero-engine turbine shaft

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