2019
DOI: 10.1016/j.engfracmech.2019.04.028
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Modeling crack growth of an aircraft engine high pressure compressor blade under combined HCF and LCF loading

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Cited by 44 publications
(12 citation statements)
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“…Chapter 3 of this work will present a distinct numerical approach that was developed and used to combine steady and dynamic stresses as a basis of simulating 3D crack growth of components such as IBR compressor blades when subjected to HCF-LCF mixed non proportional loads. This work was recently published in Engineering Fracture Mechanics, titled under 'Modeling crack growth of an aircraft engine high pressure compressor blade under combined HCF and LCF loading' [12]. The method that was developed in [12] was used to successfully calibrate the direction, shape, and aspect ratio of the simulation with actual field components which fractured in service and can be extended on other IBRs and loading conditions.…”
Section: Rotorsmentioning
confidence: 99%
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“…Chapter 3 of this work will present a distinct numerical approach that was developed and used to combine steady and dynamic stresses as a basis of simulating 3D crack growth of components such as IBR compressor blades when subjected to HCF-LCF mixed non proportional loads. This work was recently published in Engineering Fracture Mechanics, titled under 'Modeling crack growth of an aircraft engine high pressure compressor blade under combined HCF and LCF loading' [12]. The method that was developed in [12] was used to successfully calibrate the direction, shape, and aspect ratio of the simulation with actual field components which fractured in service and can be extended on other IBRs and loading conditions.…”
Section: Rotorsmentioning
confidence: 99%
“…This work was recently published in Engineering Fracture Mechanics, titled under 'Modeling crack growth of an aircraft engine high pressure compressor blade under combined HCF and LCF loading' [12]. The method that was developed in [12] was used to successfully calibrate the direction, shape, and aspect ratio of the simulation with actual field components which fractured in service and can be extended on other IBRs and loading conditions.…”
Section: Rotorsmentioning
confidence: 99%
See 1 more Smart Citation
“…8 The methodologies mentioned above address maturely problems in lifetime prediction of engineering structures under the assumption that those structures work under either high cycle fatigue (HCF) or low cycle fatigue (LCF) loads, which can be compared with the failure properties of the aero-engine components, especially the hot components, that failures of these components are consequences of the integration of LCF or HCF loads, say combined high and low cycle fatigue (CCF) loading. [9][10][11] Given that precise lifetime prediction of engineering structures like aero-engine hot components relies primarily on the completeness of data, the comprehensiveness of the model construction, and the comprehensiveness of the model mapping the actual features. [12][13][14] Hence, considering of CCF loading and understanding damage interactions are mandatory to fatigue life prediction of aero-engine hot components.…”
Section: Introductionmentioning
confidence: 99%
“…Consequently, a significant number of prediction models have been conducted and reported, including (but not limited to) Manson–Coffin‐based models, 6 Morrow or SWT mean stress modified models, 7 and S‐N ‐curve‐based models 8 . The methodologies mentioned above address maturely problems in lifetime prediction of engineering structures under the assumption that those structures work under either high cycle fatigue (HCF) or low cycle fatigue (LCF) loads, which can be compared with the failure properties of the aero‐engine components, especially the hot components, that failures of these components are consequences of the integration of LCF or HCF loads, say combined high and low cycle fatigue (CCF) loading 9–11 . Given that precise lifetime prediction of engineering structures like aero‐engine hot components relies primarily on the completeness of data, the comprehensiveness of the model construction, and the comprehensiveness of the model mapping the actual features 12–14 .…”
Section: Introductionmentioning
confidence: 99%