Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk-Reference-Cited by-同舟云学术

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Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk

Published:2023-10 Issue: Volume: Page:
ISSN:2212-540X
Container-title:Propulsion and Power Research
language:en
Short-container-title:Propulsion and Power Research

Author:

Fei Cheng-Wei,Han Yao-Jia,Wen Jiong-Ran,Li Chen,Han Lei,Choy Yat-Sze

Publisher

Elsevier BV

Subject

Fluid Flow and Transfer Processes,Mechanical Engineering,Fuel Technology,Aerospace Engineering,Automotive Engineering

Reference52 articles.

1. Dynamic parametric modeling-based model updating strategy of aeroengine casings;Fei;Chin. J. Aeronaut.,2021

2. An, Moving extremum surrogate modeling strategy for dynamic reliability estimation of turbine blisk with multi-physics fields;Lu;Aero. Sci. Technol.,2020

3. Mathematical simulation of low cycle fatigue of high-loaded engine parts;Temis;Propuls. Power Res.,2018

4. Contact stress analysis and fatigue life prediction of turbine disc–blade attachment with fir–tree tenon structure;Yuan;Fatig. Fract. Eng. Mater. Struct.,2021

5. Fatigue life prediction of centrifugal fan blades in the ventilation cooling system of the high-speed-train;He;Eng. Fail. Anal.,2021

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