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Thermodynamic Entropy-Based Fatigue Life Assessment Method for Nickel-Based Superalloy GH4169 at Elevated Temperature Considering Cyclic Viscoplasticity.
Ding, Shuiting; Xia, Shuyang; Li, Zhenlei; Zhou, Huimin; Bao, Shaochen; Li, Bolin; Li, Guo.
Afiliação
  • Ding S; School of Energy and Power Engineering, Beihang University, Beijing 100191, China.
  • Xia S; School of Energy and Power Engineering, Beihang University, Beijing 100191, China.
  • Li Z; Research Institute of Aero-Engine, Beihang University, Beijing 100191, China.
  • Zhou H; Research Institute of Aero-Engine, Beihang University, Beijing 100191, China.
  • Bao S; Research Institute of Aero-Engine, Beihang University, Beijing 100191, China.
  • Li B; School of Energy and Power Engineering, Beihang University, Beijing 100191, China.
  • Li G; School of Energy and Power Engineering, Beihang University, Beijing 100191, China.
Entropy (Basel) ; 26(5)2024 Apr 30.
Article em En | MEDLINE | ID: mdl-38785642
ABSTRACT
This paper develops a thermodynamic entropy-based life prediction model to estimate the low-cycle fatigue (LCF) life of the nickel-based superalloy GH4169 at elevated temperature (650 °C). The gauge section of the specimen was chosen as the thermodynamic system for modeling entropy generation within the framework of the Chaboche viscoplasticity constitutive theory. Furthermore, an explicitly numerical integration algorithm was compiled to calculate the cyclic stress-strain responses and thermodynamic entropy generation for establishing the framework for fatigue life assessment. A thermodynamic entropy-based life prediction model is proposed with a damage parameter based on entropy generation considering the influence of loading ratio. Fatigue lives for GH4169 at 650 °C under various loading conditions were estimated utilizing the proposed model, and the results showed good consistency with the experimental results. Finally, compared to the existing classical models, such as Manson-Coffin, Ostergren, Walker strain, and SWT, the thermodynamic entropy-based life prediction model provided significantly better life prediction results.
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Texto completo: 1 Temas: ECOS / Aspectos_gerais Bases de dados: MEDLINE Idioma: En Revista: Entropy (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Temas: ECOS / Aspectos_gerais Bases de dados: MEDLINE Idioma: En Revista: Entropy (Basel) Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China