Fatigue Life Prediction Under Thermal-Mechanical Loading in a Nickel-Base Superalloy

Author(s):  
L Rémy ◽  
H Bernard ◽  
JL Malpertu ◽  
F Rezai-Aria
2016 ◽  
Vol 40 (2) ◽  
pp. 777-787 ◽  
Author(s):  
A. García de la Yedra ◽  
J. L. Pedrejón ◽  
A. Martín-Meizoso ◽  
R. Rodríguez

Author(s):  
Björn Buchholz ◽  
Uwe Gampe ◽  
Tilmann Beck

The growing share of power generation from volatile sources such as wind and photovoltaics requires fossil fuel fired power generation units be available and capable of high load flexibility to adjust to the changing capacity of the electrical grid. Additionally, back-up units with quick start capability and energy storage technologies are needed to fill the power shortfall when volatile sources are not available. Gas turbine and combined-cycle gas and steam turbine power plants are able to meet these demands. However, safe component design for improved cycling capability, combined with optimum utilization of material regarding its mechanical properties, requires design procedures and lifing models for the complex loadings resulting from this increased volatility of power demand. Since hot gas path components like turbine blades and vanes are highly stressed by cyclic thermal and mechanical loadings, resulting Thermo-Mechanical Fatigue (TMF), life prediction models such as the classic strain-life Coffin-Manson-Basquin method do not capture the influences of thermal cycling satisfyingly. Advanced TMF prediction models are thus necessary to accurately predict the durability of hot section components. This paper addresses life prediction of the Nickel-base superalloy René 80 at elevated temperature for various loading conditions. For this purpose, isothermal Low Cycle Fatigue (LCF) and corresponding TMF tests, with various temperature ranges and thermal-mechanical phase shifts, have been performed. On this basis, a systematic approach has been developed which allows assessing the key influences on TMF life. Moreover, a generalized model for fatigue has been derived, which has the potential to predict TMF life on the basis of LCF data. The knowledge gained from the model development allows an improved life prediction and better utilization of the material capabilities. Additionally, the required number of material tests for a general insight in the materials behaviour can be reduced significantly.


2014 ◽  
Vol 3 (2) ◽  
pp. 20130049 ◽  
Author(s):  
Robert L. Amaro ◽  
Stephen D. Antolovich ◽  
Richard W. Neu ◽  
Benjamin S. Adair ◽  
Michael R. Hirsch ◽  
...  

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