Predictions of long-term creep life for the family of 9–12 wt% Cr martensitic steels

Abstract Two contemporary power-law creep methodologies for predicting long-term creep life from short-term lab-based experiments were extended to 48 different alloy compositions in the family of 9–12 wt% Cr steels. The study highlights the limitations that the data imposes on the applicability of the two methods for predicting lifetimes, while also assessing the assumptions made by both methods. It was found that a dependency exists between the creep activation energy and the test conditions when activation energy was calculated using a broader range of normalized stress states ( σ / σ TS ) than were previously explored. This contradicts the assumptions of constant activation energy made by both contemporary methods. This raises questions about both the physical interpretation of activation energy and the reliability of the extrapolation of lifetime predictions made. The paper highlights that the modified power-laws are empirical tools and illustrates the importance of study protocol design for data collection, when short-term experiments are limited to a maximum of 6,000 h. In addition, the effects of alloying additions and tempering temperature on the creep mechanism were explored.

[1]  V. Muggeo Estimating regression models with unknown break‐points , 2003, Statistics in medicine.

[2]  M. Muggeo,et al.  segmented: An R package to Fit Regression Models with Broken-Line Relationships , 2008 .

[3]  F. C. Monkman An Empirical Relationship between Rupture Life and Minimum Creep Rate in Creep Rupture Tests , 1956 .

[4]  P. J. Scharning,et al.  A new methodology for analysis of creep and creep fracture data for 9–12% chromium steels , 2008 .

[5]  Fujimitsu Masuyama,et al.  History of Power Plants and Progress in Heat Resistant Steels , 2001 .

[6]  Fujio Abe,et al.  Precipitate design for creep strengthening of 9% Cr tempered martensitic steel for ultra-supercritical power plants , 2008, Science and technology of advanced materials.

[7]  S. Purushothaman,et al.  Understanding the Larson-Miller parameter , 1977 .

[8]  Wei Liu,et al.  Effect of tempering temperature on the toughness of 9Cr–3W–3Co martensitic heat resistant steel , 2014 .

[9]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[10]  M. Delignette-Muller,et al.  fitdistrplus: An R Package for Fitting Distributions , 2015 .

[11]  P. J. Scharning,et al.  Long-term creep life prediction for a high chromium steel , 2007 .

[12]  Mark Whittaker,et al.  The changing constants of creep: A letter on region splitting in creep lifing , 2015 .

[13]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[14]  Stuart Holdsworth,et al.  Creep and fracture in high-temperature components—Design and life assessment issues , 2008 .

[15]  Z. Xiang,et al.  On the physical models for predicting the long-term creep strengths and lifetimes of modified 9Cr-1Mo steel , 2017 .

[16]  Mark Evans,et al.  Long-term creep data prediction for type 316H stainless steel , 2012 .

[17]  K. Maruyama,et al.  Influence of Data Analysis Method and Allowable Stress Criterion on Allowable Stress of Gr.122 Heat Resistant Steel , 2007 .