Creep Parameters Determination by Omega Model to Norton Bailey Law by Regression Analysis for Austenitic Steel SS-304

Article Preview

Abstract:

In the material’s creep failure analysis, the difficulty of assessing the applied thermo-mechanical boundary conditions makes it critically important. Numerous creep laws have been established over the years to predict the creep deformation, damage evolution and rupture of the materials subjected to creep phenomena. The omega model developed by the American Petroleum Institute and Material Properties Council is one of the most commonly used creep material models for numerical analysis over the years. It is good in defining the fitness of mechanical equipment for service engineering evaluation to ensure the reliable service life of the equipment. The Omega model, however, is not readily accessible and specifically incorporated for creep evaluation in FEA software codes and creep data is always scarce for the complete analysis. Therefore, extrapolation of creep behavior was performed by fitting various types of creep models with a limited amount of creep data and then simulating them, beyond the available data points. In conjunction with the Norton Bailey model, based on API-579/ASME FFS-1 standards, a curve fitting technique was employed called regression analysis. From the MPC project omega model, different creep strain rates were obtained based on material, stress and temperature-dependent data. In addition, as the strain rates increased exponentially with the increase in stresses, regression analysis was used for predicting creep parameters, that can curve fit the data into the embedded Norton Bailey model. The uncertainties in extrapolations and material constants has highlighted to necessitate conservative safety factors for design requirement. In this case study, FEA creep assessment was performed on the material SS-304 dog bone specimen, considered as a material coupon to predict time-dependent plastic deformation along with creep behavior at elevated temperatures and under constant stresses. The results indicated that the specimen underwent secondary creep deformation for most of the period.

You might also be interested in these eBooks

Info:

Periodical:

Solid State Phenomena (Volume 324)

Pages:

188-197

Citation:

Online since:

September 2021

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2021 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] B. Dyson, Use of CDM in Materials Modeling and Component Creep Life Prediction,, (2014).

Google Scholar

[2] L. Bråthe and L. Josefson, Estimation of norton-bailey parameters from creep rupture data,, Met. Sci., vol. 13, no. 12, p.660–664, 1979,.

DOI: 10.1179/030634579790434312

Google Scholar

[3] M. Prager, Development of the MPC omega method for life assessment in the creep range,, J. Press. Vessel Technol. Trans. ASME, vol. 117, no. 2, p.95–103, 1995,.

DOI: 10.1115/1.2842111

Google Scholar

[4] ASME, American Petroleum Institute API-579, Fitness for Service,, Oper. Man., no. FFS-1, p.1320, (2016).

Google Scholar

[5] M. Prager, The Omega Method – An Engineering Approach to Life Assessment,, vol. 122, no. August 2000, (2016).

Google Scholar

[6] G. L. Cosso and C. Servetto, Application of the Omega Method ( API 579-1 / ASME FFS-1 ) to the life assessment of a service exposed component and possible , further investigations on welded joints creep behaviour,, no. 2, p.33–37, (2010).

Google Scholar

[7] F. . Norton, The Creep of Steels,, vol. Mc Graw Hi, no. New York, (1929).

Google Scholar

[8] O. Golan, A. Arbel, D. Eliezer, and D. Moreno, The applicability of Norton's creep power law and its modified version to a single-crystal superalloy type CMSX-2,, Mater. Sci. Eng. A, vol. 216, no. 1–2, p.125–130, 1996,.

DOI: 10.1016/0921-5093(96)10400-7

Google Scholar

[9] D. L. May, A. P. Gordon, and D. S. Segletes, The application of the norton-bailey law for creep prediction through power law regression,, Proc. ASME Turbo Expo, vol. 7 A, p.1–8, 2013,.

DOI: 10.1115/gt2013-96008

Google Scholar

[10] R. B. Davies, R. Hales, J. C. Harman, and S. R. Holdsworth, Statistical modeling of creep rupture data,, J. Eng. Mater. Technol. Trans. ASME, vol. 121, no. 3, p.264–271, 1999,.

DOI: 10.1115/1.2812374

Google Scholar

[11] M. S. Haque and C. M. Stewart, The disparate data problem: The calibration of creep laws across test type and stress, temperature, and time scales,, Theor. Appl. Fract. Mech., vol. 100, no. January, p.251–268, 2019,.

DOI: 10.1016/j.tafmec.2019.01.018

Google Scholar

[12] A. A. Al-Bakri, Z. Sajuri, A. K. Ariffin, M. A. Razzaq, and M. S. Fafmin, Tensile and fracture behaviour of very thin 304 stainless steel sheet,, J. Teknol., vol. 78, no. 6–9, p.45–50, 2016,.

DOI: 10.11113/jt.v78.9146

Google Scholar

[13] M. Sattar, Regression Analysis Of Omega Model To Norton- Bailey Law For Creep Prediction In Fitness For Service Assessment Of Steel Material,, no. October, (2020).

Google Scholar

[14] A. P. V. ASME Codes, ASME. (2015). ASME Boiler and Pressure Vessel Code An International Code- section II part A, 1998.,, Oper. Man., p.1998, 2015, doi: http://dx.doi.org/10.1016/B978-032303506-4.10361-X.

DOI: 10.1115/1.861981_ch39

Google Scholar