Weibull parameter based probability distribution for predicting creep life of power plant materials: A non-destructive approach

Sahu, M and Ghosh, Arpita and Singh, S. N. and Sagar, Sarmishtha Palit (2022) Weibull parameter based probability distribution for predicting creep life of power plant materials: A non-destructive approach. Materials Today: Proceedings.

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Abstract

The knowledge of remaining useful life and the probability of failure at any point of time in the life cycle
of any power plant component is an important information for the plant operators to take preventive
action. This paper focuses on life data evaluation of creep-exposed power plant material based on statistical
probability distribution through Weibull analysis. The probability distribution was obtained considering
the change in non-linear ultrasonic (NLU) parameter measured in P92 steel at different creep test
conditions. The material was creep tested at 650⁰C for three different applied stresses. The NLU
parameter (b), which indicates the extent of damage, is the ratio of the amplitude of the fundamental frequency
of the transmitted signal to the square of the amplitude of the second harmonic of a sinusoidal
wave propagated into the material. The two-parameter based Weibull distribution function was adopted
for evaluating the cumulative distribution function and failure rate. A sudden increase in NLU parameter
was observed at 80% of creep damage followed by a drop in its value indicating the specimen failure.
With increase in applied stress, failure rate increase was also observed. Microstructural observations
revealed that with creep progress, the growth and coarsening of precipitates, micro crack formation
and their coalescence were the major cause for increase in failure rate. Therefore, application of this
technique can be useful for evaluating the creep life and probability of failure of any plant component
in a non-invasive way.

Item Type: Article
Uncontrolled Keywords: Creep Failure rate P92 steel Ultrasonic nonlinearity Weibull distribution
Subjects: Metallurgy > Mathematical Modelling
Metallurgy > Metallurgical Techniques > Mechanical Testing
Divisions: Material Science and Technology
Depositing User: Dr. Arpita Ghosh
Date Deposited: 01 Jun 2023 06:29
Last Modified: 01 Jun 2023 06:29
URI: http://eprints.nmlindia.org/id/eprint/9396

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