Probabilistic Fracture Mechanics Analysis of the Beltline of A PWR Nuclear Power Plant Pressure Vessel

In this paper, a simplified algorithm is implemented to develop a probabilistic fracture mechanics code using python programming language. This code is able to predict the service life of a pressurized water reactor pressure vessel of Nuclear Power Plant considering its beltline region depending upo...

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Published in2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI) pp. 1 - 5
Main Authors Muktadir, Md Sifatul, Datta, Debashis, Mollah, Abdus Sattar
Format Conference Proceeding
LanguageEnglish
Published IEEE 08.07.2021
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DOI10.1109/ACMI53878.2021.9528105

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Summary:In this paper, a simplified algorithm is implemented to develop a probabilistic fracture mechanics code using python programming language. This code is able to predict the service life of a pressurized water reactor pressure vessel of Nuclear Power Plant considering its beltline region depending upon the material properties and operational conditions. Considering the manufacturing defect which is responsible for the initial flaws of a material, this code is able to calculate the failure probability which in turns gives the suggestion of the improvement in the manufacturing process. Besides, this PFM code which is available in different countries with their own version will help to estimate the safety margins of an operating pressure vessel in our country also considered. During the manufacturing process the development of initial crack generation and propagation of the crack to its critical value is considered in this work as a function of time of operation. In addition to that, Log-Normal distribution method is applied here for the assumption of the preexisting cracks in the manufacturing material while, considering the operational stresses and fatigue load the failure probability is calculated in the presented work. Variation with the initial crack size and various design stress is considered for this analysis.
DOI:10.1109/ACMI53878.2021.9528105