PSI - Issue 42

Chahboub Yassine et al. / Procedia Structural Integrity 42 (2022) 1025–1032 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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AiméLay-Ekuakille et al. (2014) say that leaks in pipelines could have a big impact on the performance of nuclear power plants. Ferritic steels deform plastically before failure. Jairo Alberto Muoz (2021) explains that this process slows down the fracture as a large amount of energy is absorbed by plastic deformation, so the catastrophic failure can be avoided, giving more time to fix issues. The process of deforming plastically before the fracture is called ductile failure. The Gurson Tveegard Needelman (GTN) model has been used in several studies over the last 50 years to look at the ductility of the pipeline. There has not been much research into how optimization models like an artificial neural network (ANN) could be used to predict pipeline failures in the nuclear field. Most studies have only focused on direct methods to predict the pipeline's failure. However, little attention has been given to combining the GTN model with other features such as artificial neural networks or hybrid swarm optimization. A.Alaswad et al. (2016), Taslim D.Shikalgar et al. (2020), E.J.Pérez-Pérez et (2021), for this reason and others, the purpose of this article is to predict the failure of the pipeline using an artificial neural network. 1.1. GTN model The idea of Gurson (1975) is based on the model of Rice et al. (1969); Gurson suggested a micromechanical model based on a fracture mechanics perspective, such as the critical evolution of voids; Gurson developed an approximate yield criterion for ductile materials, by proposing a continuum theory for ductile failure based on void nucleation and void growth and by showing the important role of hydrostatic stress in plastic yield and void growth, the yield criterion developed by Gurson took as a basis the idealization of the material matrix as being perfectly rigid and obeying the Von-Mises yield criterion. The Gurson model only shows the growth stage of material failure. It needs to be expanded to include the nucleation and coalescence stages. So Tveegard and Needlman (1986) could make modifications to the model and introduce new parameters, as shown in the modified model of the Gurson model, which gave birth to the GTN model; the definition of the new parameters will be defined in this section : The model is defined as : ( ) 2 2 2 1 1 2 2 cosh 1 2 e M M tr q f q f          = + − +     (1) In which q 1 refers to the material constant, and trσ is the sum of principal stresses ; in addition to this, the parameter σ M is the equivalent flow stress and the new parameter f* is the ratio of voids effective volume to the material volume ratio defined as follows: ( ) ( ) c c f f f if f f  =  (2) ( ) ( ) ( ) ( ) 1 1/ c c c c f c q f f f f f f if f f f f  − = + −  − (3) Where f is the voids volume ratio, f c is the voids volume ratio at the beginning of nucleation, and f f is the voids volume ratio when the fracture occurs. σ M is the equivalent flow stress, and it is obtained from the following work hardening relation: ( ) 1 n pl pl M M M y y        =  +      (4) In which n is the strain-hardening exponent, and Ꜫ M is the equivalent plastic strain. The voids growth rate is the sum of existing voids growth fg and the new voids nucleation fn, and the following equation presents it: