Evaluation on Mechanical Fracture of PWR Pressure Vessel and Modeling Based on Neural Network

Abstract

EVALUATION ON MECHANICAL FRACTURE OF PWR PRESSURE VESSEL AND MODELING BASED ON NEURAL NETWORK. The important component of the PWR is a pressure vessel. The material resistance in the pressure vessel needs to be evaluated. One way of evaluation is by the mechanical fracture analysis. The modeling needs to know the phenomena of the analysis result in general. A number of researches have been completed on the calculation of mechanical fracture in the pressure vessel with an internal load. The mechanical fracture was modeled using a neural network approach. In relation to the material resistance of the pressure vessel, which is used in PWR AP1000, the material must be evaluated because of the effect of the load. The modeling is needed to predict the effect of the load. The aim of this study is to evaluate the material resistance through mechanical fracture analysis because of the influence load on the pressure vessel on PWR AP1000. The material, which was observed, is SA 508. This analysis consists of the calculation of stress intensity factor and J-integral with some load at the crack propagation position. The fracture mechanic was analyzed by finite element simulation. The result of Stress Intensity factor and J-Integral was compared with fracture toughness to know the durability of the material. The modeling of J-Integral and Stress Intensity Factor were obtained for some load based on neural network approach. Keywords: Material resistance, mechanical fracture, neural network, PWR, pressure vessel, crack propagation. ABSTRAK EVALUASI FRAKTUR MEKANIK PADA BEJANA TEKAN PWR DAN PEMODELAN BERBASIS NEURAL NETWORK. Komponen penting dari PWR adalah bejana tekan. Ketahanan bahan di bejana tekan perlu dievaluasi. Salah satu cara adalah dengan analisis fraktur mekanik. Pemodelan diperlukan untuk mengetahui fenomena hasil analisis pada umumnya. Terdapat penelitian untuk perhitungan fraktur mekanik dalam bejana tekan dengan beban internal. Penelitian lain adalah hasil dari fraktur mekanik dimodelkan menggunakan pendekatan jaringan syaraf. Sehubungan dengan ketahanan material dari bejana tekan yang digunakan dalam PWR AP1000, bahan harus dievaluasi karena efek dari beban. Pemodelan diperlukan untuk memprediksi pengaruh beban pada bahan dalam bejana tekan. Tujuan dari penelitian ini adalah untuk mengevaluasi ketahanan material melalui analisis fraktur mekanik karena pengaruh beban pada bejana tekan. Bahan yang diamati, adalah SA 508. Analisis ini terdiri dari perhitungan faktor intensitas tegangan dan J-integral dengan beberapa beban pada posisi perambatan retak. Fraktur mekanik dianalisis dengan metode elemen hingga. Hasil faktor intensitas tegangan dan J-Integral dibandingkan dengan ketangguhan patah untuk mengetahui daya tahan material. Pemodelan J-Integral dan faktor intensitas stres diperoleh untuk beberapa beban berdasarkan jaringan saraf. Kata kunci: Ketahanan bahan, teknik patahan, jaringan syaraf, PWR, bejana tekan, perambatan retak.

The Analysis of Optimal Crack Ratio for PWR Pressure Vessel Cladding Using Genetic Algorithm

“…One of the types of crack is the semi-elliptic crack. The semielliptic crack is described in Figure 1 [7,11]. There are three types of crack symptoms.…”

“…The stability of crack propagation distribution could be determined by comparing the value of I K with IC K . If the value I K is greater than IC K , it means that the material structure is not in a safe condition [11]. The energy release rate can be expressed as a path-independent line integral, called the Jintegral.…”

“…After obtaining the value of a J-integral, the SIF can be calculated by using the relationship equation between SIF and J-integral. The SIF is expressed in Equation (2) [11].…”

“…The critical crack size in the material was determined by comparing the value of SIF and the material's fracture toughness. If the SIF is less than the fracture toughness value, it means that the material still has resistance against fracture [11].…”

“…In another study, the residual stress in high-temperature treatment was optimized using GA and Neural Network [10]. Finally, the mechanical fracture of PWR pressure vessel was evaluated and modeled based on neural network [11].…”