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.

Application of artificial neural networks and genetic algorithms to modeling molecular electronic spectra in solution Abstract. The behavior of the fatigue life of the industrial materials is very important. In many cases, the material with experiencing fatigue life cannot be avoided, however, there are many ways to control their behavior. Many investigations of the fatigue life phenomena of alloys have been done, but it is high cost and times consuming computation. This paper report the modeling and simulation approaches to predict the fatigue life behavior of Aluminum Alloys and resolves some problems of computation. First, the simulation using genetic algorithm was utilized to optimize the load to obtain the stress values. These results can be used to provide N-cycle fatigue life of the material. Furthermore, the experimental data was applied as input data in the neural network learning, while the samples data were applied for testing of the training data. Finally, the multilayer perceptron algorithm is applied to predict whether the given data sets in accordance with the fatigue life of the alloy. To achieve rapid convergence, the Levenberg-Marquardt algorithm was also employed. The simulations results shows that the fatigue behaviors of aluminum under pressure can be predicted. In addition, implementation of neural networks successfully identified a model for material fatigue life.

Several aspects of material failure have been investigated, especially for materials used in Reactor Pressure Vessel (RPV) cladding. One aspect that needs to be analyzed is the crack ratio. The crack ratio is a parameter that compares the depth of the gap to its width. The optimal value of the crack ratio reflects the material's resistance to the fracture. Fracture resistance of the material to fracture mechanics is indicated by the value of Stress Intensity Factor (SIF). This value can be obtained from a J-integral calculation that expresses the energy release rate. The detection of the crack ratio is conducted through the calculation of J-integral value. The Genetic Algorithm (GA) is one way to determine the optimal value for a problem. The purpose of this study is to analyze the possibility of fracture caused by crack. It was conducted by optimizing the crack ratio of AISI 308L and AISI 309L stainless steels using GA. Those materials are used for RPV cladding. The minimum crack ratio and J-Integral values were obtained for AISI 308L and AISI 309L. The SIF value was derived from the J-Integral calculation. The SIF value was then compared with the fracture toughness of those material. With the optimal crack ratio, it can be predicted that the material boundaries are protected from damaged events. It can be a reference material for the durability of a mechanical fracture event.Keywords: Fracture mechanics, RPV cladding, J-Integral, Stress Intensity Factor, Genetic Algorithm ANALISIS RASIO RETAK OPTIMAL UNTUK KELONGSONG BEJANA TEKAN PWR MENGGUNAKAN ALGORITMA GENETIKA. Banyak aspek kegagalan material telah diteliti, terutama untuk bahan yang digunakan pada kelongsong bejana tekan reaktor (RPV). Salah satu aspek yang perlu dianalisis adalah rasio retak. Rasio retak adalah parameter yang membandingkan kedalaman celah dengan lebarnya. Nilai optimal rasio retak mencerminkan ketahanan material terhadap patahan. Ketahanan material terhadap mekanika patahan ditunjukkan oleh nilai Stress Intensity Factor (SIF). Nilai ini dapat diperoleh dari perhitungan J-integral yang mengekspresikan tingkat pelepasan energi. Deteksi rasio retak dilakukan melalui perhitungan nilai J-integral. Algoritma Genetika (GA) adalah salah satu cara untuk menentukan nilai optimal suatu masalah. Tujuan dari penelitian ini adalah untuk menganalisis kemungkinan patah yang disebabkan oleh retak dengan menganalisis rasio retak baja tahan karat AISI 308L dan AISI 309L dengan GA. Bahan tersebut digunakan untuk kelongsong RPV. Rasio retak optimal dan nilai J-Integral diperoleh untuk AISI 308L dan AISI 309L. Nilai SIF berasal dari perhitungan J-Integral. Nilai SIF kemudian dibandingkan dengan ketangguhan retak material tersebut. Dengan rasio retak optimal, dapat diprediksi batas rasio retak sehingga terlindung dari kejadian patah. Hal ini dapat menjadi bahan referensi untuk ketahanan dari mekanika patahan.Kata kunci: Mekanika Patahan, Kelongsong Bejana Tekan Reaktor, J-Integral, Faktor Intensitas Tegangan, Algoritma Genetik

In a nuclear industry area, high temperature treatment of materials is a factor which requires special attention. Assessment needs to be conducted on the properties of the materials used, including the strength of the materials. The measurement of material properties under thermal processes may reflect residual stresses. The use of Genetic Algorithm (GA) to determine the optimal residual stress is one way to determine the strength of a material. In residual stress modeling with several parameters, it is sometimes difficult to solve for the optimal value through analytical or numerical calculations. Here, GA is an efficient algorithm which can generate the optimal values, both minima and maxima. The purposes of this research are to obtain the optimization of variable in residual stress models using GA and to predict the center of residual stress distribution, using fuzzy neural network (FNN) while the artificial neural network (ANN) used for modeling. In this work a single-material 316/316L stainless steel bar is modeled. The minimal residual stresses of the material at high temperatures were obtained with GA and analytical calculations. At a temperature of 650 0 C, the GA optimal residual stress estimation converged at -711.3689 MPa at a distance of 0.002934 mm from center point, whereas the analytical calculation result at that temperature and position is -975.556 MPa . At a temperature of 850 0 C, the GA result was -969.868 MPa at 0.002757 mm from the center point, while with analytical result was -1061.13 MPa. The difference in residual stress between GA and analytical results at a temperature of 650 o C is about 27%, while at 850 o C it is 8.67%. The distribution of residual stress showed a grouping concentrated around a coordinate of (-76; 76) MPa. The residuals stress model is a degree-two polynomial with coefficients of 50.33, -76.54, and -55.2, respectively, with a standard deviation of 7.874.