Fatigue damage caused by alternating operational stresses in terms of temperature or pressure change is the one of important damage mechanisms in the nuclear power plants (NPPs). Although components important to safety were designed to withstand the fatigue damage, cumulative usage factor (CUF) at some locations can exceed the design limit beyond the design life. So, it is important to monitor the fatigue damage of major components during the long term operation. To evaluate fatigue damage, the Green’s function approach has been generally used. In this approach, thermal stresses can be directly calculated from the convolution integration on the coolant temperature history and Green’s function. And, Green’s function is defined as a stress variation at the arbitrary point when the coolant temperature is increased as a unit step. However, this approach cannot be applied to the fatigue analysis using temperature-dependent material properties because it is assumed that the system is linear. In this paper, the modified Green’s function approach considering temperature-dependent material properties is proposed by using neural network. To verify the modified Green’s function method, thermal stresses by the proposed method are compared with those by finite element analysis (FEA) at the transition wall of reactor pressure vessel and the analysis results between two methods are well agreed. Finally, it is anticipated that more precise fatigue evaluation is performed by using the proposed method.
Green’s function for an elastic layer with temperature-dependent properties
