Online Fatigue-Monitoring Models with Consideration of Temperature Dependent Properties and Varying Heat Transfer Coefficients

Thermal stress failure caused by alternating operational loads is the one of important damage mechanisms in the nuclear power plants. To evaluate the thermal stress responses, the Green’s function approach has been generally used. In this paper, a method to consider varying heat transfer coefficients when using the Green’s function method is proposed by using artificial parameter method and superposition principle. Time dependent heat transfer coefficient has been treated by using a modified fluid temperature and a constant heat transfer coefficient. Three-dimensional temperature and stress analyses reflecting entire geometry and heat transfer properties are required to obtain accurate results. An efficient and accurate method is confirmed by comparing its result with corresponding 3D finite element analysis results for a reactor pressure vessel (RPV). From the results, it is found that the temperature dependent material properties and varying heat transfer coefficients can significantly affect the peak stresses and the proposed method can reduce computational efforts with satisfactory accuracy. 1. Introduction Thermal stress failure caused by alternating operational loads is the one of important damage mechanisms in the nuclear power plants. The design of major nuclear components for the prevention of fatigue failure has been performed on the basis of ASME codes, which are usually very conservative. The sustained interest in the area of remaining life prediction arises from the need to avoid costly outages and safety considerations and to extend the plant operation life. So, it is very important to monitor the degradation due to thermal fatigue in order to keep the integrity of major components during the long term operation. The important step in online fatigue monitoring is the conversion of plant transients to the stress responses in the components. A largely used methodology of stress-based fatigue monitoring is the Green’s Function Technique (GFT), which is an effective method for online fatigue monitoring system with a fast algorithm to calculate the thermal stresses corresponding to actual operating data acquired from sensors. The time histories of the outputs are evaluated from the time histories of the inputs by solving a set of convolution integrals. The only data necessary to perform the calculation are the Green’s functions of the thermomechanical model, that is, time histories of thermal stresses due to unit step inputs. Many researchers [1–5] have shown that the temperature responses and thermal stresses can be estimated using GFT. In