Prediction of time-varying inner wall temperature of surge lines by a dynamic neural network

Abstract

Thermal fatigue, mainly caused by thermal stratification, is one of the major factors for reliability degradation of surge lines in nuclear power plants and is well managed through on-line temperature monitoring. However, the vast amount of outer wall temperature data generated by on-line temperature monitoring requires an efficient way of calculating the inner wall temperature. In this paper, the time-varying inner wall temperature is predicted rapidly and precisely using a dynamic artificial neural network model based on outer wall temperature and flow velocity. By optimizing the length of time series, the time delay of heat transfer between the inner wall and outer wall temperature is fully considered in the dynamic model. Consequently, the best root mean squared error of the temperature prediction is as low as 3.78. More importantly, the importance analysis based on dynamic model is also performed to quantify the importance of outer wall temperature variables, which can be used to optimize the arrangement of outer wall temperature measurement points for further applications under different circumstances to reduce cost while maintain high prediction accuracy. The results in this study will be helpful for the optimization of measurement points arrangement and rapid on-line thermal fatigue evaluation of nuclear power plants.