A life assessment methodology for heat exchanger and steam generator tubing

The development of an accurate technique for the service life prediction of steam generator tubes is influenced by two major problems. These are: (1) availability of limited information regarding the in-service conditions of the tubes, and (2) effects of material aging and degradation resulting from service exposure.;In an attempt to solve the above problems, this dissertation research focused on the development of a model-based methodology for the life assessment of steam generator tubes subjected to a certain degradation process. The methodology combines engineering analysis of the degradation process under study with the analysis of process field data and information to establish semi-empirical parametric and artificial neural network prediction (NNP) models to forecast the future trend in the degradation. The projection of this trend to a pre-defined allowable degradation level was used to determine the life expectancy of the component. The salient feature of this methodology is in its capacity to recognize the process nonlinearities and to identify the correct process trends which cannot be detected by simple applications of traditional forecasting techniques. This capacity greatly reduces the amount of required field data for a good forecast.;The proposed life assessment approach was used to predict the wear process of a Once-Through Steam Generator (OTSG) tube within its 15th tube support as a complex application of trend forecasting. To implement the methodology, a tube wear process model was developed to simulate the process trend over time with regard to aging and degradation mechanisms resulting from service exposure. The simulated wear data were used to establish various semi-empirical prediction models.;The results indicated that the power-exponential model (at;The results also demonstrated that NNP models perform extremely well for both trend recognition and prediction, even with a limited amount of data. An attractive feature of NNP models is that they are far less noise-sensitive than parametric models. The estimation of residual life of tubing was demonstrated using both parametric and NNP models.