Improving the Predictivity of a Steam Generator Clogging Numerical Model by Global Sensitivity Analysis and Bayesian Calibration Techniques

In pressurized nuclear reactors, steam generators are massive tubular heat exchangers transferring heat from the primary to the secondary fluid to produce the steam needed by the turbines. After several years of operation, because of deposit, their tube support plates (TSPs) can undergo clogging that may cause important economic and safety issues in case of nonpreventive actions. To understand and predict this phenomenon, several nondestructive examinations can generally be gathered at various times during the heat exchanger operation. A numerical mechanistic model has been recently developed and implemented in a dedicated computer code. The objective of this work is to improve the modeling of the clogging phenomenon to increase the predictive capability of the computer code. A global sensitivity analysis, based on Sobol’ indices, is first performed by the use of a metamodel that is learned on several runs of the computer code. Such an analysis, cast under a physical perspective, helps the identification of the most influential physical parameters and paves the way to a better understanding of TSP clogging. A Bayesian calibration of an epistemic calibration model parameter is then applied to fit the simulation results to experimental data. The additional information coming from the experimental data is then transferred to the calibration parameter with a mathematical model (artificial neural network). The resulting hybrid model thus compensates some lacks of the initial physical model on the considered data set.