ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Global Sensitivity Analysis and Bayesian Calibration of a Clogging Numerical Model

3 September 2018, 15:30 – 15:50

Abstract

Submitted by
Bertrand Iooss
Authors
Bertrand Iooss (EDF R&D), Loïc Le Gratiet (EDF R&D), Guillaume Damblin (CEA), Sandrine Gyuran (CEA), Laurent Lefebvre (Framatome), Mathieu Segond (Framatome), Roberto Spaggiari (Framatome)
Abstract
Steam generators are massive heat exchangers transferring the heat from the primary to the secondary fluid to produce the steam needed by the turbines of nuclear power plants. After several years of operation, they can be subject to clogging that limits their heat exchange capacity and causes important economic and safety issues. In order to understand and predict this phenomenon, several non-destructive examinations (televisual data and eddy-current signals) have been gathered at various times of the heat exchanger operation, and a numerical mechanistic model has been recently developed by EDF.

The objective of this work is to improve the modeling of 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 neural network metamodel that has learnt on several runs of the computer code. By discussing the results with the clogging specialist engineers, this step helps improving the understanding of the clogging phenomenon. A Bayesian calibration of an epistemic model parameter is then applied in order to fit simulations to data. The resulting model allows compensating for physical phenomena not taken into account by the initial clogging numerical model.

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