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Home / Publications / Journals / Nuclear Science and Engineering / Volume 176 / Number 1 / Pages 81-97

Calibration And Improved Prediction Of Computer Models By Universal Kriging

François Bachoc, Guillaume Bois, Josselin Garnier, and Jean-Marc Martinez

Nuclear Science and Engineering / Volume 176 / Number 1 / January 2014 / Pages 81-97

Technical Paper / dx.doi.org/10.13182/NSE12-55

This paper addresses the use of experimental data for calibrating a computer model and improving its predictions of the underlying physical system. A global statistical approach is proposed in which the bias between the computer model and the physical system is modeled as a realization of a Gaussian process. The application of classical statistical inference to this statistical model yields a rigorous method for calibrating the computer model and for adding to its predictions a statistical correction based on experimental data. This statistical correction can substantially improve the calibrated computer model for predicting the physical system on new experimental conditions. Furthermore, a quantification of the uncertainty of this prediction is provided. Physical expertise on the calibration parameters can also be taken into account in a Bayesian framework. Finally, the method is applied to the thermal-hydraulic code FLICA 4, in a single-phase friction model framework. It allows significant improvement of the predictions of FLICA 4.

 
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