IMPLEMENTATION OF DATA ASSIMILATION METHODOLOGY FOR PHYSICAL MODEL UNCERTAINTY EVALUATION USING POST-CHF EXPERIMENTAL DATA

• Main Authors: JAESEOK HEO, SEUNG-WOOK LEE, KYUNG DOO KIM
• Format: Article
• Language: English
• Published: Elsevier 2014-10-01
• Series: Nuclear Engineering and Technology
• Subjects: Model Calibration; Uncertainty Quantification; Bayes' Theorem
• Online Access: http://www.sciencedirect.com/science/article/pii/S1738573315301029

The Best Estimate Plus Uncertainty (BEPU) method has been widely used to evaluate the uncertainty of a best-estimate thermal hydraulic system code against a figure of merit. This uncertainty is typically evaluated based on the physical model's uncertainties determined by expert judgment. This paper introduces the application of data assimilation methodology to determine the uncertainty bands of the physical models, e.g., the mean value and standard deviation of the parameters, based upon the statistical approach rather than expert judgment. Data assimilation suggests a mathematical methodology for the best estimate bias and the uncertainties of the physical models which optimize the system response following the calibration of model parameters and responses. The mathematical approaches include deterministic and probabilistic methods of data assimilation to solve both linear and nonlinear problems with the a posteriori distribution of parameters derived based on Bayes' theorem. The inverse problem was solved analytically to obtain the mean value and standard deviation of the parameters assuming Gaussian distributions for the parameters and responses, and a sampling method was utilized to illustrate the non-Gaussian a posteriori distributions of parameters. SPACE is used to demonstrate the data assimilation method by determining the bias and the uncertainty bands of the physical models employing Bennett's heated tube test data and Becker's post critical heat flux experimental data. Based on the results of the data assimilation process, the major sources of the modeling uncertainties were identified for further model development.