[2B13-17] Analysis Method 2

[2B13] Dimension-reduced cross-section adjustment method based on Bayesian Monte-Carlo method

*Yuhei Fukui¹, Tomohiro Endo¹, Akio Yamamoto¹ (1. Nagoya University)

Keywords:Cross-section adjustment, Dimension reduction, Bayesian Monte-Carlo method, Data assimilation

We are investigating a cross-section adjustment method incorporating dimension reduction and the Bayesian Monte-Carlo method, which is a robust data assimilation method. Virtual “true” cross-sections are generated using a perturbation set based on the covariance data, and the numerical result using the “true” cross-section is considered as a virtual “true” experimental value. We applied the proposed cross-section adjustment method to the virtual “true” experimental value and verified the performance of the method.