Investigation on Isotopic Depletion Validation and Uncertainty Analysis With Kalman Filtering

Abstract Isotopic concentration is one of the essential indicators to evaluate the burnup process, which provides detailed descriptions of fuel assembly status among various reactor core conditions. This work aims to develop a method in support of isotopic depletion validation. The developed validation method attempts to improve predictions of isotopic concentration across burnup fusing the computational results from a high-fidelity model with a small amount of measurement data. The isotopic predictions are improved by minimizing the discrepancies between the model prediction and the reality, as well as reducing the associated predicting uncertainties. However, this is such a challenge due to the lack of experimental measurement data and limitations by qualities of existing data. Isotopic validation method developed in this work takes advantages of Kalman Filtering (KF) to solve the challenges mentioned above. This work makes the best use of the reduced data to optimize the prediction process based on a few measurements across burnup. The simulations and uncertainty analysis are completed by OpenMC with a buffer code. Results show that the discrepancies between computational predictions and the reality are reduced, and uncertainties of isotopic concentrations across burnup decrease by about 50%.