Uncertainty analysis of dynamic mode decomposition for xenon dynamic forecasting

In this paper, a systematic uncertainty quantification of dynamic mode decomposition (DMD) for xenon dynamic prediction is brought out based on HPR1000 reactor. The DMD method is a data-driven approach that decomposes complex systems into spatio-temporal structures and can be used to predict the power distribution in the process of xenon oscillation. To further investigate and improve the robustness of DMD with respect to observation noise, different error metrics are established. The dependence of the prediction error on observation noise level and hyper parameters including the window length and the singular value truncation threshold are investigated. Various numerical experiments based on a typical fuel cycle of the HPR1000 reactor demonstrate that DMD with optimal hyper parameters is robust with respect to observation noise, which confirms that DMD is feasible for real engineering applications.