A data-driven strategy for xenon dynamical forecasting using dynamic mode decomposition. (15th December 2020)

• Record Type: Journal Article
• Authors: Gong, Helin; Yu, Yingrui; Peng, Xingjie; Li, Qing
• Is Part Of: Annals of nuclear energy. Volume 149(2020)
• Publication Date: 2020-12-15
• Subjects: Dynamic mode decomposition -- Data-driven strategy -- Xenon oscillation -- HPR1000 -- Power distribution; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2020.107826
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: The dynamic mode decomposition (DMD) is adapted to forecast the power distribution during the process of xenon oscillations. The DMD based strategy is a fast, model-free and easy-to-implement data-driven method. Numerical results on HPR1000 shows the efficiency in the prediction of power distribution. The current amount of SPNDs in HPR1000 is enough to capture the dynamical behavior of xenon oscillations. Abstract: Detection and prediction of xenon induced oscillations are an important part in the operation of pressurized water reactors. Several models have been proposed for the prediction or estimation of xenon oscillations with drawbacks e.g. strongly depend on the initial xenon and iodine distributions, hard-to-implement or computationally costly. In this article, we proposed a fast, model-free and easy-to-implement data-driven strategy based on dynamic mode decomposition (DMD) to forecast the power distribution during the process of xenon oscillations. Various comparative experiments based on HPR1000 reactor show that the proposed data driven strategy is able to capture the complex relational characteristics of the temporal and spatial data of xenon oscillations. It can efficiently discover the hidden dynamicities and thus offers an accurate prediction of the system behavior.