Prediction and uncertainty analysis of power peaking factor by cascaded fuzzy neural networks. (December 2017)

• Record Type: Journal Article
• Authors: Back, Ju Hyun; Yoo, Kwae Hwan; Choi, Geon Pil; Na, Man Gyun; Kim, Dong Yeong
• Is Part Of: Annals of nuclear energy. Volume 110(2017:Dec.)
• Page Start: 989
• Page End: 994
• Publication Date: 2017-12
• Subjects: Cascaded fuzzy neural network (CFNN) -- Local power density (LPD) -- Power peaking factor -- Uncertainty analysis; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2017.08.006
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: Local power density (LPD) should be perdicted accurately to prevent fuel rods from melting. The most important power peaking factor (PPF) of parameters related to LPD is predicted. The cascaded fuzzy neural network (CFNN) is used in this prediction model. This prediction is sufficiently accurate to be used in PPF monitoring. Abstract: Nuclear reactor cores should be maintained within various safety limits such as the local power density (LPD). Therefore, a detailed three-dimensional core power distribution monitoring is required during reactor operation. In addition, LPD must be predicted to prevent nuclear fuel melting. In this study, the most important parameter related to LPD—the power peaking factor—was predicted. A cascaded fuzzy neural network (CFNN) methodology was utilized to predict the power peaking factor in the reactor core. A CFNN model was developed using the numerical simulation data of the optimized power reactor 1000 and its performance was analyzed. Additionally, its uncertainty analysis was conducted to determine the prediction accuracy of the CFNN model. The prediction intervals were found to be pretty narrow, which confirms that the predicted value is reliable. The accuracy of the proposed CFNN model proves to be able to assist nuclear reactor operators in monitoring the power peaking factor.