Prediction of the remaining time and time interval of pebbles in pebble bed HTGRs aided by CNN via DEM datasets

Wu Mengqi; Liu Xu; Gui Nan; Yang Xingtuan; Tu Jiyuan; Jiang Shengyao; Zhao Qian

doi:10.1016/j.net.2022.09.019

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자료유형: 학술저널

저자정보: Wu Mengqi (Tsinghua University) Liu Xu (Tsinghua University) Gui Nan (Tsinghua University) Yang Xingtuan (Tsinghua University) Tu Jiyuan (Tsinghua University) Jiang Shengyao (Tsinghua University) Zhao Qian (RMIT University)

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제55권 제1호

발행연도: 2023.1

수록면: 339 - 352 (14page)

DOI: 10.1016/j.net.2022.09.019

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Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNNbased deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19þDEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19þDEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool

#Convolutional neural network (CNN) #Discrete element method (DEM) #Time prediction #Pebble-flow #Pebble bed HTGRs #onvolutional neural network (CNN) #Discrete element method (DEM) #Time prediction #Pebble-flow #Pebble bed HTGRs

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