• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

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 CNN-based 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 VGG19+DEX 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 VGG19+DEX 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.

• Nuclear Engineering and Technology
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• v.56 no.5
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• pp.1679-1686
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• 2024

Graphite material plays an important role in nuclear reactors especially the high-temperature gas-cooled reactors (HTGRs) by its outstanding comprehensive nuclear properties. The structural integrity of graphite pebble fuel elements is the first barrier to core safety under any circumstances. The correct knowledge of the stiffness coefficient of the graphite pebble fuel element inside the reactor's core is significant to ensure the valid design and inherent safety. In this research, a vertical extrusion device was set up to measure the stiffness coefficient of the graphite pebble fuel element by the Institute of Nuclear and New Energy Technology (INET) of Tsinghua University in China. The stiffness coefficient equations of graphite pebble fuel elements at different temperatures are given (in a helium atmosphere). The result first provides the data on the high-temperature stiffness coefficient of pebbles in helium gas. The result will be helpful for the engineering safety analysis of pebble-bed nuclear reactors.