Prediction of the remaining time and time interval of pebbles in pebble bed HTGRs aided by CNN via DEM datasets
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 an...
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| 主要な著者: | , , , , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Elsevier
2023-01-01
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| シリーズ: | Nuclear Engineering and Technology |
| 主題: | |
| オンライン･アクセス: | http://www.sciencedirect.com/science/article/pii/S173857332200451X |
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