用深度学习预测沸水堆剩余临界性，避免燃料浪费或提前停堆。
Predicting BWR Criticality with Data-Driven Machine Learning Model
- 基于数据驱动的深度学习模型预测反应堆余剩临界性
- 可避免燃料过早耗尽或过度剩余，提升经济性
- 适用于核电站燃料管理优化，对运营人员有价值
核电站运行中需确定每周期所需燃料量。大型核电站设计为满负荷运行，经济上要求燃料能维持至周期末（EOC）临界状态。若反应堆在周期末前变为次临界，将导致燃料提前耗尽；反之，若周期末仍有多余反应性，则燃料未充分利用。本文提出一种基于数据驱动的深度学习方法，用于估算沸水堆的剩余临界性，以优化燃料使用，避免经济损失。
原文摘要 · Abstract (English)
One of the challenges in operating nuclear power plants is to decide the amount of fuel needed in a cycle. Large-scale nuclear power plants are designed to operate at base load, meaning that they are expected to always operate at full power. Economically, a nuclear power plant should burn enough fuel to maintain criticality until the end of a cycle (EOC). If the reactor goes subcritical before the end of a cycle, it may result in early coastdown as the fuel in the core is already depleted. On contrary, if the reactor still has significant excess reactivity by the end of a cycle, the remaining fuels will remain unused. In both cases, the plant may lose a significant amount of money. This work proposes an innovative method based on a data-driven deep learning model to estimate the excess criticality of a boiling water reactor.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。