用AI预测核反应堆热限偏差，提升燃料效率与运行安全。
A Methodology for Thermal Limit Bias Predictability Through Artificial Intelligence
- 基于全卷积网络融合特征，预测更接近实测值的热限参数。
- 相比离线方法，节点误差降低74%，最大偏差减少52%。
- 已部署于多座沸水堆，适用于核电运营优化场景。
核电站运营商面临离线与在线热限之间不可预测的偏差问题，即热限偏差，导致设计余量保守、燃料成本上升及运行效率降低。本文提出一种基于深度学习的方法，用于预测并校正沸水堆（BWRs）的热限偏差，聚焦于限制功率密度最大值（MFLPD）这一衡量线性热生成率（LHGR）极限的关键指标。所提模型采用全卷积编码器-解码器结构，并引入特征融合网络，以预测更贴近在线测量值的修正后MFLPD。在五个独立燃料循环上评估显示，该模型使平均节点阵列误差降低74%，极限值平均绝对偏差减少72%，最大偏差下降52%。结果表明，该方法可显著改善燃料循环经济性与运行规划；其商业版本已在多个运行中的BWRs中部署。
原文摘要 · Abstract (English)
Nuclear power plant operators face significant challenges due to unpredictable deviations between offline and online thermal limits, a phenomenon known as thermal limit bias, which leads to conservative design margins, increased fuel costs, and operational inefficiencies. This work presents a deep learning based methodology to predict and correct this bias for Boiling Water Reactors (BWRs), focusing on the Maximum Fraction of Limiting Power Density (MFLPD) metric used to track the Linear Heat Generation Rate (LHGR) limit. The proposed model employs a fully convolutional encoder decoder architecture, incorporating a feature fusion network to predict corrected MFLPD values closer to online measurements. Evaluated across five independent fuel cycles, the model reduces the mean nodal array error by 74 percent, the mean absolute deviation in limiting values by 72 percent, and the maximum bias by 52 percent compared to offline methods. These results demonstrate the model's potential to meaningfully improve fuel cycle economics and operational planning, and a commercial variant has been deployed at multiple operating BWRs.
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