Deep Learning Solution Technology for Sparse Data of Multi-Channel Flow Field of PWR Rod Bundle
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摘要: 反应堆堆芯典型工况雷诺数高达105，冷却剂流动具有显著的非线性，实际流动边界及状态与理想流动方程存在一定的匹配性偏差，会导致求解过程中数据与控制方程的约束相冲突，彼此制约，导致求解收敛困难。为解决该问题，本文研发了一种基于深度学习的稀疏数据求解方法，通过设计不匹配性自适应调节方案，在控制方程中引入自适应调节因子，动态修正理想模型，克服因数据与方程不一致所引发的收敛障碍及精度不足等问题。在此技术基础上，进一步探讨了在小样本数据条件下的流场求解策略，设计了均匀配点、基于速度梯度配点、混合配点策略，旨在通过优化样本点的空间分布，提升流场求解的整体精度。研究结果表明，在3种策略中，均匀配点策略能够更全面地覆盖流场的整体特性，表现出最佳的优化效果，达到决定系数（R2）大于0.95、均方误差（MSE）在10−4至10−3量级的精度；且在仅采用60个小样本数据配点下（占原始数据点的7.8%）。本文所提出的方法也能有效实现高精度流场求解，为稀疏数据条件下求解压水堆堆芯棒束多通道流场提供了一种高效且适用的技术方案。Abstract: The Reynolds number can reach 105 in typical reactor core operating conditions, and the coolant flow exhibits significant nonlinearity. An inevitable mismatch between the actual flow boundaries and states and the ideal flow equations can lead to conflicts between the data and the constraints of the governing equations during the solving process. This mutual restriction can cause difficulties in achieving convergence. This paper developed a sparse data-solving method based on deep learning to address this issue. By designing an adaptive mismatch correction scheme, an adaptive adjustment factor is introduced into the governing equations to correct the ideal model dynamically. This approach overcomes the convergence difficulties and accuracy issues caused by the inconsistency between the data and the equations. Based on this technology, the study further explored flow field-solving strategies under small sample data conditions and designed uniform, velocity-gradient-based, and hybrid point distribution strategies. These strategies aim to optimize the spatial distribution of sample points to improve the overall accuracy of the flow field solutions. The results show that among the three strategies, the uniform point distribution strategy provides the most comprehensive coverage of the overall flow field characteristics and achieves the best optimization effect, with an R² value greater than 0.95 and MSE on the order of 10−4 to 10−3. Moreover, even with only 60 small sample points (7.8% of the original data points), the proposed method can still effectively achieve high-accuracy flow field solutions, providing an efficient and highly applicable solution for solving the multi-channel flow field of PWR reactor core rod bundles under sparse data conditions.
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图 5 有无调节因子结果对比
u, v分别为式（1）中u在x和y方向的速度分量
Figure 5. Comparison of Results with and without Adjustment Factor
图 7 交混翼下游2 mm处流场预测结果对比
Figure 7. Comparison of Prediction Results of Flow Field at 2 mm Downstream of Mixing Vane
图 8 交混翼下游2 mm处预测效果评估
Figure 8. Evaluation of Prediction Effect at 2 mm Downstream of Mixing Vane
图 9 交混翼下游200 mm处流场预测结果对比
Figure 9. Comparison of Prediction Results of Flow Field at 200 mm Downstream of Mixing Vane
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