求解中子扩散问题的物理信息神经网络的通量网络与有效倍增因子的联合初始化

中文摘要 AI 辅助

有效倍增因子（keff）的高效确定是反应堆堆芯中子学分析中的重要计算任务。物理信息神经网络（PINNs）将中子扩散方程和边界条件纳入网络训练，以高效确定中子通量分布和keff。为进一步提高PINNs计算keff的效率，本研究提出了联合初始化物理信息神经网络（JI-PINN）。该方法利用K本征值问题的低分辨率近似解，构建通量网络参数与keff的联合初始状态，随后在物理约束下对两者进行联合优化。所提方法在四个测试案例上得到验证：二维两组分两材料案例、IAEA二维基准题、二维两组分四材料案例以及三维单组案例。对于这些测试案例，总计算时间分别减少了25.4%、38.2%、49.4%和28.9%，同时保持了相当的求解精度，且keff与参考值显著偏差相关的异常结果出现频率也有所降低。所提方法为用PINNs求解中子扩散K本征值问题提供了更高效、鲁棒的初始化策略。

英文摘要

Efficient determination of the effective multiplication factor (keff) is an important computational task in reactor core neutronics analysis. Physics-informed neural networks (PINNs) incorporate neutron diffusion equations and boundary conditions into network training to efficiently determine the neutron flux distribution and keff. To further improve the efficiency of keff calculations using PINNs, a Joint Initialization Physics-Informed Neural Network (JI-PINN) is proposed in this work. In this method, a low-resolution approximate solution to the K-eigenvalue problem is used to construct a joint initial state for the flux network parameters and keff, and both are then jointly optimized under physical constraints. The proposed method was validated on a two-dimensional two-group two-material case, the IAEA 2D benchmark, a two-dimensional two-group four-material case, and a three-dimensional single-group case. For these test cases, the total computational time was reduced by 25.4%, 38.2%, 49.4%, and 28.9%, respectively, while comparable solution accuracy was maintained. The occurrence of anomalous results associated with marked deviations of keff from the reference value was also reduced. The proposed method provides a more efficient and robust initialization strategy for solving neutron diffusion K-eigenvalue problem with PINNs.

发表机构

• School of Computer Science and Technology, Chongqing University of Posts and Telecommunications（重庆邮电大学计算机科学与技术学院）
• Center for Scientific Intelligence Innovation, University of Science and Technology of China（中国科学技术大学科学智能创新中心）
• Institute of Advanced Technology, University of Science and Technology of China（中国科学技术大学先进技术研究院）

机构由 AI 辅助整理，请以论文原文为准。