基于PINN的源迭代法求解及加速算法研究  

Research on the Solution and Acceleration Algorithm of Source Iteration Method Based on PINN

作　　者：江勇 安萍[1] 刘东[1] 于洋[1] Jiang Yong;An Ping;Liu Dong;Yu Yang(National Key Laboratory of Nuclear Reactor Technology,Nuclear Power Institute of China,Chengdu,610213,China)

机构地区：[1]中国核动力研究设计院核反应堆技术全国重点实验室,成都610213

出　　处：《核动力工程》2025年第2期148-155,共8页Nuclear Power Engineering

基　　金：四川省自然科学基金(2023NSFSC0066、2023NSFSC1321);四川省揭榜挂帅行业共性技术攻关项目(23jBGOV0001);国家基础性科研院所基础科研稳定支持专项资助项目(WDZC-2023-05-03-05)。

摘　　要：本文将基于物理驱动的人工智能方法和传统源迭代法结合,建立求解少群扩散方程的新型方法流程,并采用Anderson加速方法对迭代源项进行加速。二维多材料、三维单材料等例题的计算结果显示,基于物理驱动的物理信息神经网络(PINN)和传统源迭代法相结合,在保证计算精度的前提下可计算出连续中子注量率分布,采用Anderson加速可减少迭代次数,成功实现了少群中子扩散方程的正向求解,助推了人工智能算法在核领域的应用。This paper integrates physics-driven artificial intelligence methods with the traditional source iteration method to establish a novel approach for solving the few-group diffusion equations,and employs the Anderson acceleration method to accelerate the iterative source term.The results of numerical examples such as two-dimensional multi material and three-dimensional single material show that the combination of physics-driven Physics-Informed Neural Networks(PINN)and traditional source iteration method can calculate the continuous neutron flux density distribution while ensuring calculation accuracy.The use of Anderson acceleration method can reduce the number of iteration,ssuccessfully achieving the forward solution of the few-group neutron diffusion equations.This advancement promotes the application of artificial intelligence algorithms in the nuclear field.

关 键 词：物理信息神经网络(PINN) Anderson加速 源迭代 中子扩散方程 

分 类 号：TL32[核科学技术—核技术及应用]