基于 PINN 的燃料棒稳态温度分布快速预测方法研究

A fast prediction method of fuel rod steady-state temperature distribution base on Physical Informed Neural Network (PINN) is established in this research. The burnup, linear power, boundary temperature and space position are taken as characteristic parameters to solve the parametric solid heat conduction equations using PINN. Based on this method, rapid prediction models for the steady-state temperature distribution of fuel pellet and cladding were constructed. The calculation results show that the calculation speed of fast prediction models are about 1000 times faster than that of commercial finite element method software, and they also have high accuracy. The maximum relative deviation of the steady-state temperature prediction of fuel pellets and cladding is about 0.318% and 0.013% respectively compared with the validation set. The established PINN model can quickly and accurately predict the steady-state temperature distribution of fuel rods.