Cellular neural network to the spherical harmonics approximation of neutron transport equation in x–y geometry. Part I: Modeling and verification for time-independent solution

This paper describes a novel method based on using cellular neural networks (CNN) coupled with spherical harmonics method (PN) to solve the time-independent neutron transport equation in x–y geometry. To achieve this, an equivalent electrical circuit based on second-order form of neutron transport equation and relevant boundary conditions is obtained using CNN method. We use the CNN model to simulate spatial response of scalar flux distribution in the steady state condition for different order of spherical harmonics approximations. The accuracy, stability, and capabilities of CNN model are examined in 2D Cartesian geometry for fixed source and criticality problems.

► This paper describes the solution of time-independent neutron transport equation. ► Using a novel method based on cellular neural networks (CNNs) coupled with PN method. ► Utilize the CNN model to simulate spatial scalar flux distribution in steady state. ► The accuracy, stability, and capabilities of CNN model are examined in x–y geometry.