An efficient numerical method for charged particle transport based on hybrid collision model and machine learning

Abstract

Charged particle transport is an important energy transport mode in the combustion process of inertial confinement fusion plasma. On the one hand, charged particles inside the hot spot have strong non-equilibrium effect, so it is necessary to solve the Boltzmann transport equation to accurately simulate the energy transport process of charged particles. On the other hand, charged particle transport has the characteristics of high collision frequency and complex blocking power, so the calculation amount of traditional Monte Carlo algorithm is difficult to bear under the existing calculation conditions. Aiming at the computational bottleneck caused by the large Coulomb potential collision cross section, we developed a hybrid collision model which greatly reduced the computational cost while maintaining the second-order accuracy of the collision process. In order to solve the computational bottleneck caused by complex blocking power model, we developed a neural network model based on machine learning to achieve formal unity and efficient calculation of different blocking power. Based on the developed calculation method, we developed the charged particle transport MC function modules of RDMG program and LARED-S program, and applied them to the study of critical target performance of inertial confinement fusion, which showed good computational efficiency and accuracy.