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

TL;DR

This paper introduces a hybrid collision model and machine learning approach to efficiently simulate charged particle transport in inertial confinement fusion, reducing computational costs while maintaining accuracy.

Contribution

It presents a novel hybrid collision model and neural network-based blocking power calculation to improve efficiency in charged particle transport simulations.

Findings

Reduced computational cost of charged particle transport simulations.

Maintained second-order accuracy in collision modeling.

Demonstrated effectiveness in inertial confinement fusion applications.

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 a strong non-equilibrium effect, so it is necessary to solve the Boltzmann transport equation to simulate the energy transport process of charged particles accurately. On the other hand, charged particle transport has the characteristics of high collision frequency and complex blocking power, so the calculation amount of the 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 the 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 calculation method, we developed the charged particle transport MC function modules of the 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.