Artificial intelligence-assisted physical design of fusion materials

Pan Liu; Fanyu Qu; Zilong Yuan; Qiang Gao; Gaoyang Liu; Zhangchun Tang; Chencheng Liu; Hongwei Qiao; Wenbin Xiong

doi:10.1117/12.2692867

23 August 2023 Artificial intelligence-assisted physical design of fusion materials

Pan Liu, Fanyu Qu, Zilong Yuan, Qiang Gao, Gaoyang Liu, Zhangchun Tang, Chencheng Liu, Hongwei Qiao, Wenbin Xiong

Proceedings Volume 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023); 1278411 (2023) https://doi.org/10.1117/12.2692867
Event: 2023 2nd International Conference on Applied Statistics, Computational Mathematics and Software Engineering (ASCMSE 2023), 2023, Kaifeng, China

Abstract

Inertial confinement fusion (ICF) is an approach to fusion that relies on the inertial of the fuel mass to provide confinement. Conditions under which inertial confinement is sufficient for efficient thermonuclear burn, a capsule (generally a spherical shell) containing different materials and thermonuclear fuel is compressed in an implosion process to conditions of high density and temperature. Another important process is the energy transport, in which the hohlraum coupling effect and hohlraum radiation uniform are the important physical parameters that can limit the energy transport. It is described the ignition condition by different physical parameters. Because the physical processes in fusion ignition are complex, and more physical quantities in the existence of multiple correlations and strong correlations, a single model often can not cope with fusion physics, this paper uses artificial intelligence, combined with complex physical processes, repeated model combination and iteration, to obtain the fusion materials model combination method, to provide an optimal parameter library for experimental physics. In this paper, we obtain the neutron yield of the main fuel DT can reach 10²⁰, which indicates that the aim of achieving fusion can be achieved.

Citation Download Citation

Pan Liu, Fanyu Qu, Zilong Yuan, Qiang Gao, Gaoyang Liu, Zhangchun Tang, Chencheng Liu, Hongwei Qiao, and Wenbin Xiong "Artificial intelligence-assisted physical design of fusion materials", Proc. SPIE 12784, Second International Conference on Applied Statistics, Computational Mathematics, and Software Engineering (ASCMSE 2023), 1278411 (23 August 2023); https://doi.org/10.1117/12.2692867

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