66 / 2023-04-12 09:32:15

Machine learning assisted pulse shaping for double cone ignition implosions

inertial confinement fusion,Rayleigh-Taylor instability,pulse shaping,machine-learning

惯性约束聚变物理学 > 其他ICF途径和脉冲功率技术-II

Pulse shaping is a powerful tool for mitigating implosion instabilities in direct-drive inertial confinement fusion. However, the high-dimensional and nonlinear nature of implosions makes the pulse optimization quite challenging. The optimal pulse shape depends on the details of laser non-uniformity, target layering, and target material. In this research, we use machine learning to design the pulse shape for higher compression density and more stable implosion. The optimization model takes into account the facility-specific laser imprint pattern and RTI seeding. This optimization is applied to the novel double-cone ignition (DCI) scheme. Simulation shows that the optimized pulse increases the areal density expectation by 16% in 1-D and the clean fuel thickness by a factor of 4 in 2-D. This pulse design method could be a useful tool for controlling the instability of direct-drive ICFs.