7th Asia-Pacific Conference on Plasma Physics, 12-17 Nov, 2023 at Port Messe Nagoya

Machine learning assisted pulse shaping for double cone ignition implosions
Tao Tao1, Guannan Zheng1, Qing Jia1, Rui Yan1,Jian Zheng1
1
University of Science and Technology of Chinae-mail (speaker): tt397396@ustc.edu.cn
Hydrodynamic instability is the fundamental physicalchallenge for inertial confinement fusion. Implosions cancompress fusion fuel to the extreme conditions required
for thermonuclear reactions, but defects in the laser andthe target can also be amplified during the implosion
acceleration, breaking the spherical symmetry of theshell and leading to fuel mixing or shell breakup. There
are two general ideas to suppress the instability: First,minimizing the seed from laser imprinting, which
includes optical smoothing of the spot, optimization ofbeam overlap, and precision target fabrication. Second,
improve the ablation of the interface to suppress thegrowth of the instabilities, which mainly relies on laser
pulse shaping. The optimal pulse shape is related to theseed detail and the implosion geometry, it is difficult for
manual pulse shape tuning to achieve the optimal
implosion performance.
In the novel double cone ignition (DCI) [1] scheme, apair of gold cones is used to guide the implosion ofshells (Figure 1). DCI reduces the illumination solid
angle and the ns-driver energy requirement. However,
the instability risk of DCI is high due to its direct drive
design and its distinctive overlap pattern. In our work [2],machine learning is used to randomly sample a large
number of pulse shapes in fluid simulations, extract laser
imprint features and RTI growth rate, and evaluate pulse
shape performance based on compression areal density
and instability amplitude. As the number of samples
increases, machine learning gradually summarizes thepotential relationship between pulse shape and implosion
performance, and promotes pulse shape evolution basedon this relationship. In the demo application in DCI, themachine learning performs a targeted pulse optimization
based on the specific focal spot and beam overlap
patterns extracted from the real laser device. The
optimized pulse achieves good compression shock
synchronization, preserves the spectral characteristics ofthe laser imprint, and accounts for the coupled evolutionof the laser imprint and RTI. Compared to the manually
tuned pulse shape, the machine-optimized pulse
improves the unmixed fuel layer thickness by a factor of
4 while maintaining a similar compression areal density(Figure 2). This work is a successful application ofmachine learning methods to enable experiments toachieve fast iterative optimization with high confidence.
References[1] Zhang, Jie, et al. "Double-cone ignition scheme forinertial confinement fusion." Philosophical Transactionsof the Royal Society A 378.2184 (2020): 20200015.
[2] Tao, Tao, et al. "Laser pulse shape designer for
direct-drive inertial confinement fusion implosions."
High Power Laser Science and Engineering 11 (2023):
e41.

Figure 1: Geometry of the DCI scheme. (a) target
consists of gold cones and the fuel-containing shell, shell
has an initial radius of 450 μm and a thickness of 50 μm.
(b) Driver laser setup, the total drive energy is 12 kJ, thelaser spot power perturbation RMS~15%.

Figure 2: (a) manually tuned pulse shape and its
implosion streamline, (b) pulse shape and its implosion
streamline optimized by machine learning, (c)-(d)density perturbation of the shell layer at the imprint stage
and at the end of acceleration.