Laser pulse shape designer for direct-drive inertial confinement fusion

TL;DR

This paper introduces a machine learning-enhanced pulse shape designer for direct-drive inertial confinement fusion, enabling efficient optimization of implosion performance and stability with reduced experimental risk.

Contribution

It presents a novel pulse shape design method combining linear analysis and machine learning, improving efficiency and reliability over traditional empirical tuning.

Findings

Optimized pulses meet assembly requirements and mitigate imprint.

The method reduces workload and accelerates the design process.

Potential for improved implosion performance in experiments.

Abstract

A pulse shape designer for direct drive inertial confinement fusion has been developed, it aims at high compression of the fusion fuel while keeping hydrodynamics instability within tolerable level. Fast linear analysis on implosion instability enables the designer to fully scan the vast pulse configuration space at a practical computational cost, machine learning helps to summarize pulse performance into an implicit scaling metric that promotes the pulse shape evolution. The designer improves its credibility by incorporating various datasets including extra high-precision simulations or experiments. When tested on the double-cone ignition scheme [J. Zhang et al, Phil. Trans. R. Soc. A. 378.2184 (2020)], optimized pulses reach the assembly requirements, show significant imprint mitigation and adiabatic shaping capability, and have the potential to achieve better implosion performance in real experiments. This designer serves as an efficient alternative to traditional empirical pulse shape tuning procedure, reduces workload and time consumption. The designer can be used to quickly explore the unknown parameter space for new direct-drive schemes, assists design iteration and reduces experiment risk.