Optimization of target compression for high-gain fast ignition via machine learning

نویسندگان

چکیده

The hydrodynamic scaling relations are of great importance for the design and optimization target compression in laser-driven fusion. In this paper, we propose an artificially intelligent method to construct implosion velocity areal density direct-drive fast ignition by combining one-dimensional simulations machine learning methods. It is found that a large fuel mass high required high-gain fusion can be obtained simultaneously optimizing with less laser energy, taking full advantage separation processes scheme. applied double-cone scheme [Zhang et al., “Double-cone inertial confinement fusion,” Philos. Trans. R. Soc., A 378(2184), 20200015 (2020)]. An optimized proposed 1.30 g/cm2 215.7 μg energy 168 kJ. Two-dimensional further employed validate results. Our methods results may useful experiments toward

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ژورنال

عنوان ژورنال: Physics of Plasmas

سال: 2023

ISSN: ['1070-664X', '1527-2419', '1089-7674']

DOI: https://doi.org/10.1063/5.0159764