Abstract Details
24 / 2023-03-30 21:27:23
Efficient designing laser profiles and target structures for laser-driven fusion
Abstract Accepted
Laser-driven inertial confinement fusion is an important approach to achieve controllable nuclear fusion. It applies high-power laser pulses or X-rays to ablate outer surface of a spherical shell containing the fusion fuel, leading to a centripetal implosion and an increase in pressure and temperature of the fuel. In order to reach the Lawson criterion and thus realize a self-sustaining burning plasma, we have to compress the fuel to several hundred times of the solid density and rise the target temperature to over 5 keV.
Firstly, to realize an efficient implosion, the driven laser pulse and target structure are designed using a random walk method for a given laser energy. It can quickly optimize the laser pulse and target structure parameters for an efficient isentropic compression of the target. A correlation matrix can also be constructed to analyze the correlation between the parameters.
Secondly, we propose a hybrid optimization method by combining the random walk and the Bayesian methods, to further improve the optimization efficiency. The series of laser pulses and target structures that produces relatively high areal density obtained by the random walk optimization are used as the basic sampling data of the Bayesian optimization. It can greatly reduce the desired number of samples for Bayesian optimizations and the Bayesian optimization also makes up for the small step size and low efficiency of the random walk method in the later stage of optimization, and reduces the randomness in the optimization process. The hybrid optimization method greatly improves the optimization efficiency, and has been applied to the experiments of the Double-Cone ignition scheme. We believe that it will play a greater role in the future laser fusion experiments.
Firstly, to realize an efficient implosion, the driven laser pulse and target structure are designed using a random walk method for a given laser energy. It can quickly optimize the laser pulse and target structure parameters for an efficient isentropic compression of the target. A correlation matrix can also be constructed to analyze the correlation between the parameters.
Secondly, we propose a hybrid optimization method by combining the random walk and the Bayesian methods, to further improve the optimization efficiency. The series of laser pulses and target structures that produces relatively high areal density obtained by the random walk optimization are used as the basic sampling data of the Bayesian optimization. It can greatly reduce the desired number of samples for Bayesian optimizations and the Bayesian optimization also makes up for the small step size and low efficiency of the random walk method in the later stage of optimization, and reduces the randomness in the optimization process. The hybrid optimization method greatly improves the optimization efficiency, and has been applied to the experiments of the Double-Cone ignition scheme. We believe that it will play a greater role in the future laser fusion experiments.
Important Date
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Conference Date
Jun 05
2023
toJun 09
2023
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Apr 30 2023
Early Bird Registration
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May 01 2023
Abstract Submission Deadline
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May 01 2023
Abstract Notification of Acceptance
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May 01 2023
Draft paper submission deadline
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May 31 2023
Registration deadline
Sponsored By
Science and Technology on Plasma Physics Laboratory
Department of Astronomy, Beijing Normal University
Department of Astronomy, Beijing Normal University
Organized By
Matter and Radiation at Extremes
Institute of Fluid Physics, China Academy of Engineering Physics, China
Institute of Applied Physics and Computational Mathematics, Beijing, China
Institute of Fluid Physics, China Academy of Engineering Physics, China
Institute of Applied Physics and Computational Mathematics, Beijing, China
Contact Information
- Contact
- mr******@aip.org
- +86********
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