S. Wei, F. Wu, Y. Zhu, J. Yang, L. Zeng, X. Li, J. Zhang
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引用次数: 0
Abstract
High energy gain is essential for the energy production via laser fusion. In this paper, an efficient method combining the hydrodynamic simulations and the machine learning algorithms is proposed to optimize the laser pulse for fast ignition simulations. An analytical model between the energy gain and compressed plasma parameters is derived as the evaluate function for the optimizations. An implosion with a fusion gain more than 100 is achieved with a total laser energy about 730 kJ in the spherical fast ignition scheme or 300 kJ in the double-cone ignition (DCI) scheme in one-dimensional simulations. The implosion data generated during the course of optimization is found to be suitable for the training of a deep neural network (DNN) surrogate model. In the future, this DNN surrogate model could be transfer learned with experimental feedback and optimize the laser pulse with a higher accuracy.
期刊介绍：
The Journal of Fusion Energy features original research contributions and review papers examining and the development and enhancing the knowledge base of thermonuclear fusion as a potential power source. It is designed to serve as a journal of record for the publication of original research results in fundamental and applied physics, applied science and technological development. The journal publishes qualified papers based on peer reviews.
This journal also provides a forum for discussing broader policies and strategies that have played, and will continue to play, a crucial role in fusion programs. In keeping with this theme, readers will find articles covering an array of important matters concerning strategy and program direction.
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