Author:
Liu Pan,Zhou Yinzhao,Xiong Wenbin,Liu Chencheng,Liu Jie,Liu Gongjie,Tang Zhangchun,Gao Qiang
Abstract
Abstract
Volume ignition is a method of igniting a fuel as a whole by simultaneously achieving ignition conditions throughout the fuel zone. The basic criterion for ignition is that the thermonuclear energy is greater than the energy leakage at the fuel boundary, resulting in self-sustaining heating and deep combustion. Deuterium-tritium fuels are wrapped in medium to high Z media to reduce radiative leakage and achieve lower-temperature holistic ignition and non-equilibrium combustion, ultimately allowing the fuel to achieve high combustion efficiency. Volume ignition is the use of energy balance relations under the local thermodynamic equilibrium approximation to establish the energy balance equation for thermonuclear systems, and the system ignition threshold is obtained by solving this equation. By understanding the physical process, we believe that the non-equilibrium process is universal to the volume ignition process. Changes in external factors (density, boundaries, albedo, etc.) at the moment of ignition can have a significant impact on the development direction of the system, and an ignition system with a large surface density can nevertheless withstand a large amount of reverse work and continue to burn. The design of the ignition target tries to avoid these factors through margin design, but conversely, the rational use of these laws can further improve the design margin of the capsule. With the aid of big data, the volume ignition method is easier to calculate and has a shorter iteration time. The traditional way is to propose a model, set the material, and then perform the calculation while using big data can set any model and material for calculation. In this paper, a simple comparison will be made to find out that the efficiency of the physical design of a volume ignition target will be effectively improved with the aid of big data. Volume ignition targets can be used not only in Z-pinch-driven systems but also for laser-driven volume ignition.
Subject
General Physics and Astronomy
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Artificial intelligence-assisted design of load-driver end docking in Z-FFR fusion explosion chambers;Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023);2023-10-25
2. Natural Language Processing Combined with Digital Twins to Drive Fusion Physics Design;Proceedings of the 2023 3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum;2023-09-22
3. Physical design for driven device of Z-FFR based on Machine Learning;2022 15th International Symposium on Computational Intelligence and Design (ISCID);2022-12