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- W4323570360 abstract "In the double-cone ignition (DCI) inertial confinement fusion scheme, head-on collision of high density plasma jets is one of the most distinguished feature when compared with other schemes. However, the application of traditional hydrodynamic simulation methods become limited. To overcome such limitations, we propose a new simulation method for large-scale high density plasmas. This method takes advantages of modern particle-in-cell simulation techniques and binary Monte Carlo collisions, including both long-range collective electromagnetic fields and short-range particle-particle interactions. Especially, in this method, the restrictions of simulation grid size and time step, which usually appear in a fully kinetic description, are eliminated. In addition, collisional coupling and state-dependent coefficients are also removed in this method. The correctness and robustness of the new simulation method are verified, by comparing with fully kinetic simulations at small scales and purely hydrodynamic simulations at large scale. Following the conceptual design of the DCI scheme, the colliding process of two plasma jets with initial density of 100 g/cc, initial thermal temperature of 70 eV, and counter-propagating velocity at 300 km/s is investigated using this new method. Quantitative values, including density increment, increased plasma temperature, confinement time at stagnation and conversion efficiency from the colliding kinetic energy to thermal energy, are obtained. These values agree with the recent experimental measurements at a reasonable range." @default.
- W4323570360 created "2023-03-09" @default.
- W4323570360 creator A5025790863 @default.
- W4323570360 creator A5033589973 @default.
- W4323570360 date "2023-03-06" @default.
- W4323570360 modified "2023-09-29" @default.
- W4323570360 title "Head-on collision of large-scale high density plasmas jets: a first-principle kinetic simulation approach" @default.
- W4323570360 doi "https://doi.org/10.48550/arxiv.2303.03011" @default.
- W4323570360 hasPublicationYear "2023" @default.
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