Two-dimensional radiation hydrodynamic simulations of high-speed head-on collisions between high-density plasma jets

姓名
邮箱
手机号码
标题
留言内容
验证码

Abstract

Head-on collisions of plasma jets are common hydrodynamic phenomena in astrophysical and laser-plasma interaction processes. Deriving scaling relationships between colliding plasmas and initial conditions of plasma jets is of great significance in optimizing the design and the data analysis of the relevant experiments. Double-cone ignition (DCI) scheme is an excellent platform for studying plasma jets’ collision, since the collision between high-speed, high-density plasma jets can be easily generated and characterized in both simulations and experiments. In this work, we employ the upgraded two-dimensional arbitrary Eulerian-Lagrange (ALE) program MULTI-2D to simulate the collision process of plasma jets with high speed (≥100 km/s) and high density (≥10 g/cm ³). Using the database obtained from the simulations, hydrodynamic scaling laws describing the collision process of plasma jets are derived by the Bayesian inference method in machine learning. The Bayesian inference method not only has the parameter estimation function of traditional least square method, but also possesses other potential advantages such as giving the probability distribution of estimated parameters. Numerical results show that the collision of plasma jets with open boundaries is easy to form an isochoric plasma distribution with high-density. Increasing the initial density and velocity of the plasma jet is helpful in enhancing the density and temperature of the colliding plasma. Increasing the initial temperature of plasma jet is beneficial to achieving colliding plasmas with a higher temperature, while leading plasma density and pressure to decrease after head-on collision. When the initial density, temperature and velocity of the plasma jets are set to be 15 g/cm ³, 30 eV and 300 km/s, respectively, the colliding plasma density can reach more than 300 g/cm ³. This is very favorable for the following fast electron heating process in the double-cone ignition (DCI) scheme. The issue about quantum degeneracy after collision is discussed in this work. Under the typical initial conditions of plasma jets in DCI scheme ( $100\,\,\rm{km}/\mathrm{s}\leqslant {V}_{0}\leqslant 500\,\,\rm{km}/\mathrm{s},10\,\,\rm{eV}\leqslant {T}_{0}\leqslant 100\,\,\rm{eV},10\,\,\mathrm{g}/\mathrm{c}\mathrm{m}^3\leqslant {\rho }_{0}\leqslant 50\,\,\mathrm{g}/\mathrm{c}\mathrm{m}^3)$ , both quantum degenerate plasma and classical non-degenerate plasma can be obtained in a temperature range between $ 0.3{T}_{F} $ (Fermi temperature) and $ 3{T}_{F} $ . By comparing the plasma temperature with the Fermi temperature of the collision, the criterion for achieving quantum degenerate plasma or non-degenerate plasma under given initial conditions is obtained with the help of the derived hydrodynamic scaling laws. The criterion shows that higher initial velocity, higher temperature and lower density of plasma jets are required if we want to obtain non-degenerate plasma after collision.

Keywords:

Authors and contacts

Corresponding author:Wu Fu-Yuan,fuyuan.wu@sjtu.edu.cn; Zhang Jie,jzhang1@sjtu.edu.cn

Funds:This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA25051200), and Startup Fund for Young Faculty at SJTU (Grant No. 21X010500627).

FullText HTML: translate this paragraph

References

[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]

Cited By

DownLoad: Full-Size Img PowerPoint

对撞等离子体参数	对撞等离子体参数和喷流初始参数的流体定标关系
ρ/(g·cm^–3)	$ \rho {\text{ = }}0.074\rho _0^{0.74}V_0^{1.33}T_0^{ - 0.32} $
ρR/(g·cm^–2)	$ \rho R{\text{ = }}0.002\rho _0^{0.85}V_0^{0.91}T_0^{ - 0.24} $
T/eV	$ T{\text{ = }}0.437\rho _0^{0.12}V_0^{1.16}T_0^{0.17} $
P/Mbar	$ P{\text{ = }}0.020\rho _0^{0.86}V_0^{2.56}T_0^{ - 0.21} $

DownLoad: CSV

[1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]

[1]	Song Rui, Liu Xue-Mei, Wang Hai-Bin, Lü Hao, Song Xiao-Yan.Hardness prediction of WC-Co cemented carbide based on machine learning model. Acta Physica Sinica, 2024, 73(12): 126201.doi:10.7498/aps.73.20240284
[2]	Zhang Xu, Ding Jin-Min, Hou Chen-Yang, Zhao Yi-Ming, Liu Hong-Wei, Liang Sheng.Machine learning based laser homogenization method. Acta Physica Sinica, 2024, 73(16): 164205.doi:10.7498/aps.73.20240747
[3]	Zhang Jia-Hui.Machine learning forin silicoprotein research. Acta Physica Sinica, 2024, 73(6): 069301.doi:10.7498/aps.73.20231618
[4]	Luo Qi-Rui, Shen Yi-Fan, Luo Meng-Bo.Computer simulation and machine learning of polymer collapse and critical adsorption phase transitions. Acta Physica Sinica, 2023, 72(24): 240502.doi:10.7498/aps.72.20231058
[5]	Ying Da-Wei, Zhang Si-Hui, Deng Shu-Jin, Wu Hai-Bin.Single shot imaging for cold atoms based on machine learning. Acta Physica Sinica, 2023, 72(14): 144201.doi:10.7498/aps.72.20230449
[6]	Zhang Yi-Fan, Ren Wei, Wang Wei-Li, Ding Shu-Jian, Li Nan, Chang Liang, Zhou Qian.Machine learning combined with solid solution strengthening model for predicting hardness of high entropy alloys. Acta Physica Sinica, 2023, 72(18): 180701.doi:10.7498/aps.72.20230646
[7]	Guo Wei-Chen, Ai Bao-Quan, He Liang.Reveal flocking phase transition of self-propelled active particles by machine learning regression uncertainty. Acta Physica Sinica, 2023, 72(20): 200701.doi:10.7498/aps.72.20230896
[8]	Liu Ye, Niu He-Ran, Li Bing-Bing, Ma Xin-Hua, Cui Shu-Wang.Application of machine learning in cosmic ray particle identification. Acta Physica Sinica, 2023, 72(14): 140202.doi:10.7498/aps.72.20230334
[9]	Guan Xing-Yue, Huang Heng-Yan, Peng Hua-Qi, Liu Yan-Hang, Li Wen-Fei, Wang Wei.Machine learning in molecular simulations of biomolecules. Acta Physica Sinica, 2023, 72(24): 248708.doi:10.7498/aps.72.20231624
[10]	Yang Zhang-Zhang, Liu Li, Wan Zhi-Tao, Fu Jia, Fan Qun-Chao, Xie Feng, Zhang Yi, Ma Jie.Combining machine learning algorithm to improve prediction performance of ab initio method for vibrational energy spectra of HF/HBr/H³⁵Cl/Na³⁵Cl. Acta Physica Sinica, 2023, 72(7): 073101.doi:10.7498/aps.72.20221953
[11]	Li Wei, Long Lian-Chun, Liu Jing-Yi, Yang Yang.Classification of magnetic ground states and prediction of magnetic moments of inorganic magnetic materials based on machine learning. Acta Physica Sinica, 2022, 71(6): 060202.doi:10.7498/aps.71.20211625
[12]	Ai Fei, Liu Zhi-Bing, Zhang Yuan-Tao.Numerical study of discharge characteristics of atmospheric dielectric barrier discharges by integrating machine learning. Acta Physica Sinica, 2022, 71(24): 245201.doi:10.7498/aps.71.20221555
[13]	Kang Jun-Feng, Feng Song-Jiang, Zou Qian, Li Yan-Jie, Ding Rui-Qiang, Zhong Quan-Jia.Machine learning based method of correcting nonlinear local Lyapunov vectors ensemble forecasting. Acta Physica Sinica, 2022, 71(8): 080503.doi:10.7498/aps.71.20212260
[14]	Zhang Jia-Wei, Yao Hong-Bo, Zhang Yuan-Zheng, Jiang Wei-Bo, Wu Yong-Hui, Zhang Ya-Ju, Ao Tian-Yong, Zheng Hai-Wu.Self-powered sensing based on triboelectric nanogenerator through machine learning and its application. Acta Physica Sinica, 2022, 71(7): 078702.doi:10.7498/aps.71.20211632
[15]	Wan Xin-Yang, Zhang Ye-Hui, Lu Shuai-Hua, Wu Yi-Lei, Zhou Qiong-Hua, Wang Jin-Lan.Machine learning accelerated search for new double perovskite oxide photocatalysis. Acta Physica Sinica, 2022, 71(17): 177101.doi:10.7498/aps.71.20220601
[16]	Zhang Zhe, Yuan Xiao-Hui, Zhang Yi-Hang, Liu Hao, Fang Ke, Zhang Cheng-Long, Liu Zheng-Dong, Zhao Xu, Dong Quan-Li, Liu Gao-Yang, Dai Yu, Gu Hao-Chen, Li Yu-Tong, Zheng Jian, Zhong Jia-Yong, Zhang Jie.Efficient energy transition from kinetic to internal energy in supersonic collision of high-density plasma jets from conical implosions. Acta Physica Sinica, 2022, 71(15): 155201.doi:10.7498/aps.71.20220361
[17]	Lin Jian, Ye Meng, Zhu Jia-Wei, Li Xiao-Peng.Machine learning assisted quantum adiabatic algorithm design. Acta Physica Sinica, 2021, 70(14): 140306.doi:10.7498/aps.70.20210831
[18]	Chen Jiang-Zhi, Yang Chen-Wen, Ren Jie.Machine learning based on wave and diffusion physical systems. Acta Physica Sinica, 2021, 70(14): 144204.doi:10.7498/aps.70.20210879
[19]	Liu Wu, Zhu Cheng-Wan, Li Hao-Tian, Zhao Su-Ling, Qiao Bo, Xu Zheng, Song Dan-Dan.Optimization of Ga content gradient in Cu(In,Ga)Se₂solar cells through machine learning and device simulation. Acta Physica Sinica, 2021, 70(23): 238802.doi:10.7498/aps.70.20211234
[20]	Yang Zi-Xin, Gao Zhang-Ran, Sun Xiao-Fan, Cai Hong-Ling, Zhang Feng-Ming, Wu Xiao-Shan.High critical transition temperature of lead-based perovskite ferroelectric crystals: A machine learning study. Acta Physica Sinica, 2019, 68(21): 210502.doi:10.7498/aps.68.20190942

Catalog

Vol. 71, Issue 22 - 2022-11-20

Metrics

Abstract views:4665
PDF Downloads:166
Cited By:0

Search

Article