摘要
三维磁流体力学(MHD)数值模拟是行星际太阳风研究的重要手段.本文发展了一种由多种观测数据驱动的三维行星际太阳风MHD数值模型.模型的计算区域为0.1 AU到1AU附近,使用Lax-Friedrich差分格式在六片网格系统中进行数值求解.边界条件中磁场使用GONG台站观测的光球磁图外推获得,密度通过LASCO观测的白光偏振亮度反演得到,速度根据以上两种观测数据并利用一种基于人工神经网络技术(ANN)的方法得到,温度通过自洽方法根据磁场和密度导出.利用该模型模拟了第2062卡灵顿周(CR2062)时期的行星际太阳风,模拟结果显示出丰富的观测特征,并与OMNI以及Ulysses的实际观测值符合得较好.该模型可用于提供接近真实的行星际太阳风,有助于提高空间天气预报的精度.
Three-dimensional Magnetohydrodynamics(MHD)modeling is a key method for studying the interplanetary solar wind.This paper develops a new solar wind MHD model driven by multiple observations.The computation region of this model is from 0.1 Astronomical Unit(AU)to 1 AU.The model solves the ideal MHD equations in a six-component grid system by using the Total Variation Diminution(TVD)Lax-Friedrich scheme.In the new model,the physical parameters at the inner boundary are determined as following:the magnetic field is derived using the magnetogram synoptic map from GONG;the electron density is derived from the Polarized Brightness(PB)observations from LASCO;the velocity is deduced using an Artificial Neural Network(ANN)tactic with both the magnetogram and PB observations,and the temperature is derived from the magnetic field and electron density by a self-consistent method.We use this model to simulate the interplanetary solar wind during CR2062.The results show various observational characteristics,and are in good agreement with the OMNI and Ulysses observations.Thus,this model can be used to provide more realistic interplanetary solar wind and will be helpful for the research on space weather prediction.
作者
杨易
沈芳
杨子才
YANG Yi;SHEN Fang;YANG Zicai(National Space Science Center,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049;Institute of Space Science and Applied Technology,Harbin Institute of Technology,Shenzhen 518055)
出处
《空间科学学报》
CAS
CSCD
北大核心
2020年第3期305-314,共10页
Chinese Journal of Space Science
基金
国家自然科学基金项目(41774184,41874202,41731067)
国家重点实验室专项研究基金项目
国家“万人计划”青年拔尖人才项目共同资助。
关键词
MHD模拟
行星际太阳风
数据驱动
MHD simulation
Interplanetary solar wind
Observations driven
