# Numerical Study of Divergence Cleaning and Coronal Heating/Acceleration Methods in the 3D COIN-TVD MHD Model

^{1}SIGMA Weather Group, State Key Laboratory for Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, China^{2}College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing, China

In the solar coronal numerical simulation, the coronal heating/acceleration and the magnetic divergence cleaning techniques are very important. The coronal–interplanetary total variation diminishing (COIN-TVD) magnetohydrodynamic (MHD) model is developed in recent years that can effectively realize the coronal–interplanetary three-dimensional (3D) solar wind simulation. In this study, we focus on the 3D coronal solar wind simulation by using the COIN-TVD MHD model. In order to simulate the heating and acceleration of solar wind in the coronal region, the volume heating term in the model is improved efficiently. Then, the influence of the different methods to reduce the ^{−9}. Although these numerical simulations are performed for the background solar corona, these methods are also suitable for the simulation of CME initiation and propagation.

## Introduction

The 3D COIN-TVD MHD model which was proposed in [1–3] and was improved in [4–8] in recent years can effectively realize the coronal–interplanetary 3D solar wind simulation. This model uses the TVD Lax–Friedrichs (TVD-LF) scheme uniformly in the corona region and the interplanetary space region, and a combination of Open Multi-Processing (OpenMP) based on shared memory and Message Passing Interface (MPI) based on distributed memory has been successfully used to study the solar wind background from the corona to the interplanetary space.

The solar energy is stored in the solar nucleus, and the generated radiant energy spreads from the inside to the outside. The solar temperature should theoretically decrease with the increase of the heliocentric distance. However, the temperature of the upper atmosphere corona is much higher than that of the lower atmosphere (photosphere). The reason for the abnormal warming of the atmosphere has not yet been investigated. Therefore, coronal heating/acceleration is a central issue in the solar coronal simulation and has been discussed by many researchers (e.g., [9–16]). Parker proposed a basic theory for the problem of heating an expanding solar corona [17–19]. Later, various methods for solar wind acceleration and coronal heating have been developed. For example, the Alfvén wave heating method (AHM) can accelerate solar wind through the exchange of momentum and energy between large-scale Alfvén wave turbulence and solar wind plasma [10]. The turbulent heating method (THM) assumes that the turbulent free energy is transformed into the energy accelerated by the solar wind when the turbulent free energy changes with the heliocentric distance [10]; By adding momentum and energy source terms to the MHD equations [16], the volume heating method (VHM) has been widely used in solar wind simulation (e.g., [15, 20, 21].

In the MHD simulation, the divergence of the magnetic field should be strictly controlled to zero. The nonzero divergence of the magnetic field can lead to the

In this study, we adopt the COIN-TVD model to simulate the coronal solar wind. Similar to [20, 21], we use the volume heating sources to model the solar wind heating/acceleration process in the simulation.

In *Governing Equations of Coronal Interplanetary-Total Variation Diminishing Model*, we introduce the equations of the COIN-TVD MHD model. *Mesh Grid System and Numerical Scheme* describes mesh grid system and boundary conditions. *Volume Heating Method and Magnetic Field Divergence Cleaning Methods* presents the VHM method and three magnetic field divergence processing methods. *Numerical Results* shows the results of numerical simulation and comparisons of three methods for processing magnetic field divergence. In *Conclusions and Discussions*, we make the conclusion and discussion.

## Governing Equations of Coronal–Interplanetary Total Variation Diminishing Model

The ideal MHD equations are used to simulate the coronal solar wind. Under the Corotating coordinate system, equations can be written as:

where ** B** is the magnetic field,

**is pressure,**

*P***is the unit tensor,**

*I**G*is the gravitational constant,

## Mesh Grid System and Numerical Scheme

### Mesh Grid System

In the spherical coordinate, the range of the calculation area is expressed as 1*r* is the radial distance from the solar center to the solar surface, _{S} at the inner boundary of 1R_{S} to 0.3636R_{S} at the outer boundary near 22.5R_{S}, and the total number of grids at *r*-direction is 224. In the latitudinal and longitudinal directions, the grid resolution is

### Numerical Scheme

In the COIN-TVD model, all of the physical quantities are computed from the TVD-LF numerical scheme in a face-centered grid structure (e.g., [7, 8]). And this scheme is performed in the six-component mesh grid system.

The inner boundary is located on the surface of the Sun, where the inner boundary setting depends on local fluid conditions (e.g., [2]; 2007, [16, 21, 33]). When ** v** = 0.

The Carrington Rotation (CR) 2199 is chosen for background establishment. The initial magnetic field *B*_{0} is given by using the potential field source surface (PFSS) model [35, 36], the spherical harmonics coefficients were used to obtain the initial PFSS solution is 6. And other initial parameters, such as plasma density _{0}, temperature T_{0}, and velocity **v**, are calculated by Parker’s solar wind flow solution [17]. The temperature and the number density on the solar surface are set to be 1.5 × 10^{6} K and 1.67 × 10^{8 }cm^{−3}, respectively. The boundary condition of the magnetic field at the inner surface also remains fixed all through the simulation. The parameters at the outer boundary are set according to the projected characteristic boundary conditions e.g., [32, 37, 38].

## Volume Heating Method and Magnetic Field Divergence Cleaning Methods

In this section, we introduce the numerical schemes of the volume heating method and three methods to constrain

### Volume Heating Method

Due to the limitations of observation and theory, there is no mature theoretical model to describe the mechanism of coronal heating and solar wind acceleration. Here, we use the volume heating method to solve the issue of coronal heating and solar wind acceleration. We add the source terms of momentum *S*_{M} and energy *Q*_{E} to the MHD Eq. 1, Eq. 2, Eq. 3, and Eq. 4 as follows:

According to the work in [7, 39–41], we set energy and momentum source terms as follows:

Here,

Here, *r* is the heliocentric distance, *Rs* is the solar radius, *R*_{SS} = 2.5R_{S}, and *,* respectively. Inspired by the Wang–Sheeley–Arge (WSA) model [42, 43], the solar wind speed is related to the magnetic field expansion factor

Following [20, 44, 45], the source term Q_{E} also contains a heat conduction term, the expression of the heat conduction term is _{E}, the partial differential in the formula decreases the calculation accuracy. And after the research in [45], many works (e.g., Feng, 2012, [21]; 2017 [33]) verify that without adding heat conduction item, the coronal solar wind can also be accelerated and heated.

### Powell Method

The Powell method to maintain the magnetic divergence cleaning constraint is given as follows.

Two divergence source terms,

In this way, the divergence of the magnetic field can be propagated to the boundary to reduce the numerical error of

This means that the

### Diffusive Method

The diffusive method is proposed to reduce the error of the magnetic divergence, in which an artificial diffusivity is added at each time step as

Here,

For satisfying the condition

### Composite Diffusive/Powell Method

We combined the Powell method and the diffusive method together in the MHD calculation in the composite diffusive/Powell method for the first time, and this method can further control the error of the magnetic field divergence.

The composite diffusive/Powell method adds two divergence source terms,

## Numerical Results

In this section, we show the numerical results of the solar coronal simulation from 1R_{S} to 22.5R_{S} for CR2199, which are obtained by executing the methods introduced in *Volume Heating Method and Magnetic Field Divergence Cleaning Methods*.

It takes about 100 h in physical time to obtain the steady state in our simulation. Figures 1,2 present the distribution of the magnetic field lines, the radial velocity, the number density and the temperature on the meridional plane at *Φ* = 180°–0° from model A and model B, respectively. From these figures, it can be seen that the high latitude areas always have fast speed, high temperature, and low density. On the contrary, the radial speed is slower, the temperature is lower, and the number density is higher at lower latitudes around the heliospheric current sheet (HCS), and this is the characteristic feature of the solar wind in the corona [47]. Model B is successful in simulating the acceleration and heating of the solar wind in the corona, as shown in Figure 2. Compared with Figure 1, we can find that both the radial speed and temperature in Figure 2 are higher than those in Figure 1 obviously. This result indicates that the VHM can accelerate and heat the coronal solar wind, and the parameters

**FIGURE 1**. The distribution of the radial speed *VR* (km/s) **(A)**, density *RO* × 10^{8} (/cm^{3}) **(B)** and temperature *TP* × 10^{6} (K) **(C)** on the meridional plane of *Φ* = 180°–0° from 1 to 22.5R_{s}, deduced from model A. The streamline represents the magnetic field lines.

**FIGURE 2**. The distribution of the radial speed *VR* (km/s) **(A)**, density *RO* × 10^{8} (/cm^{3}) **(B)** and temperature *TP* × 10^{6} (K) **(C)** on the meridional plane of *Φ* = 180°–0° from 1 to 22.5R_{s}, deduced from model B. The streamline represents the magnetic field lines.

Then, we present the simulation results of the coronal solar wind with three magnetic divergence cleaning methods. Figures 3–5, respectively, show the variation in the radial speed, the number density, and the temperature along heliocentric distance from 1 to 22.5 Rs with different latitudes of *θ* = −80° and *θ* = −10° at the same longitude of *Φ* = 0°, where *θ* = −80° locates at the open field region and *θ* = −10° locates at the HCS region. Comparing the three figures, we can find that the radial speed in the open field region is larger than that in the HCS region, the temperature is higher in the open field, and the number density is smaller in the high latitude region.

**FIGURE 3**. The distribution of radial speed *VR* (km/s) along heliocentric distance with different latitudes of *θ* = −80° **(A)** and *θ* = −10° **(B)** at the same longitude *Φ* = 0° from three divergence methods.

**FIGURE 4**. The distribution of density *RO* (/cm^{3}) along heliocentric distance with different latitudes of *θ* = −80° **(A)** and *θ* = −10° **(B)** at the same longitude *Φ* = 0° from three divergence methods.

**FIGURE 5**. The distribution of temperature *TP* × 10^{6} (K) along heliocentric distance with different latitudes of *θ* = −80° **(A)** and *θ* = −10° **(B)** at the same longitude *Φ* = 0° from three divergence methods.

The composite diffusive/Powell method which combines the diffusive method and the Powell method is our new try to handle the

To quantitatively see how

Here,

To investigate how the three magnetic divergence cleaning methods control the *Error*(** B**) among the three methods.

Figures 6,7 show the distributions of the *Error*(** B**) on the different meridional planes of

*Φ*= 180°–0° and

*Φ*= 270°–90°, respectively, for the steady-state solar wind. The three panels in Figures 6, 7 present the results from the composite diffusive/Powell method, the diffusive method and the Powell method, from left to right, respectively. It is obvious that the

*Error*(

**) deduced from the composite diffusive/Powell method is lower than that from the other two methods, on both meridional planes. This indicates that the composite diffusive/Powell method is the most effective method among the three methods in dealing with the magnetic field divergence.**

*B***FIGURE 6**. The distribution of *Error*(*B*) on the meridional plane of *Φ* = 180°–0° from 1 to 22.5R_{S,} from composite diffusive/Powell method **(A)**, diffusive method **(B)**, and Powell method **(C)**, respectively.

**FIGURE 7**. The distribution of *Error*(*B*) on the meridional plane of *Φ* = 270°–90° from 1 to 22.5R_{S}, the results from composite diffusive/Powell method **(A)**, diffusive method **(B)** and Powell method **(C)**, respectively.

Here, we use the following metric for measuring divergence, which was also adopted by other research studies (e.g., [33, 47]):

where *M* is the total number of grid points in the computational domain. We know that there are other metrics that can be used to measure the divergence. As pointed in [49], the metric defined by Eq. 14 may rely on the spatial resolution. However, in this simulation, we make the comparison among the three cases with the same mesh system and the same metric definition; therefore, the influence of the spatial resolution on the comparison of the metric by Eq. 14 can be ignored.

Figure 8 shows the evolution of the *Error*(** B**)

^{ave}with time deduced from the three methods. It can be recognized that the value of the

*Error*(

**)**

*B*^{ave}from the composite diffusive/Powell method is around 10

^{–8.7}–10

^{–8.5}, from the diffusive method is around 10

^{–8.6}–10

^{–8.2}, and from the Powell method is around 10

^{–8.6}–10

^{–7.1}. The composite diffusive/Powell method has the smallest

*Error*(

**)**

*B*^{ave}, and this method is a new try to maintain the magnetic divergence-free constraint. From Figure 8, we can also find that the

*Error*(

**)**

*B*^{ave}from the composite diffusive/Powell method and diffusive method is smaller than that from the Powell method obviously. Moreover, the

*Error*(

**)**

*B*^{ave}from the composite diffusive/Powell method keeps on decreasing after 60 h and is significantly smaller than that from the diffusive method near 100 h. Overall, we can find that all the divergence cleaning methods can keep the related errors under control, though the divergence errors of the Powell method are larger than those of the other methods, the divergence errors shown in Figures 6–8 are indeed small, and the largest worst number is 10

^{–7}, shown as the orange and red colors in Figures 6,7. The

*Error*(

**)**

*B*^{ave}from the Powell method is about 10

^{–7.3}, from the diffusive method is 10

^{–8.4}, and from the composite diffusive/Powell method is 10

^{–8.7}near 100 h. The composite diffusive/Powell method is the best method to reduce the error of magnetic divergence among the three methods in this research.

**FIGURE 8**. The temporal evolution of the Log_{10}*Error*(*B*)^{ave} from the three divergence cleaning methods.

## Conclusions and Discussions

In this study, by using the 3D COIN-TVD MHD model, we simulate the solar wind in the coronal region, in which the divergence cleaning and coronal heating/acceleration methods are included. The volume heating method is an effective way for coronal heating, in which the parameters can be adjusted according to the WSA model in the simulation of the coronal solar wind. In the COIN-TVD MHD model, increasing the parameters

For the divergence cleaning methods, here we choose the diffusive method, the Powell method and the composite diffusive/Powell method. We compared the numerical characteristics of the combination of each method for handling the divergence of the magnetic field and the COIN-TVD MHD model in the solar coronal simulation. The numerical results show that all of them can produce large-scale structured solar wind and reduce the divergence of the magnetic field more or less. The difference between the three divergence cleaning methods is summarized as follows:

1) The Powell method is relatively simple to apply. It only needs to add two items to the source term of the MHD equations. In this study, the Powell method can reduce the error of the relative magnetic field divergence, but it is less effective than the other two methods in dealing with magnetic divergence.

2) The diffusive method also has a good effect on reducing magnetic field divergence error in this study. It reduces the error of divergence by adding a source term in the induction equation and the

3) The composite diffusive/Powell method is a preliminary new try in this study, and it combines the Powell method and the diffusive method during the simulation. It has been proven that this composite method is the most efficient way to reduce the relative divergence errors among the three methods we used. Moreover, it also ensures the conservation of the MHD equations during the simulation.

In addition to the methods we mentioned, there are many other methods to simulate the coronal heating and the solar wind acceleration process and to control the divergence of the magnetic field. For example, both the Alfvén wave heating method and the turbulent heating method are effective for coronal heating. The Powell method can also company with other methods to control the magnetic divergence, which may be implemented in the future. Moreover, although these simulations are performed for the background solar corona, these methods can also be used for the simulation of CME initiation and propagation in the interplanetary space.

## Data Availability Statement

The original contributions presented in the study are included in the article/supplementary files, further inquiries can be directed to the corresponding author/s.

## Author Contributions

FS provided the thesis research topic, FS and YL provided the code for the three-dimensional solar wind numerical simulation. CL modified the code, ran the program code, and drew pictures based on the data. FS, CL, and MZ participated in the analysis of the results and the writing of the manuscript. XL modified the manuscript.

## Funding

This work was jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences, Grant no. XDB 41000000, National Natural Science Foundation of China (Grant nos 41774184 and 41974202), and State Key Laboratory of Special Research Fund Project.

## Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

## Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

## Acknowledgments

We acknowledge the use of synoptic magnetogram from the Global Oscillation Network Group (GONG). The numerical simulation of the model uses Tianhe-1A supercomputing machine.

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Keywords: MHD simulation, corona heating and acceleration, magnetic divergence cleaning, solar wind, volumn heating

Citation: Liu C, Shen F, Liu Y, Zhang M and Liu X (2021) Numerical Study of Divergence Cleaning and Coronal Heating/Acceleration Methods in the 3D COIN-TVD MHD Model. *Front. Phys.* **9**:705744. doi: 10.3389/fphy.2021.705744

Received: 06 May 2021; Accepted: 13 July 2021;

Published: 30 July 2021.

Edited by:

Qiang Hu, University of Alabama in Huntsville, United StatesReviewed by:

Keiji Hayashi, Stanford University, United StatesMehmet Yalim, University of Alabama in Huntsville, United States

Copyright © 2021 Liu, Shen, Liu, Zhang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Fang Shen, fshen@spaceweather.ac.cn