arXiv subjects

Rong Lin

Publications and source records attributed to Rong Lin.

5 recordsLinked to original sources

arXiv2025

Generation of proton beams at switchback boundary-like rotational discontinuities in the solar wind

Alfv\'enic rotational discontinuities (RDs) are abundant in the inner heliosphere and can be used to model the boundary of switchbacks, i.e. Alfv\'enic magnetic kinks. To investigate the effects of RDs on proton kinetics, we model a pair of switchback-boundary-like RDs with a hybrid Particle-In-Cell (PIC) approach in a 2D system. We find that, at one of the boundary RDs, a significant population of protons remains trapped over long times, creating a secondary beam-like component with temperature anisotropy $T_\perp/T_\|\gtrsim4$ in the proton velocity distribution function that excites ion cyclotron waves within the downstream portion of the transition layer. Further analysis suggests that the static electric field in the vicinity of the RD is the key factor in trapping the protons. This work indicates that switchback boundaries could represent a viable environment for the creation of proton beams in the heliosphere; it also highlights the need to investigate RD sub-structures, especially the embedded current systems of interplanetary RDs. Finally, this paper underscores the importance of high-resolution observations of the solar wind velocity distributions around RDs.

physics.space-ph↗

arXiv2025

Chaotic Motion of Ions In Finite-amplitude Low-frequency Alfv\'en Waves

Finite-amplitude low-frequency Alfv\'en waves (AWs) are ubiquitous in space plasmas, where they play a key role in the transport and dissipation of energy, particularly in the heating of ions in the solar corona and solar wind. In this study, we investigate the nonlinear interaction between ions and obliquely propagating AWs. When the wave amplitude and propagation angle lie within specific ranges, ion motion becomes chaotic. We quantify this behavior using the maximum Lyapunov exponent ($\lambda_{\mathrm{m}}$) and define a new parameter, the Chaos Ratio (CR), to describe the fraction of chaotic particles across different initial states. The global chaos threshold is determined as the contour CR = 0.01. Analysis of magnetic moment variations reveals that the physical origin of chaos is pitch-angle scattering induced by \textit{wave-driven field-line curvature} (WFLC), which disrupts adiabatic invariance and leads to stochastic ion energization. The onset condition for chaos can be expressed by an effective relative curvature radius, $P_{eff.} < 25$. This analytical criterion delineates the boundary of the chaotic region in the ($k_x$, $k_z$, $B_w$) parameter space and agrees well with numerical results. The identified WFLC mechanism provides a new physical pathway for converting macroscale Alfv\'enic disturbances into microscopic ion heating. \textbf{This analysis offers a simplified model that illustrates a plausible ion energization mechanism in Alfv\'enic turbulent plasmas}, including those associated with solar wind switchbacks and coronal fluctuations. These results highlight a universal chaotic process that may underlie stochastic heating in heliospheric and astrophysical plasmas.

physics.plasm-ph↗

arXiv2025

SIP-IFVM: A time-evolving coronal model with an extended magnetic field decomposition strategy

Time-evolving magnetohydrodynamic (MHD) coronal modeling, driven by a series of time-dependent photospheric magnetograms, represents a new generation of coronal simulations. This approach offers greater realism compared to traditional coronal models constrained by a static magnetogram. However, its practical application is seriously limited by low computational efficiency and poor numerical stability. Therefore, we propose an extended magnetic field decomposition strategy and implement it in the implicit MHD model to develop a coronal model that is both efficient and numerically stable enough for simulating the long-term evolutions of the global corona. The traditional decomposition strategies split the magnetic field into a time-invariant potential field and a time-dependent component $\mathbf{B}_1$. It works well for quasi-steady-state coronal simulations where $\left|\mathbf{B}_1\right|$ is typically small. However, as the inner-boundary magnetic field evolves, $\left|\mathbf{B}_1\right|$ can grow significantly larger and its discretization errors often lead to nonphysical negative thermal pressure, ultimately causing the code to crash. In this paper, we mitigate such undesired situations by introducing a temporally piecewise-constant variable to accommodate part of the non-potential field and remain $\left|\mathbf{B}_1\right|$ consistently small throughout the simulations. We incorporate this novel magnetic field decomposition strategy into our implicit MHD coronal model and apply it to simulate the evolution of coronal structures within 0.1 AU over two solar-maximum Carrington rotations. The results show that this coronal model effectively captures observations and performs more than 80 times faster than real time using only 192 CPU cores, making it well-suited for practical applications in simulating the time-evolving corona.

astro-ph.SR↗

arXiv2023

Prediction of solar wind speed by applying convolutional neural network to potential field source surface (PFSS) magnetograms

An accurate solar wind speed model is important for space weather predictions, catastrophic event warnings, and other issues concerning solar wind - magnetosphere interaction. In this work, we construct a model based on convolutional neural network (CNN) and Potential Field Source Surface (PFSS) magnetograms, considering a solar wind source surface of $R_{\rm SS}=2.5R_\odot$, aiming to predict the solar wind speed at the Lagrange 1 (L1) point of the Sun-Earth system. The input of our model consists of four Potential Field Source Surface (PFSS) magnetograms at $R_{\rm SS}$, which are 7, 6, 5, and 4 days before the target epoch. Reduced magnetograms are used to promote the model's efficiency. We use the Global Oscillation Network Group (GONG) photospheric magnetograms and the potential field extrapolation model to generate PFSS magnetograms at the source surface. The model provides predictions of the continuous test dataset with an averaged correlation coefficient (CC) of 0.52 and a root mean square error (RMSE) of 80.8 km/s in an eight-fold validation training scheme with the time resolution of the data as small as one hour. The model also has the potential to forecast high speed streams of the solar wind, which can be quantified with a general threat score of 0.39.

astro-ph.SR↗

arXiv2021

Power Anisotropy, Dispersion Signature and Turbulence Diffusion Region in the 3D Wavenumber Domain of Space Plasma Turbulence

We explore the multi-faceted important features of turbulence (e.g., anisotropy, dispersion, diffusion) in the three-dimensional (3D) wavenumber domain ($k_\parallel$, $k_{\perp,1}$, $k_{\perp,2}$), by employing the k-filtering technique to the high-quality measurements of fields and particles from the MMS multi-spacecraft constellation. We compute the 3D power spectral densities (PSDs) of magnetic and electric fluctuations (marked as $\rm{PSD}(\delta \mathbf{B}(\mathbf{k}))$ and $\rm{PSD}(\delta \mathbf{E}'_{\langle\mathbf{v}_\mathrm{i}\rangle}(\mathbf{k}))$), both of which show a prominent spectral anisotropy in the sub-ion range. We give the first 3D image of the bifurcation between power spectra of the electric and magnetic fluctuations, by calculating the ratio between $\rm{PSD}(\delta \mathbf{E}'_{ \langle\mathbf{v}_\mathrm{i}\rangle}(\mathbf{k}))$ and $\rm{PSD}(\delta \mathbf{B}(\mathbf{k}))$, the distribution of which is related to the non-linear dispersion relation. We also compute the ratio between electric spectra in different reference frames defined by the ion bulk velocity, that is $\mathrm{PSD}(\delta{\mathbf{E}'_{\mathrm{local}\ \mathbf{v}_\mathrm{i}}})/\mathrm{PSD}(\delta{\mathbf{E}'_{ \langle\mathbf{v}_\mathrm{i}\rangle}})$, to visualize the turbulence ion diffusion region (T-IDR) in wavenumber space. The T-IDR has an anisotropy and a preferential direction of wavevectors, which is generally consistent with the plasma wave theory prediction based on the dominance of kinetic Alfv\'en waves (KAW). This work manifests the worth of the k-filtering technique in diagnosing turbulence comprehensively, especially when the electric field is involved.

physics.plasm-ph↗