NIRwave: A wave-turbulence-driven solar wind model constrained by PSP observations

TL;DR

NIRwave is a new 3D MHD solar wind model driven by wave turbulence, validated with PSP data, accurately reproducing key solar wind features and improving upon previous models.

Contribution

This work introduces a wave-turbulence-driven heating mechanism into a 3D MHD model of the solar wind, constrained by recent PSP observations.

Findings

Reproduces bimodal solar wind speeds of 410 km/s and 650 km/s.

Global mass-loss rate of 2.6e-14 solar masses per year.

Better agreement with observational data than previous polytropic models.

Abstract

We generate a model description of the solar wind based on an explicit wave-turbulence-driven heating mechanism, and constrain our model with observational data. We included an explicit coronal heating source term in the general 3D magnetohydrodynamic code NIRVANA to simulate the properties of the solar wind. The adapted heating mechanism is based on the interaction and subsequent dissipation of counter-propagating Alfv\'en waves in the solar corona, accounting for a turbulent heating rate Q_p. The solar magnetic field is assumed to be an axisymmetric dipole with a field strength of 1 G. Our model results are validated against observational data taken by the Parker Solar Probe (PSP). Our NIRwave solar wind model reconstructs the bimodal structure of the solar wind with slow and fast wind speeds of 410 km/s and 650 km/s respectively. The global mass-loss rate of our solar wind model is 2.6e-14 solar masses per year. Despite implementing simplified conditions to represent the solar magnetic field, the solar wind parameters characterising our steady-state solution are in reasonable agreement with previously established results and empirical constraints. The number density from our wind solution is in good agreement with the derived empirical constraints, with larger deviations for the radial velocity and temperature. In a comparison to a polytropic wind model generated with NIRVANA, we find that our NIRwave model is in better agreement with the observational constraints that we derive.