4.6 Article

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

期刊

ASTRONOMY & ASTROPHYSICS
卷 672, 期 -, 页码 -

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EDP SCIENCES S A
DOI: 10.1051/0004-6361/202245009

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magnetohydrodynamics (MHD); solar wind; Sun; activity; turbulence; waves

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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. Our model results, validated against PSP observations, reconstruct the bimodal structure of the solar wind and show reasonable agreement with previously established results and empirical constraints.
Aims. 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.Methods. 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 Alfven 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).Results. Our NIRwave solar wind model reconstructs the bimodal structure of the solar wind with slow and fast wind speeds of 410 kms(-1) and 650 kms(-1) respectively. The global mass-loss rate of our solar wind model is 2.6 x 10(-14) M-circle dot yr(-1). 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.

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