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Kolmogorov versus Iroshnikov-Kraichnan spectra: Consequences for ion heating in the solar wind

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 Added by Chung-Sang Ng
 Publication date 2011
  fields Physics
and research's language is English




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Whether the phenomenology governing MHD turbulence is Kolmogorov or Iroshnikov-Kraichnan (IK) remains an open question, theoretically as well as observationally. The ion heating profile observed in the solar wind provides a quantitative, if indirect, observational constraint on the relevant phenomenology. Recently, a solar wind heating model based on Kolmogorov spectral scaling has produced reasonably good agreement with observations, provided the effect of turbulence generation due to pickup ions is included in the model. Without including the pickup ion contributions, the Kolmogorov scaling predicts a proton temperature profile that decays too rapidly beyond a radial distance of 15 AU. In the present study, we alter the heating model by applying an energy cascade rate based on IK scaling, and show that the model yields higher proton temperatures, within the range of observations, with or without the inclusion of the effect due to pickup ions. Furthermore, the turbulence correlation length based on IK scaling seems to follow the trend of observations better.



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116 - S. C. Chapman , B. Hnat 2006
Solar wind turbulence is dominated by Alfv{e}nic fluctuations but the power spectral exponents somewhat surprisingly evolve toward the Kolmogorov value of -5/3, that of hydrodynamic turbulence. We show that at 1AU the turbulence decomposes linearly into two coexistent components perpendicular and parallel to the local average magnetic field. The first of these is consistent with propagating Alfv{e}n wavepackets and shows the scaling expected of Alfv{e}nic turbulence, namely Irosnikov- Kraichnan. The second shows Kolmogorov scaling which we also find in the number and magnetic energy density, and Poynting flux.
In recent years, a phenomenological solar wind heating model based on a turbulent energy cascade prescribed by the Kolmogorov theory has produced reasonably good agreement with observations on proton temperatures out to distances around 70 AU, provided the effect of turbulence generation due to pickup ions is included in the model. In a recent study [Ng et al., J. Geophys. Res., 115, A02101 (2010)], we have incorporated in the heating model the energy cascade rate based on Iroshnikov-Kraichnan (IK) scaling. We showed that the IK cascade rate can also produce good agreement with observations, with or without the inclusion of pickup ions. This effect was confirmed both by integrating the model using average boundary conditions at 1 AU, and by applying a method [Smith et al., Astrophys. J., 638, 508 (2006)] that uses directly observed values as boundary conditions. The effects due to pickup ions is found to be less important for the IK spectrum, which is shallower than the Kolmogorov spectrum. In this paper, we will present calculations of the pickup ions effect in more details, and discuss the physical reason why a shallower spectrum generates less waves and turbulence.
Various remote sensing observations have been used so far to probe the turbulent properties of the solar wind. Using the recently reported density modulation indices that are derived using angular broadening observations of Crab Nebula during 1952 - 2013, we measured the solar wind proton heating using the kinetic $rm Alfvacute{e}n$ wave dispersion equation. The estimated heating rates vary from $approx 1.58 times 10^{-14}$ to $1.01 times 10^{-8} ~rm erg~ cm^{-3}~ s^{-1}$ in the heliocentric distance range 5 - 45 $rm R_{odot}$. Further, we found that heating rates vary with the solar cycle in correlation with density modulation indices. The models derived using in-situ measurements (for example, electron/proton density, temperature, and magnetic field) that the recently launched Parker Solar Probe observes (planned closest perihelia $rm 9.86~ R_{odot}$ from the center of the Sun) are useful in the estimation of the turbulent heating rate precisely. Further, we compared our heating rate estimates with the one derived using previously reported remote sensing and in-situ observations.
We investigate how the proton distribution function evolves when the protons undergo stochastic heating by strong, low-frequency, Alfven-wave turbulence under the assumption that $beta$ is small. We apply our analysis to protons undergoing stochastic heating in the supersonic fast solar wind and obtain proton distributions at heliocentric distances ranging from 4 to 30 solar radii. We find that the proton distribution develops non-Gaussian structure with a flat core and steep tail. For $r >5 R_{rm S}$, the proton distribution is well approximated by a modified Moyal distribution. Comparisons with future measurements from emph{Solar Probe Plus} could be used to test whether stochastic heating is occurring in the solar-wind acceleration region.
285 - F. Fraternale 2015
Fluctuations in the flow velocity and magnetic fields are ubiquitous in the Solar System. These fluctuations are turbulent, in the sense that they are disordered and span a broad range of scales in both space and time. The study of solar wind turbulence is motivated by a number of factors all keys to the understanding of the Solar Wind origin and thermodynamics. The solar wind spectral properties are far from uniformity and evolve with the increasing distance from the sun. Most of the available spectra of solar wind turbulence were computed at 1 astronomical unit, while accurate spectra on wide frequency ranges at larger distances are still few. In this paper we consider solar wind spectra derived from the data recorded by the Voyager 2 mission during 1979 at about 5 AU from the sun. Voyager 2 data are an incomplete time series with a voids/signal ratio that typically increases as the spacecraft moves away from the sun (45% missing data in 1979), making the analysis challenging. In order to estimate the uncertainty of the spectral slopes, different methods are tested on synthetic turbulence signals with the same gap distribution as V2 data. Spectra of all variables show a power law scaling with exponents between -2.1 and -1.1, depending on frequency subranges. Probability density functions (PDFs) and correlations indicate that the flow has a significant intermittency.
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