Energy partitioning constraints at kinetic scales in low-<i>β</i> turbulence

Gershman, D. J.; Viñas, A. F.; Dorelli, J.; Goldstein, M. L.; Shuster, J. R.; Avanov, L. A.; Boardsen, S. A.; Stawarz, J. E.; Schwartz, Steven J.; Schiff, C.; Lavraud, B.; Saito, Y.; Paterson, W. R.; Giles, B. L.; Pollock, C. J.; Strangeway, R. J.; Russell, C. T.; Torbert, R. B.; Moore, T. E.; Burch, J. L.

doi:10.1063/1.5009158

Cited by 31 publications

(48 citation statements)

References 84 publications

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“…This kind of comparison is a significant advantage of the current approach in which we employ both single and multi-spacecraft techniques. Similar energy partitioning was found for low-beta plasma in the magnetosheath by Gershman et al (2018). Since plasma β for the present interval is lower than one (according to the Wind measurements β p = 0.43) our results are qualitatively consistent with Gershman et al (2018).…”

Section: Turbulence Resultssupporting

confidence: 90%

Solar Wind Turbulence Studies Using MMS Fast Plasma Investigation Data

Bandyopadhyay

Chasapis

Chhiber

et al. 2018

ApJ

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Studies of solar wind turbulence traditionally employ high-resolution magnetic field data, but highresolution measurements of ion and electron moments have been possible only recently. We report the first turbulence studies of ion and electron velocity moments accumulated in pristine solar wind by the Fast Particle Investigation instrument onboard the Magnetospheric Multiscale (MMS) Mission. Use of these data is made possible by a novel implementation of a frequency domain Hampel filter, described herein. After presenting procedures for processing of the data, we discuss statistical properties of solar wind turbulence extending into the kinetic range. Magnetic field fluctuations dominate electron and ion velocity fluctuation spectra throughout the energy-containing and inertial ranges. However, a multi-spacecraft analysis indicates that at scales shorter than the ion-inertial length, electron velocity fluctuations become larger than ion velocity and magnetic field fluctuations. The kurtosis of ion velocity peaks around few ion-inertial lengths and returns to near gaussian value at sub-ion scales.

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Section: Turbulence Resultssupporting

confidence: 90%

Solar Wind Turbulence Studies Using MMS Fast Plasma Investigation Data

Bandyopadhyay

Chasapis

Chhiber

et al. 2018

ApJ

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“…To summarize, in the present letter, we have estimated the Alfvén ratio and the normalized ratio of density to magnetic fluctuations in the turbulent magnetosheath by using high‐resolution data from the MMS mission. The observed ratios decrease with increasing wavenumber, which is consistent with the predicted values from two‐fluid theory (Zhao et al, ), as well as linear Vlasov theory (Gary, ) for KAW‐like fluctuations, as was recently described by Chen and Boldyrev () and Gershman et al (). This complements the results from linear Vlasov theory that KSWs in a plasma where β = 1 and T e < T i are strongly damped (e.g., Gary, ).…”

Section: Discussion/summarysupporting

confidence: 90%

“…In the above equations, we use the mean value of obtained from measurements and the wavenumber from Taylor's hypothesis, and we also assume that there is a strong anisotropy in wavevectors (k ⟂ ≫ k ∥ ), which is measured to be the case in the magnetosheath (e.g., Chen & Boldyrev, 2017;Gershman et al, 2018;Narita & Glassmeier, 2005).…”

Section: Resultsmentioning

confidence: 99%

Ion‐Scale Kinetic Alfvén Turbulence: MMS Measurements of the Alfvén Ratio in the Magnetosheath

Roberts

Toledo‐Redondo

Perrone

et al. 2018

Geophysical Research Letters

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Turbulence in the Earth's magnetosheath at ion kinetic scales is investigated with the magnetospheric multiscale spacecraft. Several possibilities in the wave paradigm have been invoked to explain plasma turbulence at ion kinetic scales such as kinetic Alfvén, slow, or magnetosonic waves. To differentiate between these different plasma waves is a challenging task, especially since some waves, in particular, kinetic slow waves and kinetic Alfvén waves, share some properties making the possibility to distinguishing between them very difficult. Using the excellent time resolution data set provided from both the fluxgate magnetometer and the Fast Plasma Instrument, the ratio of trace velocity fluctuations to the magnetic fluctuations (in Alfvén units), which is termed the Alfvén ratio, can be calculated down to ion kinetic scales. Comparison of the measured Alfvén ratio is performed with respect to the expectation from two‐fluid magnetohydrodynamic theory for the kinetic slow wave and kinetic Alfvén wave. Moreover, the plasma data also allow normalized fluctuation amplitudes of density and magnetic field to be compared differentiating between magnetosonic‐like and kinetic Alfvén‐like turbulence. Using these two different ratios, we can rule out that the fluctuations at ion scales are dominated by magnetosonic‐like fluctuations or kinetic slow‐like fluctuations and show that they are consistent with kinetic Alfvén‐like fluctuations. This suggests that in the wave paradigm, heating in the direction of the parallel magnetic field is predominantly by the Landau damping of the kinetic Alfvén wave.

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“…Much remains to be learned, but it seems rather certain that Big Data and Data Science will play a role in MHD turbulence in perhaps much the same way as it has demonstrated its utility in other fields. These issues could be investigated soon with the existence, as mentioned earlier, of new observational small‐scale data with the Mulstiscale Magnetospheric mission (Chasapis et al, ; Ergun et al, ; Gershman et al, ; Wilder et al, ), with experimental data (e.g., coming from the Coriolis table; Aubourg et al, ), and with numerical data stemming from high‐performance computing studies of FDT (Ishihara et al, ), of rotating and/or stratified turbulence (de Bruyn Kops, ; Rosenberg et al, ), as well as of turbulent interfaces in RST (Watanabe et al, ), and of MHD turbulence (Beresniak, ; Lee et al, ; Zhai & Yeung, ).…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, high values of the control parameter, namely, the Reynolds number which measures the relative strength of nonlinearities to (linear) dissipation, are achieved nowadays using liquid helium (Collaboration SHREK: B. Saint-Michel et al, 2014). At the same time, a multitude of observations is increasing our understanding of such complex fluids, with various space missions (Marino et al, 2008(Marino et al, , 2011Tsurutani et al, 2016), now including using data stemming from Magnetospheric MultiScale (see, e.g., Burch et al, 2016), looking at turbulence in the magnetotail (Ergun et al, 2018) or in the magnetosheath (Gershman et al, 2018), as well as in the solar wind in general (Chasapis et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Helicity Dynamics, Inverse, and Bidirectional Cascades in Fluid and Magnetohydrodynamic Turbulence: A Brief Review

Pouquet

Rosenberg

Stawarz

et al. 2019

Earth and Space Science

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We briefly review helicity dynamics, inverse and bidirectional cascades in fluid and magnetohydrodynamic (MHD) turbulence, with an emphasis on the latter. The energy of a turbulent system, an invariant in the nondissipative case, is transferred to small scales through nonlinear mode coupling. Fifty years ago, it was realized that, for a two‐dimensional fluid, energy cascades instead to larger scales and so does magnetic excitation in MHD. However, evidence obtained recently indicates that, in fact, for a range of governing parameters, there are systems for which their ideal invariants can be transferred, with constant fluxes, to both the large scales and the small scales, as for MHD or rotating stratified flows, in the latter case including quasi‐geostrophic forcing. Such bidirectional, split, cascades directly affect the rate at which mixing and dissipation occur in these flows in which nonlinear eddies interact with fast waves with anisotropic dispersion laws, due, for example, to imposed rotation, stratification, or uniform magnetic fields. The directions of cascades can be obtained in some cases through the use of phenomenological arguments, one of which we derive here following classical lines in the case of the inverse magnetic helicity cascade in electron MHD. With more highly resolved data sets stemming from large laboratory experiments, high‐performance computing, and in situ satellite observations, machine learning tools are bringing novel perspectives to turbulence research. Such algorithms help devise new explicit subgrid‐scale parameterizations, which in turn may lead to enhanced physical insight, including in the future in the case of these new bidirectional cascades.

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Energy partitioning constraints at kinetic scales in low-β turbulence

Cited by 31 publications

References 84 publications

Solar Wind Turbulence Studies Using MMS Fast Plasma Investigation Data

Solar Wind Turbulence Studies Using MMS Fast Plasma Investigation Data

Ion‐Scale Kinetic Alfvén Turbulence: MMS Measurements of the Alfvén Ratio in the Magnetosheath

Helicity Dynamics, Inverse, and Bidirectional Cascades in Fluid and Magnetohydrodynamic Turbulence: A Brief Review

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