Turbulence, nonlinear dynamics, and sources of intermittency and variability in the solar wind Intermittency & turbulence “Intermittency is the nonuniform distribution of eddy formations in a stream. The modulus or the square of the vortex field, the energy dissipation velocity or related quantities quadratic in the gradients of Velocity and Temperature (of the concentration of passive admixture) may serve as indicators. “ (E A Novikov, J Appl Math & Nech, 35, 266 (1971) Intermittency in simple form • • • • Duffing oscillator Lorenz attractor Rikitake dynamo many others Spatial fluctuations of dissipation are very large – gradients Are not uniformly distributed; the cascade produces intermittency Some types of intermittency and potential effects on solar prediction (1) Large scale/low frequency intermittency - variability of sources - Inverse cascade (space) 1/f noise (time) - Effects of dynamics on the “slow manifold” Dynamo reversals, rare events (big flares?) (2) Inertial range intermittency - “scaling” range - reflects loss of self similarity at smaller scales - KRSH This is a lot of what you see and measure (1) Dissipation rage intermittency - vortex or current sheets or other dissipation structures - usually breaks self similarity because there are characteristic physical scales Controls local reconnection rates and local dissipation/heating; small scale “events” Langmuir cells • • • • • Turbulence Waves Structure Gradients Mode coupling Intermittent turbulence in hydro • Dynamics at cloud tops: temperature gradients, driven by droplets (J. P. Mellado, Max Plank Meteor. • ocean surface-air Interface (J P Mellado) • Vorticity In interstellar Turbulence (Porter, Woodward, Pouquet) • PDFs intermittency corresponds to “extreme events,” especially at small scales fat tails • Higher order moments and nonGaussianity (esp. increments or gradients) For Gaussian, odd moments zero, Even moments < 2 > determined by <x2>; For intermittency, <x2n> > Gaussian value • Kurtosis and filling fraction F κ= < 4>/ <x2>2 HEURISTIC: κ ∼ 1/F energy containing inertial dissipation energy input cascade heating Energy spectrum E(k) “standard” turbulence spectrum Log(wavenumber) • Dissipation: conversion of (collective) fluid degrees of freedom into motions into kinetic degrees of freedom • Heating: increase in random kinetic energy • Entropy increase: irreversible heating How nonlinearity and cascade produces intermittency concentration of gradients • Amplification of higher order moments – Suppose that q & w are Gaussian, and ∼ then pdf( ) is exponential-like with κ � 6-9. • Amplification greater at smaller scale ( e. g., • Role of stagnation points (coherency!) ∼ – No flow or propagation to randomize the concentrations • Formation is IDEAL ,wavenumber ) (e.g., Frisch et al. 1983; Wan et al, PoP 2009) • Dissipation is more intense in presence of gradients relation between intermittency and dissipation Coherent structures are generated by ideal effects! dissipative ideal Contours of current density: Already have non-Gaussian coherent structures …before finite resolution errors set in TIME Same initial condition Turbulent fluctuations have structure and dissipation is not uniform ε : dissipation rate; ∆v: velocity increment Kolmogorov ’41 ∆v ∼ (εr) 1/3 <∆vp > = const. ε p/3 r p/3 Kolmogorov ’62 ∆v ∼ (εr r) 1/3 But this is NOT observed! εr = r-3 ∫ d3x’ ε(x’) Kolmogorov refined similarity hypothesis <∆vp > = const. <εr p/3 > r p/3 = const. ε p/3 r p/3 + ξ(p) (Oubukhov ’62) multifractal theory comes from this! Intermittency in hydrodynamics • Anselmet et al, JFM 1984 Need to be sure Pdf is resolved well enough to compute higher order moments! <∆urn> ∼ rζ(n) Pdfs of longitudinal velocity increments have fat tails; fatter for smaller scales ζ(n) Scaling of exponents at increasing order: reveals departures from self similarity and multifractal scalings (beta, log-normal, She-Levesque, etc n SW/MHD intermittency • More dynamical variables • Analogous effects Intermittency in MHD & Solar wind • Multifractal scalings • PDFs of increments (Politano et al, 1998; Muller and Biskamp 2000) (Burlaga, 1991; Tu & Masrch 1994, Horbury et al 1997) Muller & Biskamp, 2000 Sorriso et al, 1999 Cellularization, turbulent relaxation and structure in plasma/MHD: large scale evolution produces local relaxation suppression of nonlinearity nonGaussian statistics boundaries of relaxed regions correspond to small scale intermittent structures • Local relaxation can give rise to • Force free states • Alfvenic states • Beltrami states AND • characteristic small scale intermittent structures , e.g. current sheets v - Simulations show RAPID relaxation & production of local correlations. - Spatial “patches” of correlations bounded by discontinuities. θ b Characteristic distributions appear in less than one nonlinear time! Directional alignment: pdf Run with Hc f(cos(vb)) 0 v-b correlations: large (black >0; white < 0 ) (here, 2D MHD) • Analysis of patches of Alfvenic correlations • • • • Distributions of cos(θ ) [angle between velocity and magnetic field] Global statistics & statistics of linear subsamples (∼1-2 correlation scales) SW and 3D MHD SIM (512^3) Linear SIM samples 10 hr SW samples Global Alfvenicity σc ≈ 0.3 - For a specified sample size, can get highly variable Alfvenicity (see Roberts et al. 1987a,b) - Same effect in SW and in SIMs! PVI Coherent Structure Detection: designed to work the same way in analysis of solar wind and simulation data = • |∆ , | <|∆ , |>/ ∆ (x;s) = (x + s) – B(x) • • • Greco et al, GRL 2008; ApJ 2009 PVI links classical discontinuities and intermittency & compares well between SW and simulations Sim Greco et al, ApJ 2009; Servidio et al JGR, 2011 PVI > 3 events are statistically inconsistent with Gaussian statistics at the 90+ % level distribution of PVI SW 500 correlation scale PVI time series Waiting time distribution between “events” PVI vs. classical discontinuity methods PVI events in SW And in MHD turbulence simulations • Use PVI to find reconnection sites • In SIMs & in SW (caveats) From Servidio et al, JGR, 116, A09102 (2011) Trajectory thru SIM ”time series” of PVI Condition is PVI > threshold (1,2,3… ↓ Get a Table of efficiencies At PVI>7 - only ID ~40% of reconnection sites - But >95% of events are reconnection sites Same approach in SW, but compare t Gosling/Phan identified exhaust events: PVI>7 event in SW very likely to be at/near a reconnection event! Osman et al, PRL 112, 215002 (2014) Evidence that coherent structure are sites of enhanced heating: Solar wind proton temperature distribution conditioned on = |∆ , | <|∆ , |>/ Similar (weaker) effects in: electron temp & electron heat flux ALSO: neighborhoods of larger PVI events are hotter Wind s/c Osman et al, 2011 Osman et al, 2012 20 Implications for energetic particle transport Transport boundaries are observed: “dropouts” of Solar energetic particles core” ofH-FE SEP with ions vs arrival dropouts time For 9 Jan 1999 SEP event From Mazur et al, ApJ (2000) plasma intermittency • At kinetic scales • Still more variables, but analogous effects Localized kinetic effects in 2.5D Eulerian Vlasov simulation (undriven initial value problem; strongly turbulent ) • Magnetic field, current density, X points • Kinetic effects near a “PVI event” Greco et al, PRE, 86, 066405 (2012) Servidio et al, PRL 108, 045001 (2012) Out-of-plane current • Anisotropy Tmax/Tmin in small area • • • • a) nonMaxwellianity b) proton T anisotropy c) proton heat flux D) kurtosis of f(v) There is a strong association of kinetic effects with current structures! Dissipation is concentrated in sheet-like structures in kinetic plasma Wan, Matthaeus, Karimabadi, Roytershteyn, Shay, Wu , Daughton, Loring, Chapman, 2012 Strength of electric current density in shear-driven kinetic plasma (PIC) simulation (see Karimabadi et al, PoP 2013) Thinnest sheets seen are comparable to electron inertial length. Sheets are clustered At about the ion inertial length heirarchy of coherent, dissipative structures at kinetic scales Scale dependent kurtosis: MHD, kinetic sims, SW comparison Wu et al, ApJ Letters 763:L302012 (2013) Very low frequency/very large scale intermittency • 1/f noise: – – – – – • Gives “unstable” statistics – bursts and level-changes Long time tails on time correlations Generic mechanisms for its production (Montroll & Schlesinger, 1980) Often connected with inverse cascade, quasi-invariants, highly nonlocal interactions (opposite of Kolmogorov’s assumption!) Dynamo generates 1/f noise (experiments: Ponty et al, 2004 • connected to statistics of reversals (Dmitruk et al, 2014) – 1/k 1/f inferred from LOS photospheric magnetic field – 1/f signature in lower corona – 1/f signatures observed in density and magnetic field in solar wind at 1 AU (M+G, 1986; Ruzmaiken, 1988; Matthaeus et al, 2007; Bemporad et al, 2008) An example from 3D MHD with strong mean magnetic field (Dmitruk & WHM, 2007) - nearly in condensed state - energy shifts at times scales of 100s to 1000s Tnl - characteristic Tnl ~ 1 - Where do these timescales come from ? Numerical experiments on MHD Turbulence with mean field: onset of 1/f noise due to “quasi-invariant” Increasing B0 behavior of a Fourier mode In time, from simulation Eulerian frequency spectra Eulerian frequency spectrum: transform of one point two time Correlation fn. B0=8 Dmitruk & Matthaeus, 2009 1/f noise in SW (1AU ISEE-3, OMNI datasets) Matthaeus & Goldstein, PRL 1986 1/f: 1AU, MDI and UVCS – high/low latitude comparisons Ulysses MDI UCVS Matthaeus et al, ApJ 2007 Bemporad et al, ApJ 2008 1/f noise and reversals in spherical MHD dynamo Incompressible MHD spherical Galerkin model low order truncation Run for 1000s of Tnl See ramdon reversals of the dipole moment 1/f noise with rotation and or magnetic helicity Dmitruk et al, PRE in press 2014 With rotation/helicity Waiting times for reversals scale like geophysical data! possible 1/f In time domain cascade energy containing Slow & incoherent nonuniform dissipation intermittency corrections! Faster more Coherent more nonGaussian heating Energy spectrum E(k) More detailed cascade picture: central role of Log(wavenumber) • Cascade: progressively enhances nonGaussian character • Generation of and patchy correlations • Coherent structures are sites of • for inverse cascade/quasi-invariant case, 1/f noise low frequency irregularity in time, and build up of long wavelegnth fluctuations Toy model to generate intermittency - May be useful in transport studies as an improvement over random phase data - We already saw that structure is generated by ideal processes…so… Synthetic realizations with intermittency • Minimal Lagrangian Map (Rosales & Meneveau, 2006) • Add magnetic field; map using velocity (Subedi et al, 2014) Choose spectrum Iterate low pass filtering get filtered fields Push filtered vector fields v & b with filtered v-field at this level Re-map onto grid by averaging; proceed to next level After several (M=7) iterations Pdfs of longitudinal magnetic increments vs lag. Perpendicular current density in a plane Comparison of scale dependent kurtosis: SW, synthetic and MHD simulation summary Intermittency is a factor in solar prediction and space weather: • Large scale/low frequency intermittency (1/f noise) controls unsteady fluctuations in global parameters including extreme events • Inertial range intermittency generates structures that channel, trap and transpsort SEPs and change connectivity of field lines • Small scale (kinetic) intermittency implements heating and dissipation and controls reconnection rates Coherent magnetic structures emerge in many theoretical models Current and Magnetic field in 2D MHD simulation Parker problem: RMHD Rappazzo & Velli 2010 3D isotropic MHD current Mininni, NJP 2008 3D Hall MHD compressible, strong B0, current Dmitruk 2006 2.5D kinetic hybrid Parashar et al, 2010

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