1 / 18

cattaneo@flash.uchicago

Mean field dynamos: analytical and numerical results. Fausto Cattaneo Center for Magnetic-Self Organization and Department of Mathematics University of Chicago. cattaneo@flash.uchicago.edu. 1919 Dynamo action introduced (Larmor) 30’s – 50’s Anti-dynamo theorems (Cowling; Zel’dovich)

nicholas-lamb
Télécharger la présentation

cattaneo@flash.uchicago

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Mean field dynamos: analytical and numerical results Fausto Cattaneo Center for Magnetic-Self Organization and Department of Mathematics University of Chicago cattaneo@flash.uchicago.edu CMSO 2005

  2. 1919 Dynamo action introduced (Larmor) 30’s – 50’s Anti-dynamo theorems (Cowling; Zel’dovich) 50’s - 60’s Averaging is introduced  Formulation of Mean Field Electrodynamics (Parker 1955; Braginskii 1964; Steenbeck , Krause & Radler 1966) 90’s – now Large scale computing. MHD equations can be solved directly Check validity of assumptions Explore other types of dynamos Historical considerations Universal mechanism for generation of large-scale fields from turbulence lacking reflectional symmetry CMSO 2005

  3. Evolution equations for the Mean field (homogeneous, isotropic case) Transport coefficients determined by velocity and Rm  mean induction—requires lack of reflectional symmetry (helicity) β turbulent diffusivity Many assumption needed Linear relation between and Separations of scales FOS (quasi-linear) approximation Short correlation time Assumed that fluctuations not self-excited Mean Field Theory CMSO 2005

  4. In order for MFT to work: fluctuations must be controlled by smoothing procedure (averages) system must be strongly irreversible When Rm << 1 irreversibility provided by diffusion When Rm >> 1 , problems arise: development of long memory  loss of irreversibility unbounded growth of fluctuations Troubles CMSO 2005

  5. Exactly solvable kinematic model (Boldyrev & FC) lots of assumptions can be treated analytically Nonlinear rotating convection (Hughes & FC) fewer assumptions solved numerically ______________________________________________________________ Shear-buoyancy driven dynamo (Brummell, Cline & FC) Intrinsically nonlinear dynamo Not described by MFT Examples CMSO 2005

  6. Model for random passive advection (Kazentsev 1968; Kraichnan 1968) Velocity: zero mean, homogeneous, isotropic, incompressible, Gaussian and delta-correlated in time Exact evolution equation for magnetic field correlator Solvable models Input: velocity correlator Ouput: magnetic correlator CMSO 2005

  7. Exact evolution equation Non-helical case (C= 0): Kazantsev 1968 Helical case: Vainshtein & Kichatinov 1986; Kim & Hughes 1997 Spectral version: Kulsrud & Anderson 1992; Berger& Rosner 1995 Symmetric form: Boldyrev, Cattaneo & Rosner 2005 Solvable models Operator in square brackets is self-adjoint CMSO 2005

  8. Dynamo growth rate from MFT: In this model MFT is exact; with Dynamo growth rate of mean field: Dynamo growth rate of fluctuations: Large scale asymptotics of corresponding eigenfunction Solvable models CMSO 2005

  9. Conclusion: For a fixed time t, large enough spatial scales exist such that averages of the fluctuations are negligible. on these scales the evolution of the average field is described by MFT However For any spatial scale x, contributions from mean field to the correlator at those scales quickly becomes subdominant Solvable models Fluctuations eventually take over on any scale CMSO 2005

  10. Energy densities  cold kinetic g magnetic x 4 hot B0 time Convectively driven dynamos with rotation Cattaneo & Hughes CMSO 2005

  11. Turbulent convection with near-unit Rossby number System has strong small-scale fluctuations No evidence for mean field generation Rotating convection Cattaneo & Hughes Non rotating Rotating Energy: hor. average Energy ratio Magnetic field Velocity CMSO 2005

  12. System has helicity, yet no mean field Consider two possibilities: Nonlinear saturation of turbulent -effect (Cattaneo & Vainshtein 1991; Kulsrud & Anderson 1992; Gruzinov & Diamond 1994) -effect is “collisional” and not turbulent What is going on? CMSO 2005

  13. Averages and -effect Introduce (external) uniform mean field. Compute below dynamo threshold. Calculate average emf. Extremely slow convergence. emf: volume average Cattaneo & Hughes emf: volume average and cumulative time average time CMSO 2005

  14. Convectively driven dynamos The α-effect here is inversely proportional to Pm (i.e. proportional to η). It is therefore not turbulent but collisional • Convergence requires huge sample size. • Divergence with decreasing η? • Small-scale dynamo is turbulent but -effects is not!! Press this if running out of time CMSO 2005

  15. Interaction between localized velocity shear and weak background poloidal field generates intense toroidal magnetic structures Magnetic buoyancy leads to complex spatio-temporal beahviour By + Something completely different Cline, Brummell & Cattaneo CMSO 2005

  16. Slight modification of shear profile leads to sustained dynamo action System exhibits cyclic behaviour, reversals, even episodes of reduced activity By - By + Shear driven dynamo CMSO 2005

  17. In turbulent dynamos behaviour dominated by fluctuations Averages not well defined for realistic sample sizes In realistic cases large-scale field generation possibly driven by non-universal mechanisms Shear Large scale motions Boundary effects Conclusion CMSO 2005

  18. The end CMSO 2005

More Related