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Magnetic Reversals in a Simple Model of MagnetohydrodynamicsRoberto Benzi1 and Jean-Francois Pinton21Dipartimento di Fisica and INFN, Universita Tor Vergata, Via della Ricerca Scientifica 1, I-00133 Roma, Italy2Laboratoire de Physique, CNRS and Ecole Normale Superieure de Lyon, 46 allee dItalie, F69007 Lyon, France(Received 1 May 2009; published 8 July 2010)We study a simple magnetohydrodynamical approach in which hydrodynamics and MHD turbulenceare coupled in a shell model, with given dynamo constraints in the large scales. We consider the case of alow Prandtl number fluid for which the inertial range of the velocity field is much wider than that of themagnetic field. Random reversals of the magnetic field are observed and it shown that the magnetic fieldhas a nontrivial evolutionlinked to the nature of the hydrodynamics turbulence.DOI: 10.1103/PhysRevLett.105.024501 PACS numbers: 47.27.Ak, 91.25.CwThe question of transitions between statistical solutionsis central to the behavior of many out-of-equilibrium sys-tems in physics and geophysics [14]. As one particularexample addressed here, we note that natural dynamos areintrinsically dynamical. Formally, the coupled set of mo-mentum and induction equations is invariant under thetransform u;B ! u;B so that states with oppositepolarities can be generated from the same velocity field (uand B are, respectively, the velocity and magnetic fields).In the case of the geodynamo, polarity switches are calledreversals [5] and occur at very irregular time intervals [6].Such reversals have been observed recently in laboratoryexperiments using liquid metals, in arrangements wherethe dynamo cycle is either favored artificially [7] or stemsentirely from the fluid motions [4,8]. In these laboratoryexperiments, as also presumably in Earths core, the ratioof the magnetic diffusivity to the viscosity of the fluid(magnetic Prandtl number PM) is quite small. As a result,the kinetic Reynolds number RV of the flow is very highbecause its magnetic Reynolds number RM RVPM needsto be large enough so that the stretching of magnetic fieldlines balances the Joule dissipation. Hence, the dynamoprocess develops over a turbulent background, and in thiscontext it is often considered as a problem of bifurcationin the presence of noise. For the dynamo instability, theeffect of noise enters both additively and multiplicatively, asituation for which a complete theory is not currentlyavailable. Some specific features have been ascribed toits onset (e.g., bifurcation via an on-off scenario [9]) andto its dynamics [10]. Turbulence also implies that pro-cesses occur over an extended range of scales; however,in a low magnetic Prandtl number fluid the hydrodynamicrange of scales is much wider than the magnetic one. Inlaboratory experiments, the induction processes that par-ticipate in the dynamo cycle involve the action of largescale velocity gradients [4,11,12], with possible contribu-tions of velocity fluctuations at small scales [1315].Building upon the above observations, we propose herea simple model which incorporates hydromagnetic fluctu-ations (as opposed to noise) in a dynamo instability. Ourgoal here is not to derive a low dimensional model from theinteraction of selected modes (see, for instance, [16] andreferences therein) but to assume the existence of suchlarge scale symmetry-breaking features and to investigatethe effects of turbulence fluctuations onto the dynamics ofreversals. The models stems from the approach introducedin [17] for the hydrodynamic studies.We consider an energy cascade model, i.e., a shellmodel aimed at reproducing a few of the relevant charac-teristic features of the statistical properties of the Navier-Stokes equations [18]. In a shell model, the basic variablesdescribing the velocity field at scale rn 2nr0 k1nis a complex number un satisfying a suitable set of non-linear equations (here r0 2). There are many versions ofshell models which have been introduced in literature.Here we choose the one referred to as Sabra shell model.Let us remark that the statistical properties of intermittentfluctuations, computed using either shell variables or theinstantaneous rate of energy dissipation, are in close quali-tative and quantitative agreement with those measured inlaboratory experiments, for homogeneous and isotropicturbulence [18]. The MHD shell modelintroduced in[19]allows a description of turbulence at low magneticPrandtl number since the steps of both cascades can befreely adjusted [20,21]. Although geometrical features arelost, this is a clear advantage over 3D simulations [22,23].We consider here a formulation extended from the Sabrahydrodynamic shell model:dundt i3nu; u nB; B k2nun fn; (1)dBndt i3nu; B nB; u mk2nBn; (2)where n 1; 2; . . . andnu;wkn11un2wn12un1wn2kn12un1wn11un1wn1kn12un1wn212un2wn1;(3)for which, following [17], we chose 0:4. For thisPRL 105, 024501 (2010) P HY S I CA L R EV I EW LE T T E R Sweek ending9 JULY 20100031-9007=10=105(2)=024501(4) 024501-1 2010 The American Physical Societyhttp://dx.doi.org/10.1103/PhysRevLett.105.024501value of , the Sabra model is known to show statisticalproperties (i.e., anomalous scaling) close to the ones ob-served in homogenous and isotropic turbulence. Themodel, without forcing and dissipation, conserves the ki-netic energy EV njunj2, the magnetic energy EB njBnj2, and the cross-helicity RenunBn. In the samelimit, the model has a U1 symmetry corresponding to aphase change expi in both complex variables un and Bn.The quantity nv;w is the shell model version of thetransport term ~ur ~w. The forcing term fn is given by fn S1nf0=u1; i.e., we force injection in the large scale with aconstant power. We want to introduce in Eq. (2) an extra(large scale) term aimed at producing two statisticallystationary equilibrium solutions for the magnetic field.For this purpose, we add to the right-hand side of (2) anextra term M2B2; namely, for n 2, Eq. (2) becomesdB2dt F2u; B M2B2 mk22B2; (4)where F2u; B is a shorthand notation for i=32u; B 2B; u. The term M2B2 is chosen with two require-ments: (1) it must break theU1 symmetry, and (2) it mustintroduce a large scale dissipation needed to equilibrate thelarge scale magnetic field production. There are manypossible ways to satisfy these two requirements. Here wesimply choose M2B2 amB32. We argue, see the discus-sion at the end of this Letter, that the two requirements area necessary condition to observe large scale equilibration.From a physical point of view, symmetry breaking alsooccurs in real dynamos since the magnetic field is directedin one preferential direction which changes sign during areversal. Thus symmetry breaking is a generic featurewhich we introduce in our model by prescribing somelarge scale geometrical constrain. On the other hand, largescale dissipation must be responsible for the equilibrationmechanism of the large scale field. The choice of a non-linear equilibration is made here to highlight the existenceof a nonlinear center manifold for the large scale dynamics[24]. In other words, Eq. (4) with M2B2 amB32 issupposed to describe the normal form dynamics of thelarge scale magnetic field. Note that our assumption onM2does not necessarily imply a time scale separation betweenthe characteristic time scale of B2 and the magnetic turbu-lent field. Finally, since the system has an inverse cascadeof helicity [19], we set B1 0 as a boundary condition atlarge scale in order to prevent nonstationary behavior.The free parameters of the model are the power input f0,the magnetic viscosity m, and the saturation parametersam. Our numerical simulations have been computed withn 1; 2; . . . ; 25. Actually, the parameter f0 could be elim-inated by a suitable rescaling of the velocity field. We shallkeep it fixed to f0 1 i. In Fig. 1 we show the ampli-tude of hjB2ji and the magnetic energy EB hnjBnj2i asa function of m for 107, where the symbol h istands for time average. In this system, a possible estimateof Reynolds numbers is RV ffiffiffiffiffiffiffiffiffiffihEVip =k2 ffiffiffiffiffiffiffiffiffiffihEVip r0=4and RM ffiffiffiffiffiffiffiffiffiffihEVip r0=4m; in the runs shown, hEVi 0:3,this yields RV 1:5 106 and RM 0:15=m.For very large m, the magnetic field does not grow.Then, for m greater than some critical value, hB2i, as wellas EB, increases for decreasing m. Eventually, hjB2jisaturates at a given value while EB still increases, showingthat for m small enough a fully developed spectrum of Bnis achieved. This type of behavior is in agreement withprevious studies of Taylor-Green flows [25,26], s2t2 flowsin a sphere [27], or MHD shell models [28]. In the top insetof Fig. 1 we show the magnetic and energy spectrum form 103. Finally, in the lower inset we plot the magneticdissipation B mnk2nhjBnj2i and the large scale dissi-pation due to Mn. Note that at the dynamo threshold weobserve a sudden bump in the magnetic dissipation whichdecreases for decreasing n. At relatively small m, themagnetic dissipation becomes constant and quite close tothe large scale dissipation.We can reasonably predict the behavior of hjB2j2i as afunction of m by the following argument. The onset ofdynamo implies that there exists a net flux of energy fromthe velocity field to the magnetic field. At the largest scale,the magnetic field B2 is forced by the velocity field due tothe terms F2u; B. The quantity A RF2u; BB2 is theenergy pumping due to the velocity field which is inde-pendent of B2 and am. Thus, from Eq. (4) we can obtain12djB2j2dt A amjB2j2B22r B22i mk22jB2j2; (5)whereB2r andB2i are the real and imaginary part of B2. Forlarge m, the amplitude of B2 is small and the symmetry-breaking term proportional to am is negligible. Under thiscondition, and with the boundary condition constraints, weexpect from (5) or (7) that the behavior of B2 is periodic, asit has been observed in the numerical simulations. On theother hand, for relatively small m, the nonlinear equili-012345 1 10 100 1000 10 000 100 000 1106 1/mEB 0.8 0.6 0.4 0.2 0 1000 10 B,m 1/m-20-10 20 15 10 5nFIG. 1 (color online). Main figure: The behavior of hEBi (redtriangles) and hjB2j2i (green circles) as a function of m for fixedvalue of 107. The dotted blue line is the solution of (5).Upper inset: Energy spectra for Bn (green triangles) and un (redcircles) for the case m 0:001. Lower inset: The amount ofmagnetic dissipation (solid red triangles) B hnmk2njBnj2iand the dissipation due to the large scale term m amhjB2j2B22r B22ii (open green triangles).PRL 105, 024501 (2010) P HY S I CA L R EV I EW LE T T E R Sweek ending9 JULY 2010024501-2bration breaks the U1 symmetry and B2i becomes rathersmall and statistically stationary solutions can be observedwith B22r ffiffiffiffiffiffiffiffiffiffiffiffiA=amp. Computing A from the numericalsimulations, we can use (5) to predict how hjB2j2i dependson m. The result is shown in Fig. 1 by the dotted blue linewith rather good agreement.We are interested in studying the behavior of the mag-netic reversal, if any, as a function of m and, in particular,in the region where jB2j saturates, i.e., it becomes inde-pendent of m. In Fig. 2, we show three different timeseries of the B2r ReB2 as a function of time for threedifferent, relatively large, values of the magnetic diffusiv-ity. The figure highlights the two major items discussed inthis Letter, namely, the observation of reversals betweenthe two possible large scale equilibria and the dramaticincrease of the time delay between reversals for increasingm values. Note that this long time scale, as observed in theupper panel of Fig. 2, is much longer than the characteristictime scale of B2 near one of the two equilibrium states. Thesystem spontaneously develops a significant time scaleseparation, for which given polarity is maintained for timesmuch longer than the magnetic diffusion time. In Fig. 3 weshow the average persistence time (i.e., time betweenreversals) as a function of m. More precisely, let us definetn as the times at which B2tn 0 and B2 has oppositesign before and after tn. Then the persistence time isdefined as n tn tn1, while the average persistencetime is defined as the average of n. In order to obtain asignificant value of , we performed rather long numericalsimulations (from 103 to 104 longer than the time seriesshown in Fig. 2).Figure 3 clearly shows that for large m, becomesextremely large (note that the figure is in log-log scale).Thus, even if neither hjB2j2i nor d depend on m, theeffect of magnetic diffusivity is crucial for determining theaverage persistence time. For each numerical simulationshown in Fig. 3, we computed the average persistence time and its error bar (see inset). In order to develop atheoretical framework aimed at understanding the resultshown in Fig. 3, we assume, in the region where hjB2j2i isindependent of m, that B2i 0 and that the term F2u; Bcan be divided into an average forcing term proportional toB2r and a fluctuating partF2u; B B2 0; (6)where depends on f0 and 0 is supposed to be uncorre-lated with the dynamics of B2, i.e., h0B2i 0. Note thatin the context of the mean-field approach to MHD, the firsttermB2 would correspond to an alpha effect. Using (6)we can rewrite the equations for B2 as follows:dB2dt B2 amB32 0; (7)where we neglect the dissipative term since mk22 inthe region of interest. Equation (7) must be considered aneffective equation describing the dynamics of the magneticfield B2 and its reversals, and the fluctuations 0 incorpo-rate the turbulent fluctuations from the velocity and mag-netic field turbulent cascades. It is the effect of 0 thatmakes the system jump between the two statisticallystationary states. Using (5) we can obtain ffiffiffiffiffiffiffiffiffiAamp whilethe two statistical stationary states can be estimated asB0, B20 =am. The effective equation (7) is a stochas-tically differential equation. By using large deviation the-ory [29] applied to stochastic differential equations, we canpredict to be exp2am expA; (8)where is the variance of the noise 0 acting on the sys-tem. Let us note that A and must have the same dimen-sion, namely, B2=time. Thus, we write as Af, 1.5 0-1.5 20 000 25 000 30 000 35 000 40 000B2rtimem = 0.00024 1.5 0-1.5 20 000 25 000 30000 35 000 40 000B2rm = 0.00026 1.5 0-1.5 20 000 25 000 30 000 35 000 40 000B2r m = 0.00028100 td td = 1/(k22m)FIG. 2 (color online). Time behavior of B2r for three differentvalues of m (displayed on the right-hand side) and constant .The short blue segment in the upper panel shows 100td, where tdis the dissipative time scale computed as td 1=k22m. Onetime unit in the figure corresponds to the large scale eddyturnover time 1=k1ju1j. Numerical simulations have run formuch longer than the time intervals shown herein the com-plete time series there is no asymmetry between the B2 states. 10 100 1000 10 000 100 000 110-6 110-5 0.0001 0.001m 0.4 0.3 0.2 0.1 0 0.0005 0.000251/log[]mFIG. 3 (color online). Average persistence time as a functionof the magnetic viscosity m for am 0:1 and 107. Thedotted green line corresponds to the fit given by Eq. (9). In theinset we plot 1= ln and its error bars (computed from thestandard deviation) versus m to highlight the linear behaviorpredicted by (9). Note that the error bars are smaller than thesymbol size except for the very last pointPRL 105, 024501 (2010) P HY S I CA L R EV I EW LE T T E R Sweek ending9 JULY 2010024501-3where f is a function of the relevant dimensionless varia-bles. In our problem the dimensionless numbers expectedto play a role for the dynamical behavior of the magneticfield are the Reynolds number RV , the magnetic Reynoldsnumber RM (or equivalently the magnetic Prandtl numberPM), and the quantity Rm ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiAam=2mk42q, which is aneffective Reynolds number, corresponding to the efficiencyof energy transfers from the velocity field to the magneticfield at large scale. Given the fact that we operate atconstant power input and RV const, we expect f to bea function of (Rm;RM) only and we also expect the effec-tive magnetic Reynolds number to be proportional to theintegral one (Rm / RM). We then show below that a verygood description of our numerical results is obtained usingthe lowest order approximation fRm; RM RM RM,where RM is a critical magnetic Reynolds number belowwhich reversals are not observed. This choice leads to Am m=uL and finally to expCm m; (9)where C is a constant independent of m. This functionalform is displayed in Fig. 3; it agrees remarkably with theobserved numerical values of for a rather large range. Inthe inset of Fig. 3 we show 1= log as a function of m tohighlight the linear behavior predicted by Eq. (9). Thephysical statement represented by (9) is that the averagepersistence time should show a critical slowing down forrelatively large m. In other words, we expect that fluctua-tions around the statistical equilibria increase as RM in-creases. The increase of fluctuations may not be monotonicfor very large RM, which explains why we are not able to fitthe entire range of m shown in Fig. 3.Finally, we comment on the choice of a nonlinear term inEq. (4). Actually, we can avoid nonlinear equilibration toobtain the same (qualitative) results. In Fig. 4 we show twocases obtained with M2B2 B2 with the constraintsB2i 0 and 0:13. The equilibration mechanism istherefore linear while the symmetry breaking is obtainedby the constraint B2i 0. Thus the two requirements, largescale dissipation and symmetry breaking, are satisfied.Figure 4 shows that statistical equilibria can be observedindependent of nonlinear mechanism. Moreover, by chang-ing the magnetic diffusivity, we can still observe a ratherlarge difference in the average persistence time. 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Note that although large scale equilibration is achievedby a linear damping on the magnetic field, B2r shows quite well-defined statistical equilibria due to the symmetry-breaking con-straint B2i 0.PRL 105, 024501 (2010) P HY S I CA L R EV I EW LE T T E R Sweek ending9 JULY 2010024501-4http://dx.doi.org/10.1126/science.278.5343.1598http://dx.doi.org/10.1073/pnas.0602641103http://dx.doi.org/10.1017/S0022112098003619http://dx.doi.org/10.1017/S0022112098003619http://dx.doi.org/10.1017/S0022112002003063http://dx.doi.org/10.1140/epjb/e2004-00261-3http://dx.doi.org/10.1140/epjb/e2004-00261-3http://dx.doi.org/10.1103/PhysRevLett.103.024503http://dx.doi.org/10.1103/PhysRevLett.103.024503http://dx.doi.org/10.1103/PhysRevLett.98.044502http://dx.doi.org/10.1088/1367-2630/8/12/329http://dx.doi.org/10.1209/0295-5075/77/59001http://dx.doi.org/10.1103/PhysRevE.63.066211http://dx.doi.org/10.1080/03091920108203410http://dx.doi.org/10.1103/PhysRevLett.86.3024http://dx.doi.org/10.1063/1.1331315http://dx.doi.org/10.1103/PhysRevE.73.046310http://dx.doi.org/10.1103/PhysRevLett.98.164503http://dx.doi.org/10.1134/S0021364008150058http://dx.doi.org/10.1088/0953-8984/20/49/494203http://dx.doi.org/10.1088/0953-8984/20/49/494203http://dx.doi.org/10.1103/PhysRevLett.95.024502http://dx.doi.org/10.1146/annurev.fluid.35.101101.161122http://dx.doi.org/10.1103/PhysRevE.57.4155http://dx.doi.org/10.1088/1367-2630/9/8/294http://dx.doi.org/10.1103/PhysRevLett.92.144503http://dx.doi.org/10.1103/PhysRevLett.92.144503http://dx.doi.org/10.1103/PhysRevE.78.026310http://dx.doi.org/10.1103/PhysRevLett.94.164502http://dx.doi.org/10.1103/PhysRevLett.96.204503http://dx.doi.org/10.1103/PhysRevE.75.026303http://dx.doi.org/10.1103/PhysRevE.74.066310http://dx.doi.org/10.1103/PhysRevE.74.066310http://dx.doi.org/10.1214/07-AOP348http://dx.doi.org/10.1137/1017070