Large-scale dynamo action due to $\alpha$ fluctuations in a linear shear flow
S. Sridhar (RRI, India), Nishant K. Singh (IUCAA, India, NORDITA,, Stockholm)

TL;DR
This paper develops a model for large-scale magnetic field generation in shear flows with stochastic alpha fluctuations, revealing how memory effects and shear influence dynamo action.
Contribution
It extends the Kraichnan-Moffatt model to include shear and finite alpha-correlation time, deriving explicit growth rate expressions and highlighting new dynamo mechanisms.
Findings
White-noise alpha fluctuations do not enable dynamo action with shear.
Finite alpha-correlation time introduces new scales and allows weak fluctuations to generate dynamo.
Both shear and Moffatt drift enhance the magnetic field growth rate.
Abstract
We present a model of large-scale dynamo action in a shear flow that has stochastic, zero-mean fluctuations of the parameter. This is based on a minimal extension of the Kraichnan-Moffatt model, to include a background linear shear and Galilean-invariant -statistics. Using the first order smoothing approximation we derive a linear integro-differential equation for the large-scale magnetic field, which is non perturbative in the shearing rate , and the -correlation time . The white-noise case, , is solved exactly, and it is concluded that the necessary condition for dynamo action is identical to the Kraichnan-Moffatt model without shear; this is because white-noise does not allow for memory effects, whereas shear needs time to act. To explore memory effects we reduce the integro-differential equation to a partial…
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