Moffatt drift driven large scale dynamo due to $\alpha$ fluctuations with nonzero correlation times
Nishant K. Singh (Nordita, Stockholm)

TL;DR
This paper develops a theory for large-scale magnetic field generation in turbulent flows with stochastic alpha fluctuations, highlighting how Moffatt drift and finite memory effects influence dynamo growth and cutoff scales.
Contribution
It extends the Kraichnan-Moffatt model to include finite correlation times of alpha fluctuations, revealing new conditions for dynamo action driven by Moffatt drift.
Findings
Finite Moffatt drift can enable dynamo action with weak alpha fluctuations.
A cutoff wavenumber exists beyond which dynamo growth ceases.
Finite memory effects modify the growth rate and scale of the dynamo.
Abstract
We present a theory of large-scale dynamo action in a turbulent flow that has stochastic, zero-mean fluctuations of the parameter. Particularly interesting is the possibility of the growth of the mean magnetic field due to Moffatt drift, which is expected to be finite in a statistically anisotropic turbulence. We extend the Kraichnan-Moffatt model to explore effects of finite memory of fluctuations, in a spirit similar to that of Sridhar & Singh (2014), hereafter SS14. Using the first-order smoothing approximation, we derive a linear integro-differential equation governing the dynamics of the large-scale magnetic field, which is non-perturbative in the -correlation time . We recover earlier results in the exactly solvable white-noise (WN) limit where the Moffatt drift does not contribute to the dynamo growth/decay. To study finite memory effects,…
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