Effects of intermittency via non-Gaussianity on turbulent transport in magnetized plasmas

Abstract

We analyse how the turbulent transport of $\boldsymbol {E}\times \boldsymbol {B}$ type in magnetically confined plasmas is affected by the intermittent features of turbulence. The latter are modelled via the non-Gaussian distribution $P(\phi )$ of the turbulent electric potential $\phi$ . Our analysis is performed at an analytical level and confirmed numerically using two statistical approaches. We have found that the diffusion is inhibited linearly by intermittency, mainly via the kurtosis of the distribution $P(\phi )$ . The associated susceptibility for this linear process is shown to be dependent on the poloidal velocity $V_p$ and on the correlation time $\tau _c$ (or the Kubo number $K_\star$ , the ratio between $\tau _c$ and the specific time of flight $\tau _{{\rm fl}}$ ) with a maximum at $\tau _c\approx \tau _{{\rm fl}}$ ( $K_\star \approx 1$ ). Intermittency does not affect the scaling of diffusion with the Kubo number.

Scaling laws of two-dimensional incompressible turbulent transport

“…for the values of the potential f(x, t). The implications of non-Gaussianity have been discussed elsewhere [29] and found to be small.…”

Scaling laws of two-dimensional incompressible turbulent transport

“…a normal distribution for the values of the potential φ(x, t). The implications of non-Gaussianity have been discussed elsewhere [29] and found to be small. ν (0, t) for several values of the parameter ν and τ c = 1, λ c = 1.…”

Neural networks for turbulent transport prediction in a simplified model of tokamak plasmas

“…The turbulent electric potentials are constructed as an ensemble of dimension N p of stochastic, zero-averaged, homogeneous random fields {ϕ(x, t)}. The effects of intermittency on the distribution of the turbulent potential have already been studied in a previous work [19] and have been found to be minimal; thus, we can assume the Gaussianity of the fields. The ensemble of potentials {ϕ(x, t)} drives an associated ensemble of trajectories {x(t)} according to the EOMs (2a)-(2d); the transport coefficients are then computed as Lagrangian statistical averages over the resulting trajector-ies.…”