UCLA

I model, simulate, and analyze the turbulence in a particular experiment on the Large Plasma Device (LAPD) at UCLA. The experiment, conducted by Schaffner et al. [D. Schaffner et al., Phys. Rev. Lett. 109, 135002 (2012)], nulls out the intrinsic mean flow in LAPD by limiter biasing. The model that I use in the simulation is an electrostatic reduced Braginskii two-fluid model that describes the time evolution of density, electron temperature, electrostatic potential, and parallel electron velocity fluctuations in the edge region of LAPD. The spatial domain is annular, encompassing the radial coordinates over which a significant equilibrium density gradient exists. My model breaks the independent variables in the equations into time-independent equilibrium parts and time-dependent fluctuating parts, and I use experimentally obtained values as input for the equilibrium parts.

After an initial exponential growth period due to a linear drift wave instability, the fluctuations saturate and the frequency and azimuthal wavenumber spectra become broadband with no visible coherent peaks, at which point the fluctuations become turbulent. The turbulence develops intermittent pressure and flow filamentary structures that grow and dissipate, but look much different than the unstable linear drift waves, primarily in the extremely long axial wavelengths that the filaments possess. An energy dynamics analysis that I derive reveals the mechanism that drives these structures. The long k_|| ∼ 0 intermittent potential filaments convect equilibrium density across the equilibrium density gradient, setting up local density filaments. These density filaments, also with k_|| ∼ 0, produce azimuthal density gradients, which drive radially propagating secondary drift waves. These finite k_|| drift waves nonlinearly couple to one another and reinforce the original convective filament, allowing the process to bootstrap itself. The growth of these structures is by nonlinear instability because they require a finite amplitude to start, and they require nonlinear terms in the equations to sustain their growth.

The reason why k_|| ∼ 0 structures can grow and support themselves in a dynamical system with no k_|| = 0 linear instability is because the linear eigenmodes of the system are nonorthogonal. Nonorthogonal eigenmodes that individually decay under linear dynamics can transiently inject energy into the system, allowing

for instability. The instability, however, can only occur when the fluctuations have a finite starting amplitude, and nonlinearities are available to mix energy among eigenmodes.

Finally, I attempt to figure out how many effective degrees of freedom control the turbulence to determine whether it is stochastic or deterministic. Using two different methods - permutation entropy analysis by means of time delay trajectory reconstruction and Proper Orthogonal Decomposition - I determine that more than a few degrees of freedom, possibly even dozens or hundreds, are all active. The turbulence, while not stochastic, is not a manifestation of low-dimensional chaos - it is high-dimensional.