Plasma Physics (physics.plasm-ph)

Abstract: The transition from laminar to turbulent flow is an immensely important topic that is still being studied. Here we show that complex plasmas, i.e., microparticles immersed in a low temperature plasma, make it possible to study the particle-resolved onset of turbulence under the influence of damping, a feat not possible with conventional systems. We performed three-dimensional (3D) molecular dynamics (MD) simulations of complex plasmas flowing past an obstacle and observed 3D turbulence in the wake and fore-wake region of this obstacle. We found that we could reliably trigger the onset of turbulence by changing key parameters such as the flow speed and particle charge, which can be controlled in experiments, and show that the transition to turbulence follows the conventional pathway involving the intermittent emergence of turbulent puffs. The power spectra for fully developed turbulence in our simulations followed the -5/3 power law of Kolmogorovian turbulence in both time and space. We demonstrate that turbulence in simulations with damping occurs after the formation of shock fronts, such as bow shocks and Mach cones. By reducing the strength of damping in the simulations, we could trigger a transition to turbulence in an undamped system. This work opens the pathway to detailed experimental and simulation studies of the onset of turbulence on the level of the carriers of the turbulent interactions, i.e., the microparticles.

Abstract: Current understanding of the kinetic-scale turbulence in weakly-collisional plasmas still remains elusive. We employ a general framework in which the turbulent energy transfer is envisioned as a scale-to-scale Langevin process. Fluctuations in the sub-ion range show a global scale invariance, thus suggesting a homogeneous energy repartition. In this Letter, we interpret such a feature by linking the drift term of the Langevin equation to scaling properties of fluctuations. Theoretical expectations are verified on solar wind observations and numerical simulations thus giving relevance to the proposed framework for understanding kinetic-scale turbulence in space plasmas.

Abstract: This work suggests several methods of uncertainty treatment in multiscale modelling and describes their application to a system of coupled turbulent transport simulations of a tokamak plasma. We propose a method to quantify the usually aleatoric uncertainty of a system in a quasi-stationary state, estimating the mean values and their errors for quantities of interest, which is average heat fluxes in the case of turbulence simulations. The method defines the stationarity of the system and suggests a way to balance the computational cost of simulation and the accuracy of estimation. This allows, contrary to many approaches, to incorporate aleatoric uncertainties in the analysis of the model and to have a quantifiable decision for simulation runtime. Furthermore, the paper describes methods for quantifying the epistemic uncertainty of a model and the results of such a procedure for turbulence simulations, identifying the model's sensitivity to particular input parameters and sensitivity to uncertainties in total. Finally, we introduce a surrogate model approach based on Gaussian Process Regression and present a preliminary result of training and analysing the performance of such a model based on turbulence simulation data. Such an approach shows a potential to significantly decrease the computational cost of the uncertainty propagation for the given model, making it feasible on current HPC systems.