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Abstract Nonlinear frequency response analysis is a widely used method for determining system dynamics in the presence of nonlinearities. In dusty plasmas, the plasma–grain interaction (e.g. grain charging fluctuations) can be characterized by a single-particle non-linear response analysis, while grain–grain non-linear interactions can be determined by a multi-particle non-linear response analysis. Here a machine learning-based method to determine the equation of motion in the non-linear response analysis for dust particles in plasmas is presented. Searching the parameter space in a Bayesian manner allows an efficient optimization of the parameters needed to match simulated non-linear response curves to experimentally measured non-linear response curves.

The PK-4 system is a micro-gravity dusty plasma experiment currently in operation on-board the International Space Station. The experiment utilizes a long DC discharge in neon or argon gases. We apply our 2D particle-in-cell with Monte Carlo collisions discharge simulation to compute local plasma parameters that serve as input data for future dust dynamics models. The simulation includes electrons, Ne+ ions, and Nem metastable atoms in neon gas and their collisions at solid surfaces including secondary electron emission and glass wall charging. On the time scale of the on-board optical imaging, the positive column appears stable and homogeneous. On the other hand, our simulations show that on microsecond time scales the positive column is highly inhomogeneous: ionization waves with phase velocities in the range between 500 m s−1 and 1200 m s−1 dominate the structure. In these waves, the electric field and charged particle densities can reach amplitudes up to 10 times of their average value. Our experiments on ground-based PK-4 replica systems fully support the numerical findings. In the experiment, the direction of the DC current can be alternated, which has been found to favor dust particle chain formation. We discuss possible mechanisms for how the highly oscillatory plasmamore »environment contributes to the dust particle chain formation.« less

In this paper, we report the first experimental observation of internal resonance in a dusty plasma, which shows the intrinsic nonlinearities of dust interactions in plasmas. When driving a system of vertically aligned dust particle pairs in the vertical direction, the horizontal motion is found to be excited during onset of internal resonance when the higher-frequency horizontal sloshing mode is nonlinearly coupled to the vertical breathing mode through the 1:2 commensurable relation. A theoretical model of the nonlinear interaction of dust particles in plasma is also provided and the results of the theoretical model are shown to match experimental observations.

Dust kinetic temperature is a measure of the energy of the stochastic motion of a dust particle and is a result of the combination of the Brownian motion and the fluctuations in the dust charge and confining electric field. A method using the equilibrium value of the mean square displacement was recently introduced to obtain the dust kinetic temperature experimentally. As a follow up, this paper investigates the relationship between the dust kinetic energy derived from the mean square displacement technique and a technique using the probability distribution of the displacements obtained from random fluctuations of the dust particle. The experimental results indicate that the harmonic confinement potential acting on the dust particle can be obtained by combining the two methods, allowing the nonlinear effect of the confining force to be investigated. The thermal expansion in a 1-D vertical chain is discussed as a representative application as it is related to the nonlinear confinement force, or the asymmetric confinement potential.