Lane dynamics in pair-ion plasmas: effect of obstacle and geometric aspect ratio

Abstract

Lane formation dynamics of driven two-dimensional pair-ion plasmas is investigated in under-damped cases where the effect of particle inertia cannot be neglected. Extensive Langevin dynamics simulations using an OpenMP parallel program are carried out to analyse the effect of obstacle and geometric aspect ratio on lane formation dynamics previously reported in Sarma et al. (Phys. Plasmas, vol. 27, 2020, 012106) and Baruah et al. (J. Plasma Phys., vol. 87, 2021, 905870202). Lanes are found to form when like particles move along or opposite to the applied field direction. Lane order parameter, cumulative order parameter and distribution of the order parameter have been implemented to detect phase transition. The effect of geometric aspect ratio on the stability of lanes is systematically determined in both the presence and absence of an obstacle. Here, a specular reflective boundary condition is implemented to mimic an obstacle. We demonstrate that an obstacle promotes the merging of lanes, and the system gradually transitions to a partially mixed phase with higher value of aspect ratio. The occurrence of lane mixing phenomena at the separation boundary of two oppositely flowing lanes at higher value of aspect ratio is observed. In the presence of an oscillatory electric field, the lane merging tendency is reduced to a large extent as compared to the system where a constant electric field is applied. Furthermore, in the presence of both space- and time-varying electric fields, an appearance of a void is observed on either side of the obstacle. The study finds that the presence of an external magnetic field promotes acceleration of the phase transition process towards the lane mixing phase; it also reveals the existence of electric field drift in the system. Our findings may prove to be useful in understanding the nature of lane dynamics in naturally occurring pair-ion plasma systems as well as their relevance to technological applications that exploit or mitigate self-organization.