Three-dimensional finite-time Lyapunov-exponent analysis of fluid dusty plasmas under weightlessness using a machine-learning particle reconstruction technique.

Abstract

We have performed experiments on dusty plasmas under the weightlessness conditions of parabolic flights where the dust particles form an extended homogeneous dust cloud. The three-dimensional (3D) dynamic state of the dust cloud is characterized. Therefore, the particle trajectories have been recorded using a four-camera stereoscopic camera system. From that, the 3D particle trajectories have been determined using both a machine-learning particle reconstruction technique and the deterministic shake-the-box algorithm. From the trajectories, characteristic fluid parameters, such as flow fields and finite-time Lyapunov exponent (FTLE)-based fluid structures have been calculated and analyzed. The FTLE analysis indicates that the fluid is characterized by an incompressible flow with small-scale behavior. Furthermore, it is demonstrated that the machine-learning based approach allows to reliably characterize the dynamic states by comparison with the shake-the-box algorithm.