The analysis of turbulence in plasmas is fundamental in fusion research. Despite extensive progress in theoretical modeling in the past 15 years, we still lack a complete and consistent understanding of turbulence in magnetic confinement devices, such as tokamaks. Experimental studies are challenging due to the diverse processes that drive the high-speed dynamics of turbulent phenomena. This work presents a novel application of motion tracking to identify and track turbulent filaments in fusion plasmas, called blobs, in a high-frequency video obtained from Gas Puff Imaging diagnostics. We compare four baseline methods (RAFT, Mask R-CNN, GMA, and Flow Walk) trained on synthetic data and then test on synthetic and real-world data obtained from plasmas in the Tokamak à Configuration Variable (TCV). The blob regime identified from an analysis of blob trajectories agrees with state-of-the-art conditional averaging methods for each of the baseline methods employed, giving confidence in the accuracy of these techniques. By making a dataset and benchmark publicly available, we aim to lower the entry barrier to tokamak plasma research, thereby greatly broadening the community of scientists and engineers who might apply their talents to this endeavor.
In this paper we present a novel pressure‐controlled Hele‐Shaw cell to investigate different physical processes in rough fractures using 3D‐printed rock analogs. Our system can measure high‐resolution fracture aperture and tracer concentration maps under relevant field stress conditions. Using a series of hydraulic and visual measurements, combined with numerical simulations, we investigate the evolving fracture geometry characteristics, pressure‐dependent hydraulic transmissivity, flow channeling, and the nature of mass transport as a function of normal stress. Our experimental results show that as the fracture closes and deforms under increasing normal loading: (a) the contact areas grow in number and size; (b) the flow paths become more focused and tortuous; and (c) the transport dynamics of conservative tracers evolve toward a higher dispersive regime. Moreover, under the applied experimental conditions, we observed excellent agreement between the simulated‐ and the experimentally measured‐hydraulic behavior.
The analysis of turbulent flows is a significant area in fusion plasma physics. Current theoretical models quantify the degree of turbulence based on the evolution of certain plasma density structures, called blobs. In this work we track the shape and the position of these blobs in high frequency video data obtained from Gas Puff Imaging (GPI) diagnostics, by training a mask R-CNN model on synthetic data and testing on both synthetic and real data. As a result, our model effectively tracks blob structures on both synthetic and real experimental GPI data, showing its prospect as a powerful tool to estimate blob statistics linked with edge turbulence of the tokamak plasma.
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