Estimating cross-field particle transport at the outer midplane of TCV by tracking filaments with machine learning

Cross-field transport of particles in the boundary region of magnetically confined fusion plasmas is dominated by turbulence. Blobs, intermittent turbulent structures with large amplitude and a filamentary shape appearing in the scrape-off layer (SOL), are known from theoretical and experimental studies to be the main contributor to the cross-field particle transport. The dynamics of blobs differs depending on various plasma conditions, including triangularity (δ). In this work, we analyze the cross-field particle transport in plasmas with δ = +0.38, +0.15, −0.14, and −0.26 on the Tokamak `a Configuration Variable (TCV) using a novel machine learning (ML) blob-tracking approach applied to Gas Puff Imaging (GPI) data. The cross-field particle flux determined in this way is of the same order as the overall transport inferred from KN1D, GBS, and SOLPS-ITER simulations, suggesting that the blobs identified by the ML blob-tracking account for most of the cross-field particle transport in the SOL. Also, the ML blob-tracking and KN1D show a decrease in the cross-field particle transport as δ becomes more negative. The blob-by-blob analysis of the result from the tracking reveals that the decrease of cross-field particle transport with decreasing δ is accompanied by a decrease in the number of blobs in a fixed time, which tend to have larger area and lower radial speed.