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 (d). In this work, we analyze triangularity dependence of the cross-field particle transport at the outer midplane of plasmas with d = +0.38, +0.15, -0.14, and -0.26 on the Tokamak a` Configuration Variable, using our novel machine learning (ML) blob-tracking approach applied to gas puff imaging 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 d 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 d is accompanied by a decrease in the number of blobs in a fixed time, which tend to have larger area and lower radial speed. Also, the blobs in these plasmas are in the connected sheath regime, and show a velocity scaling consistent with the two-region model.