TCV Tokamak Plasma Turbulence Data Analyzed with Machine Learning
by Han, W.; Golfinopoulos, T.; Terry, J.L.; Marmar, E.S. / Plasma Science and Fusion Center Dataverse·Updated 1mo ago
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Description
Cross-field particle transport data from the boundary region of magnetically confined fusion plasmas on the Tokamak à Configuration Variable (TCV). The dataset was created by Han, W.; Golfinopoulos, T.; Terry, J.L.; Marmar, E.S. and last updated on 2026-05-14. It likely contains results from a machine learning blob-tracking approach applied to Gas Puff Imaging data for plasmas with varying triangularity.
Use Cases
Modeling cross-field particle transport based on blob dynamics described in the study
Analyzing the relationship between plasma triangularity and transport rates based on the experimental conditions mentioned
Training machine learning models for blob detection and tracking in Gas Puff Imaging data
Validating simulation results from KN1D, GBS, and SOLPS-ITER against experimental observations
Investigating correlations between blob count, area, and radial speed under different plasma conditions
Strengths
Data is derived from experiments on the Tokamak à Configuration Variable (TCV), a recognized fusion research device
Analysis includes results for four distinct plasma triangularity values (+0.38, +0.15, −0.14, and −0.26)
Methodology employs a novel machine learning blob-tracking approach, suggesting a modern analytical technique
Limitations
Column-level documentation is absent; field semantics must be inferred after download
Row count is unknown, which may limit suitability assessment
Description metadata is limited; actual data quality requires manual inspection after download
Provenance
Source
Plasma Science and Fusion Center Dataverse
Collection Method
Machine learning blob-tracking applied to Gas Puff Imaging (GPI) data from TCV tokamak experiments
Freshness
Last updated 2026-05-14 20:21:37
License is unknown; terms of use must be verified before application.