Experimental Plasma Discharge Dataset from the TCABR Tokamak

Description

This dataset comprises 2,189 experimental plasma discharges from the TCABR (Tokamak à Chauffage Alfvén Brésilien) tokamak, operated by the Institute of Physics of the University of São Paulo (IFUSP). The database includes 1,754 non-disruptive and 435 disruptive discharges. All discharges are labeled for machine learning applications.

The data features high-resolution (1 μs) magnetic diagnostics, including:

• Plasma current (Iₚ) – measured by Rogowski coils
• Loop voltage (Vₗₒₒₚ) – measured by toroidal loops
• Magnetic fluctuations – measured by an array of Mirnov coils distributed poloidally

TCABR operates in a pure Ohmic L-mode regime with a circular cross-section, providing a highly reproducible environment (∼ 5% variation) free from confounding variables associated with auxiliary heating. This characteristic makes the dataset particularly valuable for studying MHD instabilities and disruption precursors, as well as for developing and validating machine learning-based disruption prediction algorithms. 

Classification Methodology

Discharges were classified as disruptive based on four operational criteria:

1. Current quench rate: dIₚ/dt < −50 kA/ms for more than 2 ms
2. Sudden current drop: ΔIₚ/Iₚ,ₙₒₘ > 30% within a 5 ms window
3. Loop voltage spike: Vₗₒₒₚ > 5 V
4. Magnetic fluctuation burst: amplitude > 5× background level

Technical Validation

The data underwent rigorous validation, including:

• Calibration traceable to INMETRO (Brazilian National Institute of Metrology, Quality and Technology)
• Cross-diagnostic consistency checks
• Monte Carlo sensitivity analysis with ±20% uniform perturbation of classification thresholds, demonstrating label robustness for 98.6% of discharge

Data Format

Data are available in NetCDF format (tcabr_data.nc), organized in a hierarchical structure by shot ID. We recommend using the xarray Python library for data manipulation and analysis. Each shot contains multiple diagnostics, which are listed in the table below:

A Python script file (tcabr_tools.py) is also provided, containing the custom code used for the disruption labeling algorithm, Monte Carlo sensitivity analysis, and data analysis.

Potential Applications

• Training and validation of machine learning models for disruption prediction
• Studies of MHD instabilities (e.g., m/n = 2/1 modes)
• Transport and confinement analysis in Ohmic plasma
• Benchmarking for numerical simulation codes
• Development of real-time disruption mitigation systems

Changelog

v1.0.0 — 2026-08-09 — Initial Release