Bayesian inference and calibration of magnetic diagnostics.

Area of Science:

• Fusion energy research
• Plasma physics
• Magnetic confinement

Background:

• Magnetic diagnostics are essential for monitoring plasma behavior in fusion devices.
• Real-world constraints lead to signal errors in flux loops and Mirnov probes.
• Accurate calibration is vital for reliable diagnostic data.

Purpose of the Study:

• To develop and validate a robust calibration method for magnetic diagnostics in a compact toroid fusion device.
• To address signal errors arising from construction, installation, and physical limitations.
• To enhance the accuracy and reliability of fusion plasma measurements.

Main Methods:

• Model-based calibration derived from magnetostatic theory and sensor circuitry.
• Bayesian inference applied to determine sensor parameters (rotation angles, amplifier gains).
• Least-squares optimization used for flux loop data calibration.

Main Results:

• Flux loop data predicted within 1% relative error using least-squares optimization.
• Bayesian inference successfully determined Mirnov probe rotation angles and amplifier gains.
• The calibration provides physical meaning to diagnostic measurements and identifies instrument malfunctions.

Conclusions:

• A Bayesian approach effectively calibrates magnetic diagnostics despite real-world imperfections.
• The developed calibration method enhances data accuracy and diagnostic reliability.
• This work contributes to improved monitoring and control of fusion plasmas.