Bayesian Integrated Data Analysis and Experimental Design for External Magnetic Plasma Diagnostics in DEMO

Magnetic confinement nuclear fusion offers a promising solution to the world’s growing energy demands. The DEMO reactor presented here aims to bridge the gap between laboratory fusion experiments and practical electricity generation, posing unique challenges for magnetic plasma diagnostics due to limited space for diagnostic equipment. This study employs Bayesian inference and Gaussian process modeling to integrate data from pick-up coils, flux loops, and saddle coils, enabling a qualitative estimation of the plasma current density distribution relying on only external magnetic measurements. The methodology successfully infers total plasma current, plasma centroid position, and six plasma–wall gap positions, while adhering to DEMO’s stringent accuracy standards. Additionally, the interchangeability between normal pick-up coils and saddle coils was assessed, revealing a clear preference for saddle coils. Initial steps were taken to utilize Bayesian experimental design for optimizing the orientation (normal or tangential) of pick-up coils within DEMO’s design constraints to improve the diagnostic setup’s inference precision. Our approach indicates the feasibility of Bayesian integrated data analysis in achieving precise and accurate probability distributions of plasma parameter crucial for the successful operation of DEMO.