Bayesian Plasma Diagnostics in DEMO | Advanced Analysis #Sciencefather #Researcherawards



Introduction

Magnetic confinement nuclear fusion continues to emerge as a transformative solution to global energy challenges, with DEMO representing a crucial step toward practical fusion-powered electricity generation. As the successor to ITER, DEMO requires highly accurate plasma diagnostics despite stringent spatial limitations that restrict the installation of internal sensors. This context motivates the exploration of fully external magnetic measurement strategies combined with advanced probabilistic inference techniques. The study integrates Bayesian inference with Gaussian process modeling to interpret external coil data, delivering robust qualitative estimates of plasma current density profiles and key geometric parameters essential for reactor safety and performance.

Bayesian Inference for Magnetic Diagnostic Integration

The application of Bayesian inference provides a rigorous probabilistic framework for synthesizing heterogeneous magnetic diagnostic signals. By integrating data from pick-up coils, flux loops, and saddle coils, the method quantifies uncertainties and yields consistent posterior distributions for plasma properties. This probabilistic approach not only enhances diagnostic reliability but also compensates for reduced diagnostic access within DEMO. The Bayesian framework ensures transparent uncertainty propagation, which is vital for real-time control, scenario evaluation, and long-term reactor optimization.

Gaussian Process Modeling for Current Density Reconstruction

Gaussian process (GP) modeling serves as a powerful non-parametric tool to infer the spatial structure of the plasma current density using only external measurements. Its flexible regression capabilities allow for smooth functional reconstruction without imposing rigid priors on the current profile. This enhances DEMO’s capability to monitor plasma shape, stability, and heat-load distribution, particularly in challenging operational regimes. The combination of GPs with Bayesian inference further allows for estimation of plasma centroid, total current, and plasma–wall gap distances with high fidelity.

Diagnostic Performance Assessment of External Coil Technologies

The study evaluates the relative diagnostic effectiveness of normal pick-up coils versus saddle coils under DEMO’s geometric and operational constraints. Simulation-based comparisons demonstrate a marked superiority of saddle coils in providing richer magnetic information and improving inversion accuracy. This finding has direct implications for the engineering design and placement of magnetic diagnostics, ensuring both resilience and precision in the harsh DEMO environment where radiation load, limited space, and access restrictions are critical factors.

Bayesian Experimental Design for Coil Orientation Optimization

Bayesian experimental design offers a systematic pathway to maximize diagnostic utility by optimizing sensor configuration before physical deployment. The initial exploration in this study focuses on determining whether normal or tangential orientation of pick-up coils yields the highest information gain for plasma parameter inference. By quantifying expected reduction in uncertainty, this design framework supports decision-making in DEMO’s early engineering phases, ensuring that diagnostic placement enhances plasma control and safety margins.

Feasibility and Impact of Fully External Magnetic Diagnostics in DEMO

The results of this research collectively demonstrate that an entirely external magnetic diagnostic system, coupled with Bayesian integrated data analysis, can achieve the stringent accuracy thresholds demanded by DEMO. The inferred parameters—plasma current, centroid location, and multiple plasma–wall gaps—exhibit sufficient precision to support real-time control strategies. This work highlights the broader impact of probabilistic modeling in fusion research, emphasizing its potential to reduce engineering complexity while preserving diagnostic integrity for next-generation fusion reactors.


 Global Particle Physics Excellence Awards


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