Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation

TL;DR

This paper presents an AI-driven Bayesian Optimization approach to efficiently explore and optimize the design of magnetic field coils in fusion reactors, aiming to balance cost and plasma stability.

Contribution

It introduces a multi-output Bayesian Optimization method for designing tokamak coil shapes, enabling efficient Pareto front identification in high-dimensional spaces.

Findings

Successfully identified Pareto-optimal coil designs.

Reduced costs while maintaining plasma stability.

Demonstrated effectiveness of AI-driven optimization in fusion design.

Abstract

Nuclear fusion using magnetic confinement holds promise as a viable method for sustainable energy. However, most fusion devices have been experimental and as we move towards energy reactors, we are entering into a new paradigm of engineering. Curating a design for a fusion reactor is a high-dimensional multi-output optimisation process. Through this work we demonstrate a proof-of-concept of an AI-driven strategy to help explore the design search space and identify optimum parameters. By utilising a Multi-Output Bayesian Optimisation scheme, our strategy is capable of identifying the Pareto front associated with the optimisation of the toroidal field coil shape of a tokamak. The optimisation helps to identify design parameters that would minimise the costs incurred while maximising the plasma stability by way of minimising magnetic ripples.