Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation

• URL: http://arxiv.org/abs/2310.01455v1
• Date: Mon, 2 Oct 2023 10:47:00 GMT
• Title: Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation
• Authors: Timothy Nunn, Vignesh Gopakumar, Sebastien Kahn
• Abstract summary: Nuclear fusion using magnetic confinement holds promise as a viable method for sustainable energy. Most fusion devices have been experimental and as we move towards energy reactors, we are entering into a new paradigm of engineering. We demonstrate a proof-of-concept of an AI-driven strategy to help explore the design search space and identify optimum parameters.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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