Bayesian optimisation of poloidal field coil positions in tokamaks

Abstract

The tokamak is a world-leading concept for producing sustainable energy via magnetically-confined nuclear fusion. Identifying where to position the magnets within a tokamak, specifically the poloidal field (PF) coils, is a design problem which requires balancing a number of competing economic, physical, and engineering objectives and constraints. In this paper, we show that multi-objective Bayesian optimisation (BO), an iterative optimisation technique utilising probabilistic machine learning models, can effectively explore this complex design space and return several optimal PF coil sets. These solutions span the Pareto front, a subset of the objective space that optimally satisfies the specified objective functions. We outline an easy-to-use BO framework and demonstrate that it outperforms alternative optimisation techniques while using significantly fewer computational resources. Our results show that BO is a promising technique for fusion design problems that rely on computationally demanding high-fidelity simulations.

References in corpus (11)

• Flat-top plasma operational space of the STEP power plant
• Direct stellarator coil design using global optimization: application to a comprehensive exploration of quasi-axisymmetric devices
• Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
• Bayesian optimization of massive material injection for disruption mitigation in tokamaks
• High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
• Multi-objective Bayesian optimization for design of Pareto-optimal current drive profiles in STEP
• Validation of the static forward Grad-Shafranov equilibrium solvers in FreeGSNKE and Fiesta using EFIT++ reconstructions from MAST-U
• Parametric scaling of power exhaust in EU-DEMO alternative divertor simulations
• FUSE (Fusion Synthesis Engine): A Next Generation Framework for Integrated Design of Fusion Pilot Plants
• Shaping of Magnetic Field Coils in Fusion Reactors using Bayesian Optimisation
• Sample-efficient Bayesian Optimisation Using Known Invariances