A methodological approach for the optimal design of the toroidal field coils of a tokamak device using artificial intelligence

As prototypes of future commercial tokamaks, DEMOs nuclear fusion power plants are expected to be able to produce cost-effective electrical power. In this view, an optimized design becomes crucial in the whole engineering workflow. Up to now, the design of one of the most critical components, the cross-section of each of the toroidal field coils inner leg winding pack, was performed using a sequential trial-and-error procedure. In this work, a novel comprehensive approach is proposed to include all the main design aspects into a unified tool taking advantage of artificial neural networks for faster computation in finding optimal design configurations. This procedure overcomes several difficulties including dealing with both real-valued and discrete design variables, the significant CPU-time of magneto-structural analysis and also guarantees the optimality for the winding pack configuration. The proposed methodology was demonstrated for the 2019 ENEA DEMO configuration which includes 16 toroidal field coils, made-up of 6 Nb3Sn double layers and a Wind and React manufacturing technique.