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

Abstract

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.

Artificial intelligence, machine learning, deep learning, and big data techniques for the advancements of superconducting technology: a road to smarter and intelligent superconductivity

“…Demonstration Power Plants (DEMO), are conceptualized as large, high-field, and steady-state tokamaks intended to be the second generation of fusion power plants, following ITER's implementation [14]. To date, the common practice for designing the most critical components of DEMOs, such as the toroidal field coils winding pack, has relied on a sequential trial and error process using a combination of analytical and FE methods.…”

“…They significantly reduce the computational time required for magneto-structural analysis and guarantee convergence to an optimal solution for the winding pack configuration. The proposed methodology has been well demonstrated for the 2019 ENEA DEMO configuration, which consists of 16 toroidal field coils fabricated using 6 Nb 3 Sn double layers and a windand-react technique in [14].…”

Artificial intelligence-based models for reconstructing the critical current and index-value surfaces of HTS tapes

“…In [12], a multi-width no-insulation magnet critical current at different operating temperatures was estimated using the critical current density distribution, which in turn was found using a double hidden layer Bayesian regularised neural network. Another extensive discussion about the advantages and drawbacks of AI methods with respect to conventional approaches, such as analytical formulas and look-up tables, which also investigates and reports a comparison regarding the different computational times, can be found in [13].…”