Bayesian Methods for Magnetic and Mechanical Optimization of Superconducting Magnets for Fusion

Stellarators as compact fusion power sources have incredible potential to help combat climate change. However, the task of making that a reality faces many challenges. This work uses Bayesian optimization, (BO) which is a method that is well suited to black-box optimizations, to address the complicated optimization problem inherent by stellarator design. In particular it focuses on the mechanical optimization necessary to withstand the Lorentz forces generated by the magnetic coils. This work leverages surrogate models that are constructed to integrate as much information as possible from the available data points, significantly reducing the number of required model evaluations. It showcases the efficacy of Bayesian optimization as a versatile tool for enhancing both magneto-static and mechanical properties within stellarator winding packs. Employing a suite of Bayesian optimization algorithms, we iteratively refine 2D and 3D models of solenoid and stellarator configurations, and demonstrate a 15% increase in optimization speed using multi-fidelity Bayesian optimization. For fusion technology to progresses from experimental stages to commercial viability, precise and efficient design methodologies will be essential. By emphasizing its modularity and transferability, our approach lays the foundation for streamlining optimization processes, facilitating the integration of fusion power into a sustainable energy infrastructure.