Radiation shielding represents a critical engineering challenge in fusion reactor development, where intense neutron production demands efficient protection for personnel, equipment, and surrounding infrastructure. Traditional design approaches rely on iterative testing or uniform parameter sampling, which consume time and resources without guaranteeing optimal solutions.
Shield for Fusion (S4F) addresses this limitation through an automated optimization framework combining OpenMC neutronics simulations with genetic algorithms. The system evaluates four-layer shielding cabinets constructed from six commercially available materials, systematically exploring approximately 10,000 possible configurations. Rather than exhaustive sampling, the genetic algorithm intelligently navigates the design space, identifying high-performing parameter combinations within roughly 100 simulations—a substantial reduction in computational cost.
The optimization framework simultaneously manages four competing objectives: minimizing neutron flux transmission, reducing radiation dose exposure, controlling material expenses, and limiting overall shielding weight. This multi-objective approach reflects real-world constraints faced by fusion facility designers who must balance safety requirements against economic and practical limitations.
Testing confirms the genetic algorithm outperforms uniform sampling, discovering configurations with superior neutron attenuation and lower costs. The framework incorporates density correction factors to account for material variations, enhancing applicability across manufacturing specifications.
A particularly valuable feature is the inverse design tool, which rapidly retrieves shielding configurations matching specified performance targets. This capability enables designers to input desired dose rates or attenuation levels and receive ready-made solutions for their application, currently supporting multiple neutron source spectra.
The availability of open-source code promotes adoption and customization across different fusion programs and simulation environments. As fusion energy moves toward demonstration and commercial deployment, efficient shielding optimization becomes essential for reducing construction timelines and operational costs while maintaining safety standards.
