Accelerating the engineering design of breeder blankets with parametric optimisation and sequential learning

The competing requirements of fusion breeder blankets and the high dimensionality of their design space necessitate a systematic treatment to map the variations in performance against given objective metrics and to understand the operational envelope. In this endeavour, a digital engineering pipeline for design evaluation and optimisation has been developed. The tools involved are Hypnos for parametric breeder blanket geometry instantiation, OpenMC for neutronics analysis, MOOSE for thermal hydr