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 hydraulics analysis, and SLEDO for design space sampling, sensitivity analysis, and optimisation. An optimisation of the baseline design for a solid ceramic breeder mock-up that is relevant to the Lithium Breeding Tritium Innovation (LIBRTI) program is performed. Two optimisation studies are performed, the first involving only neutronics, while the second includes the impact of thermal hydraulics. The figures of merit are taken to be the tritium breeding ratio (TBR) and the pressure drop of the outer coolant (combined in a weighted sum for the second analysis). In the first study, for the same acquisition function (taken to be expected improvement), two different values are selected for the hyperparameter that controls the trade-off between exploration and exploitation. In the second study, with the inclusion of thermal hydraulics, a larger parameter space was explored to assess the performance of the method in a higher dimensionality setting. In both cases, the selected figures of merit were improved over the baseline design. Finally, we discuss extensions of the procedure to include a more thorough multi-physics analysis and a more sophisticated treatment of multiple objectives.