Ducted propellers are promising candidates for electrical aircrafts where the
propulsion efficiency must be highly optimised. The aerodynamic performance
and efficiency of ducted propellers can be obtained through models and
experiments of various fidelity, ranging from cheap analytic formulas to
expensive Computational Fluid Dynamics simulations. The ducted propeller
design problem also involves several uncertainties arising from environmental
conditions or manufacturing imperfections. To alleviate the computational
burden of the uncertainty-based design optimisation of ducted propellers, we
propose a new approach that combines multi-fidelity information fusion and
dimensionality reduction by using different surrogate models for the design and
probabilistic spaces. The method is validated on a simplified propeller model.
