Virtual design analysis has become an indispensable component in most engineering disciplines. Despite the immense developments and availability of computational resources, the relative computational cost of high-fidelity simulations is getting more and more expensive. This opened the chapter of multi-fidelity learning techniques in the field of automated design optimisation.
This work presents a novel multi-fidelity surrogate-assisted design optimisation approach for computationally expensive aerospace applications under uncertainty.
The proposed optimisation framework overcomes the challenges of probabilistic design optimisation of computationally expensive problems and is capable of finding designs with optimal statistical performance for both single- and multi-objective problems, as well as constrained problems. Our approach performs the design optimisation with a limited computational budget thanks to the integrated multi-fidelity surrogates for design exploration and uncertainty quantification. The design optimisation is realised following the principles of Bayesian optimisation. The acquisition function balances exploration and exploitation of the design space and allocates the available budget efficiently considering the cost and accuracy of the fidelity levels.
To validate the proposed optimisation framework, available multi-fidelity test functions were tailored for benchmarking problems under uncertainty. The benchmarks showed that it is profitable to use multi-fidelity surrogates when the computational budget is too limited to allow for the construction of an accurate surrogate with high-fidelity simulations but is large enough to generate a great number of low-fidelity data.
The applicability of the proposed optimisation framework for aerospace applications is presented through optimisation studies of a propeller blade airfoil and a 3D propeller blade.
