RELIABILITY-BASED DESIGN OPTIMISATION OF A DUCTED PROPELLER THROUGH MULTI-FIDELITY LEARNING

This paper proposes to apply multi-fidelity learning for reliability-based design optimisation of a ducted propeller. Theoretically, the efficiency of a propeller can be increased by placing the propeller into a duct. The increased efficiency makes the ducted propeller an appealing option for electrical aviation where optimal electricity consumption is vital. The electricity consumption is mainly dictated by the required power to reach the required thrust force. Recent design optimisation techniques such as machine learning can help us to reach high thrust to power ratios. Due to the expensive computational fluid dynamics simulations a multi-fidelity learning algorithm is investigated here for the application of ducted propeller design. The limited number of high-fidelity numerical experiments cannot provide sufficient information about the landscape of the design field and probability field. Therefore, information from lower fidelity simulations is fused into the high-fidelity surrogate using the recently published recursive co-Kriging technique augmented with Gaussian-Markov Random Fields. At each level the uncertainty can be modelled via a polynomial chaos expansion which providesa variable-fidelity quantification technique of the uncertainty. This facilitates the calculation of risk measures, like conditional Value-at-Risk, for reliability-based design optimisation. The multi-fidelity surrogate model can be adaptively refined following a similar strategy to the Efficient Global Optimisation using the expected improvement measure. The proposed combination of techniques provides an efficient manner to conduct reliability-based optimisation on expensive realistic problems using a multi-fidelity learning technique.