Design Optimisation and Flow Characterisation for Future Aeroengine Axial Fan Blades

The increased need to design higher performing aerodynamic shapes has led to design optimisation cycles requiring high-fidelity CFD models and high-dimensional parametrisation schemes. The computational cost of employing global search algorithms on such scenarios has typically been prohibitive for most academic and industrial environments. In this work, a novel methodology is presented, called AInADS. This strategy leverages the capabilities of Artificial Neural Networks for regressing complex unstructured data, while coupling them with dimensionality reduction algorithms. This approach enables employing global-based optimisation methods on high-dimensional applications through a reduced computational cost. The capabilities of AInADS are demonstrated on three turbomachinery applications of significant industrial relevance. The first case performs the efficiency optimisation of a modern jet engine fan blade with constrained pressure ratio. This outcome is compared against a state-of-the-art adjoint-based approach. Results indicate the strategy proposed achieves comparable improvements to its adjoint counterpart with a reduced computational cost and run time, and can scale better to multi-objective optimisation applications. The second application concerns the stability range of axial fan blades. Historically, the tip clearance size has been considered to be the main factor driving its behaviour. This work reveals that the stall characteristics are defined by the axial momentum flux of the tip leakage flow and that tip clearance is primarily a strong driver for this metric. AInADS is employed for carefully tailoing the axial momentum via three-dimensional design, which enables a higher degree of control over the stability range for cases where the tip clearance responds to other considerations and cannot be defined for this purpose. The effect of the axial momentum on efficiency is also addressed and the trade-off between operability range and design point performance derived. The results show that that the conditions for optimal stability differ from those for optimal efficiency and that control over the axial momentum enables tuning the design for a desired exchange. Numerical simulations have been employed to drive the analysis through a high-fidelity computational model whose behavior is supported by rich set of experimental data. Contrary to current belief, results further indicate that an accurate characterisation of stall, including onset mechanism, can be achieved through steady-state simulations, minimising the need for expensive time-accurate computations during the design phase. The final application introduces uncertainties in the design process of axial fan blades. As they are manufactured, blades deviate from the design intent shape and such geometrical variability translates to performance drifts. This work makes use of AInADS, coupled with Uncertainty Quantification methods, to estimate the statistical behaviour of a fan blade when subjected to typical manufacturing deviations. This knowledge is employed to optimise the shape of the fan blade and improve its robustness to uncertain shape deviations.