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 paper, a novel strategy is presented that 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. This methodology is demonstrated on the efficiency optimisation of a modern jet engine fan blade with constrained pressure ratio. The 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 can scale better to multi-objective optimisation applications.

GLOBAL OPTIMISATION of A TRANSONIC FAN BLADE through AI-ENABLED ACTIVE SUBSPACES

Lopez D. I.
;
Ghisu T.;
2021-01-01

Abstract

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 paper, a novel strategy is presented that 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. This methodology is demonstrated on the efficiency optimisation of a modern jet engine fan blade with constrained pressure ratio. The 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 can scale better to multi-objective optimisation applications.
2021
978-0-7918-8490-4
computational fluid dynamics
Design optimisation
fan aerodynamics
neural networks
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/320732
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