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Nacelle optimisation through multi-fidelity neural networks

Francisco Sánchez-Moreno, David MacManus, Fernando Tejero, Christopher Sheaf

International Journal of Numerical Methods for Heat & Fluid Flow

ISSN: 0961-5539

Article publication date: 25 July 2024

Issue publication date: 4 September 2024

55

Abstract

Purpose

Aerodynamic shape optimisation is a complex problem usually governed by transonic non-linear aerodynamics, a high dimensional design space and high computational cost. Consequently, the use of a numerical simulation approach can become prohibitive for some applications. This paper aims to propose a computationally efficient multi-fidelity method for the optimisation of two-dimensional axisymmetric aero-engine nacelles.

Design/methodology/approach

The nacelle optimisation approach combines a gradient-free algorithm with a multi-fidelity surrogate model. Machine learning based on artificial neural networks (ANN) is used as the modelling technique because of its ability to handle non-linear behaviour. The multi-fidelity method combines Reynolds-averaged Navier Stokes and Euler CFD calculations as high- and low-fidelity, respectively.

Findings

Ratios of low- and high-fidelity training samples to degrees of freedom of nLF/nDOFs = 50 and nHF/nDOFs = 12.5 provided a surrogate model with a root mean squared error less than 5% and a similar convergence to the optimal design space when compared with the equivalent CFD-in-the-loop optimisation. Similar nacelle geometries and aerodynamic flow topologies were obtained for down-selected designs with a reduction of 92% in the computational cost. This highlights the potential benefits of this multi-fidelity approach for aerodynamic optimisation within a preliminary design stage.

Originality/value

The application of a multi-fidelity technique based on ANN to the aerodynamic shape optimisation problem of isolated nacelles is the key novelty of this work. The multi-fidelity aspect of the method advances current practices based on single-fidelity surrogate models and offers further reductions in computational cost to meet industrial design timescales. Additionally, guidelines in terms of low- and high-fidelity sample sizes relative to the number of design variables have been established.

Keywords

Acknowledgements

This project has received funding from the Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 820997.

Data availability statement: Owing to commercial confidentiality agreements the supporting data are not available.

Citation

Sánchez-Moreno, F., MacManus, D., Tejero, F. and Sheaf, C. (2024), "Nacelle optimisation through multi-fidelity neural networks", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 34 No. 9, pp. 3615-3634. https://doi.org/10.1108/HFF-12-2023-0745

Publisher

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Emerald Publishing Limited

Copyright © 2024, Rolls-Royce plc

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