Nacelle optimisation through multi-fidelity neural networks

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dc.contributor.authorSánchez-Moreno, Francisco
dc.contributor.authorMacManus, David
dc.contributor.authorTejero, Fernando
dc.contributor.authorSheaf, Christopher
dc.date.accessioned2024-08-01T13:30:23Z
dc.date.available2024-08-01T13:30:23Z
dc.date.freetoread2024-08-01
dc.date.issued2024-07-25
dc.description.abstractPurpose 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.
dc.description.journalNameInternational Journal of Numerical Methods for Heat & Fluid Flow
dc.identifier.citationSánchez-Moreno F, MacManus D, Tejero F, Sheaf C. (2024) Nacelle optimisation through multi-fidelity neural networks. International Journal of Numerical Methods for Heat & Fluid Flow, Available online 25 July 2024
dc.identifier.issn0961-5539
dc.identifier.urihttps://doi.org/10.1108/HFF-12-2023-0745
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/22705
dc.language.isoen
dc.publisherEmerald
dc.publisher.urihttps://www.emerald.com/insight/content/doi/10.1108/HFF-12-2023-0745/full/html
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.subjectmulti-fidelity
dc.subjectneural network
dc.subjectsurrogate model
dc.subjectmulti-objective optimisation
dc.subjectnacelle
dc.titleNacelle optimisation through multi-fidelity neural networks
dc.typeArticle
dcterms.dateAccepted2024-06-30

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