Optimization of installed compact and robust nacelles using surrogate models

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dc.contributor.authorSanchez Moreno, Francisco
dc.contributor.authorMacManus, David
dc.contributor.authorHueso Rebassa, Josep
dc.contributor.authorTejero, Fernando
dc.contributor.authorSheaf, Christopher
dc.date.accessioned2022-09-14T09:35:58Z
dc.date.available2022-09-14T09:35:58Z
dc.date.issued2022
dc.description.abstractThe design and optimization of aero-engine nacelles in a configuration installed on the airframe may be an important consideration to realize the cycle benefits of new ultra-high bypass ratio aero-engines. However, this is typically a high-dimensional design problem and there is a need to reduce the associated computational costs. This work presents a method for aerodynamic nacelle optimization for an installed configuration and provides further knowledge about the characteristics of this design space. The methodology includes single fidelity surrogate models built with inviscid flow solutions. Gaussian process regression and artificial neural networks are tested as modelling techniques. Viscous computations are used to assess the optimized designs at cruise and off-design windmilling diversion condition. This approach yielded an optimal design with a reduction in fuel burn of about 0.56% relative to a design optimized in isolated configuration without considering the powerplant integration effects. The optimal design also met the robustness criteria in terms of limited flow separation at the windmilling diversion conditions.en_UK
dc.identifier.citationSanchez-Moreno F, MacManus D, Hueso-Rebassa J, et al., (2022) Optimization of installed compact and robust nacelles using surrogate models. In: 33rd Congress of the International Council of the Aeronautical Sciences: ICAS 2022, 4-9 September 2022, Stockholm, Swedenen_UK
dc.identifier.issn2958-4647
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18444
dc.identifier.urihttps://www.icas.org/ICAS_ARCHIVE/ICAS2022/data/preview/ICAS2022_0167.htm
dc.language.isoenen_UK
dc.publisherICASen_UK
dc.rightsAttribution 4.0 International
dc.rights© The Author.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectnacelleen_UK
dc.subjectoptimizationen_UK
dc.subjectinstallation effectsen_UK
dc.subjectsurrogate modelen_UK
dc.subjectCFDen_UK
dc.titleOptimization of installed compact and robust nacelles using surrogate modelsen_UK
dc.typeConference paperen_UK
dcterms.dateAccepted2022-07

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