Artificial intelligence to enhance aerodynamic shape optimisation of the Aegis UAV

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dc.contributor.authorAzabi, Yousef
dc.contributor.authorSavvaris, Al
dc.contributor.authorKipouros, Timoleon
dc.date.accessioned2019-05-07T14:56:46Z
dc.date.available2019-05-07T14:56:46Z
dc.date.issued2019-04-04
dc.description.abstractThis article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem.en_UK
dc.identifier.citationYousef Azabi, Al Savvaris and Timoleon Kipouros. Artificial intelligence to enhance aerodynamic shape optimisation of the Aegis UAV. Machine Learning and Knowledge Extraction, 2019, Volume 1, Issue 2, pp. 552-574en_UK
dc.identifier.issn2504-4990
dc.identifier.urihttps://doi.org/10.3390/make1020033
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14132
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmachine learningen_UK
dc.subjectdata visualizationen_UK
dc.subjectMulti-Objective Particle Swarm Optimisationen_UK
dc.subjectMulti-Objective Tabu Searchen_UK
dc.subjectnimrod/toolen_UK
dc.subjectparallel coordinatesen_UK
dc.subjectAthena Vortex Latticeen_UK
dc.titleArtificial intelligence to enhance aerodynamic shape optimisation of the Aegis UAVen_UK
dc.typeArticleen_UK

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