AI-driven multidisciplinary conceptual design of unmanned aerial vehicles

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dc.contributor.authorKarali, Hasan
dc.contributor.authorInalhan, Gokhan
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2024-01-19T11:09:20Z
dc.date.available2024-01-19T11:09:20Z
dc.date.issued2024-01-04
dc.description.abstractThis paper presents a multidisciplinary conceptual design framework for unmanned aerial vehicles based on artificial intelligence-driven analysis models. This approach leverages AI- driven analysis models that include aerodynamics, structural mass, and radar cross-section predictions to bring quantitative data to the initial design stage, enabling the selection of the most appropriate configuration from various optimized concept designs. Due to the design optimization cycle, the initial dimensions of key components such as the wing, tail, and fuselage are provided more accurately for later design activities. Simultaneously, the generated structure enables more suitable design point selection through the feedback loop within the design iteration. Therefore, in addition to reducing design costs, this approach also offers a substantial time advantage in the overall design process.en_UK
dc.identifier.citationKarali H, Inalhan G, Tsourdos A. (2024) AI-driven multidisciplinary conceptual design of unmanned aerial vehicles. In: AIAA SCITECH 2024 Forum, 8-12 January 2024, Orlando, Florida. Paper number AIAA 2024-1708en_UK
dc.identifier.urihttps://doi.org/10.2514/6.2024-1708
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20692
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleAI-driven multidisciplinary conceptual design of unmanned aerial vehiclesen_UK
dc.typeConference paperen_UK

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