Design of a deep learning based nonlinear aerodynamic surrogate model for UAVs

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dc.contributor.authorKarali, Hasan
dc.contributor.authorDemirezen, Mustafa U.
dc.contributor.authorYukselen, Mahmut A.
dc.contributor.authorInalhan, Gokhan
dc.date.accessioned2020-03-05T19:14:26Z
dc.date.available2020-03-05T19:14:26Z
dc.date.issued2020-01-05
dc.description.abstractIn this paper, we present a deep learning based surrogate model to determine non-linear aerodynamic characteristics of UAVs. The main advantage of this model is that it can predict the aerodynamic properties of the configurations very quickly by using only geometric configuration parameters without the need for any special input data or pre-process phase. This provides a crucial and explicit design and synthesis tool for mini and small UAVs. To achieve this goal, a large data set, which includes thousands of wing-tail configurations geometry parameters and performance coefficients, was generated using the previously developed and computationally very efficient non-linear lifting line method. This data is used for training the artificial neural network model. The preliminary results show that the neural network model has generalization capability. The aerodynamic model predictions show almost 1-1 coincidence with the numerical data even for configurations with different 2D profiles that are not used in model training. Specifically, the results of test cases are found to capture both the linear and non-linear region of the lift curves, by predicting the maximum lift coefficient, the stall angle of attack, and the characteristics of post-stall region correctly. Similarly, total drag and pitching moment coefficients are predicted successfully. The developed methodology provides the basis for bidirectional design optimization and offers insight for an inverse tool that can calculate geometry parameters for a given design condition.en_UK
dc.identifier.citationKarali H, Demirezen MU, Yukselen MA, Inalhan G. (2020) Design of a deep learning based nonlinear aerodynamic surrogate model for UAVs. In: 2020 AIAA SciTech Forum, 6-10 January 2020, Orlando, Florida, USAen_UK
dc.identifier.urihttps://doi.org/10.2514/6.2020-1288
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/15239
dc.language.isoenen_UK
dc.publisherAIAAen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleDesign of a deep learning based nonlinear aerodynamic surrogate model for UAVsen_UK
dc.typeConference paperen_UK

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