A surrogate-based multi-disciplinary design optimization framework modeling wing–propeller interaction

Journal Article (2018)
Author(s)

Christian Alba (Student TU Delft)

Ali Elham (Technical University of Braunschweig)

Brian J. German (Georgia Institute of Technology)

Leo Veldhuis (TU Delft - Flight Performance and Propulsion)

Research Group
Flight Performance and Propulsion
Copyright
© 2018 Christian Alba, Ali Elham, Brian J. German, L.L.M. Veldhuis
DOI related publication
https://doi.org/10.1016/j.ast.2018.05.002
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 Christian Alba, Ali Elham, Brian J. German, L.L.M. Veldhuis
Research Group
Flight Performance and Propulsion
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Volume number
78
Pages (from-to)
721-733
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Abstract

This paper presents a multi-disciplinary design optimization (MDO) framework for design of a general aviation aircraft wing considering the effects of tractor propellers on the wing aerodynamic characteristics. In pursuit of this objective, a wing–propeller full-interaction aerodynamic routine was developed and integrated with structural and performance models. A substantive contribution of the work is the approach for effectively modeling wing effects on propeller slipstream development while still leveraging traditional propeller and wing analysis tools. Several optimizations were carried out, starting from an existing aircraft design, to test different local and global surrogate-based optimization frameworks and to allow for the assessment of the resulting solutions and corresponding computational performance metrics. Examination of the total function calls and run times showed that the use of surrogate models improves overall optimization performance, provided that suitable surrogate modeling techniques are chosen.

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