Improved airfoil polar predictions with data-driven boundary-layer closure relations

Title

Improved airfoil polar predictions with data-driven boundary-layer closure relations

 Author

De Oliveira Andrade, G.L. (TU Delft Wind Energy)
Balbino dos Santos Pereira, R. (TU Delft Wind Energy)
Timmer, W.A. (TU Delft Wind Energy)
van Rooij, R.P.J.O.M. (TU Delft Wind Energy)

Date

2018-06-19

 Abstract

The accuracy of airfoil polar predictions is limited by the usage of imperfect turbulence models. Can machine-learning improve this situation? Will airfoil polars teach the effect of turbulence on skin-friction? We try to answer these questions by refining turbulence treatment in the Rfoil code: boundary layer closure relations are learned from airfoil polar data. Two turbulent closure relations, for skin friction and energy shape factor, are parametrized with a class-shape transformation. An experimental database is then used to define code inaccuracy measures that are minimized with an interior point gradient algorithm. Results show that airfoil polars contain exploitable information about turbulent phenomena. Inferred closures agree with direct numerical simulation results of skin friction and the new code predicts drag more accurately. Maximum lift remains under-predicted but Rfoil maintains its robustness and suitability for optimization of wind energy airfoils.


 To reference this document use:

http://resolver.tudelft.nl/uuid:1a5eb04a-423c-4c40-93c5-13f177a253fe

 DOI

https://doi.org/10.1088/1742-6596/1037/2/022009

 ISSN

1742-6588

 Source

Journal of Physics: Conference Series, 1037 (2)

 Event

TORQUE 2018, 2018-06-20 → 2018-06-22, Milano, Italy

 Part of collection

Institutional Repository

 Document type

journal article

 Rights

© 2018 G.L. De Oliveira Andrade, R. Balbino dos Santos Pereira, W.A. Timmer, R.P.J.O.M. van Rooij
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