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

Gael De Oliveira, Ricardo Pereira, Nando Timmer, Ruud Van Rooij

Research output: Contribution to journalConference articleScientificpeer-review

3 Citations (Scopus)
436 Downloads (Pure)


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.

Original languageEnglish
Article number022009
Number of pages12
JournalJournal of Physics: Conference Series
Issue number2
Publication statusPublished - 19 Jun 2018
EventTORQUE 2018: The Science of Making Torque from Wind - Milano, Italy
Duration: 20 Jun 201822 Jun 2018


Dive into the research topics of 'Improved airfoil polar predictions with data-driven boundary-layer closure relations'. Together they form a unique fingerprint.

Cite this