Low-Reynolds-number investigations on the ability of the strip of e-TellTale sensor to detect the flow features over wind turbine blade section: flow stall and reattachment dynamics

Antoine Soulier (1, 2) , Caroline Braud (1) , Dimitri Voisin , Bérengère Podvin (3)

1 LHEEA - Laboratoire de recherche en Hydrodynamique, Énergétique et Environnement Atmosphérique
2 Entreprise MerAgitée
3 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

Abstract. Monitoring the flow features over wind turbine blades is a challenging task that has become more and more crucial. This paper is devoted to demonstrate the ability of the e-TellTale sensor to detect the flow stall–reattachment dynamics over wind turbine blades. This sensor is made of a strip with a strain gauge sensor at its base. The velocity field was acquired using time-resolved particle image velocimetry (TR-PIV) measurements over an oscillating 2D blade section equipped with an e-TellTale sensor. PIV images were post-processed to detect movements of the strip, which was compared to movements of flow. Results show good agreement between the measured velocity field and movements of the strip regarding the stall–reattachment dynamics.

Domains: Engineering Sciences [physics]; Reactive fluid environment; Environmental Sciences; Environmental Engineering

Antoine Soulier, Caroline Braud, Dimitri Voisin, Bérengère Podvin. Low-Reynolds-number investigations on the ability of the strip of e-TellTale sensor to detect the flow features over wind turbine blade section: flow stall and reattachment dynamics. Wind Energy Science, 2021, 6 (2), pp.409-426. ⟨10.5194/wes-6-409-2021⟩. ⟨hal-03437709⟩