energies
Article
Modeling and Control of Dynamic Stall Loads on a Smart
Airfoil at Low Reynolds Number
Ayman Mohamed
1,2,
* , David Wood
2,
* and Jeffery Pieper
2
Citation: Mohamed, A.; Wood, D.;
Pieper, J. Modeling and Control of
Dynamic Stall Loads on a Smart
Airfoil at Low Reynolds Number.
Energies 2021, 14, 4958. https://
doi.org/10.3390/en14164958
Academic Editors: Grzegorz Nowak,
Iwona Nowak and Francesco
Castellani
Received: 8 June 2021
Accepted: 1 August 2021
Published: 13 August 2021
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1
Mechanical Design and Production Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
2
Department of Mechanical and Manufacturing Engineering, University of Calgary,
Calgary, AB T2N 1N4, Canada; pieper@ucalgary.ca
* Correspondence: ayman.mohamed2@ucalgary.ca (A.M.); dhwood@ucalgary.ca (D.W.)
Abstract: This article describes the development and testing of a modified, semi-empirical ONERA
dynamic stall model for an airfoil with a trailing edge flap—a “smart airfoil”—pitching at reduced
frequencies up to 0.1. The Reynolds number is 10
5
. The model reconstructs the load fluctuations
associated with the shedding of multiple dynamic stall vortices (DSVs) in a time-marching solution,
which makes it suitable for real-time control of a trailing edge flap (TEF). No other model captures
the effect of the DSVs on the aerodynamic loads on smart airfoils. The model was refined and
tuned for force measurements on a smart NACA 64
3
-618 airfoil model that was pitching with an
inactive TEF and was validated against the measurements when the TEF was activated. A substantial
laminar separation bubble can develop on this airfoil, which is challenging for modelers of the
unsteady response. A closed-loop controller was designed offline in SIMULINK, and the output of
the controller was applied to the TEF in a wind tunnel. The results indicated that the model has a
comparable accuracy for predicting loads with the active TEF compared to inactive TEF loads. In the
fully separated flow regime, the controller performed worse when dealing with the development of
the laminar separation bubble and DSVs.
Keywords: dynamic stall model; trailing edge flap; vortex shedding; ONERA; unsteady; airfoil pitching
1. Introduction
The interest in affordable wind energy production has led to technological innovations
such as the smart rotor in which the time-dependent loads, induced by unsteady conditions,
are controlled by active aerodynamic devices. For an airfoil in periodic motion, the unsteady
flow is commonly quantified by the reduced frequency, k = ωc/2V
∞
, where ω is the
angular frequency, c is the airfoil chord length, and V
∞
is the free-steam velocity [1]. There
are many factors that cause unsteadiness, including the complex nature of wind, yaw,
the aeroelastic response, particularly of the blades, and tower shadow effects [2,3]. As a
consequence of all these factors, the blades are subjected to strong unsteady fluctuations
in the angle of attack (α defined in Figure 1) and V
∞
, which can lead to dynamic stall.
Dynamic stall delays flow separation to an angle that is much higher than the static stall
angle (α
ss
) and is followed by the development of a large dynamic stall vortex (DSV),
which generates overshoots in the aerodynamic loads while convecting over the blade [4].
In addition, after the DSV leaves the blade, a secondary DSV might start to shed, inducing
an additional overshoot of lift (C
l
). These loads can excite the vibrational modes of the
blades and increase the vibration. Although structural failure caused by such vibrations
might not occur immediately, they will reduce the fatigue life of the blades and perhaps
other components as well [5]. Utilizing an aerodynamic control device, such as a trailing
edge flap (TEF), can alleviate fatigue loads through its high-frequency response and is
additionally attractive because of its low power consumption. However, TEFs have yet
to be included in wind turbine blades as more experiments are required to optimize the
design parameters, to introduce an effective control strategy, and to provide modeling tools
Energies 2021, 14, 4958. https://doi.org/10.3390/en14164958 https://www.mdpi.com/journal/energies