Low-order modeling of ocean-current turbine blades with dynamic trailing edge flaps in the presence of external disturbances
ORAL
Abstract
Ocean current Turbines (OCTs) operating in arrays experience unsteady inflow
conditions due to their interactions with the wake emanating from upstream
turbines. The complex unsteady flow phenomena resulting from the interaction
between the oncoming wake and the moving rotors can lead to fluctuating forces and
loads on the blades of downstream devices, deteriorating their performance and
eventually causing fatigue and failure. We present a novel approach for tailoring the
performance of OCT blades in unsteady inflow conditions using a dynamic
trailing-edge flap (TEF) mechanism. We combine unsteady hydrodynamic theory with
high-fidelity numerical simulations to develop a low-order hydrodynamic model to
predict the unsteady flow phenomena and the resulting loads on OCT blade sections
with dynamic TEFs in the presence of external flow disturbances (EFD). The blade is
modeled as a bound-vortex distribution with time-varying camber at the flap location.
The flowfield comprising of the EFDs and the vorticity shed from the blade is
represented using free discrete vortices. Unsteady flow conditions result in the
formation and shedding of leading-edge vortices (LEVs), the intensity and timing of
which have a significant role in determining the trailing edge vortex shedding pattern
and the load fluctuations. The model is capable of predicting the LEV shedding pattern
of the blade using the concept of leading-edge suction parameter (LESP) and
computing the associated loads. High-fidelity unsteady RANS simulations are used to
validate the model. Parametric studies are conducted to identify and quantify the
relationship between the EFD parameters and the hydrodynamic quantities of the blade
such as lift, drag, pitching moment and vortex shedding pattern. Finally, the low-order
modeling framework is used to formulate strategies to mitigate the destructive effects of
the EFDs through active TEF deflection by tailoring the flowfield and loads of the blade.
conditions due to their interactions with the wake emanating from upstream
turbines. The complex unsteady flow phenomena resulting from the interaction
between the oncoming wake and the moving rotors can lead to fluctuating forces and
loads on the blades of downstream devices, deteriorating their performance and
eventually causing fatigue and failure. We present a novel approach for tailoring the
performance of OCT blades in unsteady inflow conditions using a dynamic
trailing-edge flap (TEF) mechanism. We combine unsteady hydrodynamic theory with
high-fidelity numerical simulations to develop a low-order hydrodynamic model to
predict the unsteady flow phenomena and the resulting loads on OCT blade sections
with dynamic TEFs in the presence of external flow disturbances (EFD). The blade is
modeled as a bound-vortex distribution with time-varying camber at the flap location.
The flowfield comprising of the EFDs and the vorticity shed from the blade is
represented using free discrete vortices. Unsteady flow conditions result in the
formation and shedding of leading-edge vortices (LEVs), the intensity and timing of
which have a significant role in determining the trailing edge vortex shedding pattern
and the load fluctuations. The model is capable of predicting the LEV shedding pattern
of the blade using the concept of leading-edge suction parameter (LESP) and
computing the associated loads. High-fidelity unsteady RANS simulations are used to
validate the model. Parametric studies are conducted to identify and quantify the
relationship between the EFD parameters and the hydrodynamic quantities of the blade
such as lift, drag, pitching moment and vortex shedding pattern. Finally, the low-order
modeling framework is used to formulate strategies to mitigate the destructive effects of
the EFDs through active TEF deflection by tailoring the flowfield and loads of the blade.
–
Presenters
-
Sebastian Mares
University of North Carolina at Charlotte
Authors
-
Sebastian Mares
University of North Carolina at Charlotte
-
Aditya Atre
University of North Carolina at Charlotte
-
Praveen K Ramaprabhu
University of North Carolina at Charlotte
-
John Hall
University of North Carolina at Charlotte
-
Arun Vishnu Suresh Babu
University of North Carolina at Charlotte