Three-Dimensional Unsteady Aerodynamic Analysis of a Rigid-Framed Delta Kite Applied to Airborne Wind Energy

Abstract

The validity of using a low-computational-cost model for the aerodynamic characterization of Airborne Wind Energy Systems was studied by benchmarking a three-dimensional Unsteady Panel Method (UnPaM) with experimental data from a flight test campaign of a two-line Rigid-Framed Delta kite. The latter, and a subsequent analysis of the experimental data, provided the evolution of the tether tensions, the full kinematic state of the kite (aerodynamic velocity and angular velocity vectors, among others), and its aerodynamic coefficients. The history of the kinematic state was used as input for UnPaM that provided a set of theoretical aerodynamic coefficients. Disparate conclusions were found when comparing the experimental and theoretical aerodynamic coefficients. For a wide range of angles of attack and sideslip angles, the agreement in the lift and lateral force coefficients was good and moderate, respectively, considering UnPaM is a potential flow tool. As expected, UnPaM predicts a much lower drag because it ignores viscous effects. The comparison of the aerodynamic torque coefficients is more delicate due to uncertainties on the experimental data. Besides fully non-stationary simulations, the lift coefficient was also studied with UnPaM by assuming quasi-steady and steady conditions. It was found that for a typical figure-of-eight trajectory there are no significant differences between unsteady and quasi-steady approaches allowing for fast simulations.

Wing Deformation of an Airborne Wind Energy System in Crosswind Flight Using High-Fidelity Fluid–Structure Interaction

“…AWESs Aero S: [3,4] S: [5][6][7], U: [8] S: [9,10], U: [11,12] Structure [3] [6] [5,7,9] FSI [3,5,7] [6] [9,13] Atmosphere [12,14,15] [4] (LES), [12] (RANS) Dynamics [8] [4,16] [ 5,17] In Table 1, we review several levels of fidelity for different fields. In aerodynamics, lowfidelity models are based on the lifting-line techniques or look-up tables.…”

“…The aerodynamics of AWE systems were studied by Vimalakthan et al [11] and Viré et al [10] using CFD, but they also considered steady aerodynamics, without taking into account the crosswind motion of AWE systems or other unsteady effects. Castro-Fernández et al [8] took the crosswind motion into account by coupling the prescribed motion of the kite with an unsteady panel method.…”

Moving control surfaces in a geometry-resolved CFD model of an airborne wind energy system

“…Hence, they considered only the main wing and neglected the control surfaces. Castro-Fernandez et al [4] considered more complex motion in their aerodynamic simulation by prescribing crosswind flight motion to a panel representation of the wing, without control surfaces, using the vortex lattice method (VLM). None of the aforementioned studies on airborne wind energy has combined the motion of the aircraft and the explicit modeling of the control surface deflections in geometry-resolved CFD simulations.…”

Automatic testbed with a visual motion tracking system for airborne wind energy applications

“…With the same purpose, a multihole pitot tube was boarded on a rigid‐framed delta (RFD) kite to get accurate (about 1 degree) and time‐resolved measurements of the aerodynamic velocity vector, that is, angle of attack, sideslip angle, and airspeed magnitude 32 . The in situ knowledge of the aerodynamic velocity vector was shown to be essential to uncover underlying unsteady aerodynamic phenomena 32,33 . Regarding estimation techniques, extended Kalman filtering has been broadly used with purely kinematic process models, 29 in the form of a multiplicative extended Kalman filter to avoid singularities in the covariance matrix due to the use of quaternions 30 and including the aerodynamic forces and torques in the state vector 28,34 …”

“…32 The in situ knowledge of the aerodynamic velocity vector was shown to be essential to uncover underlying unsteady aerodynamic phenomena. 32,33 Regarding estimation techniques, extended Kalman filtering has been broadly used with purely kinematic process models, 29 in the form of a multiplicative extended Kalman filter to avoid singularities in the covariance matrix due to the use of quaternions 30 and including the aerodynamic forces and torques in the state vector. 28,34 Visual sensors were also applied to AWE.…”