Aerodynamic model identification of an autonomous aircraft for airborne wind energy

Licitra, Giovanni; Bürger, Adrian; Williams, Paul; Ruiterkamp, Richard; Diehl, Moritz

doi:10.1002/oca.2485

Cited by 10 publications

(9 citation statements)

References 32 publications

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“…For a fair comparison, the tether drag (D t ) incorrectly attributed to the kite in ref. [28] has been subtracted from the total experimental drag following an overestimated approximation given in Equation (12). As expected, UnPaM, as a potential flow tool, captures relatively well the drag only for small angles of attack but, in general, the code underestimates C D .…”

Section: Comparison Of Numerical and Experimental Resultsmentioning

confidence: 86%

“…Whether the generation happens on the ground (ground generation) or onboard the aircraft (fly generation), a good aerodynamic characterization is important for the design, control, and optimization of these systems. For this reason, AWE community dedicated an important effort to develop aerodynamic models for particular AWE machines [2][3][4][5][6][7][8] and also to prepare numerical tools and experimental setups to study the plethora of AWE aircraft, which includes different types of kites and rigid-wings [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Comparison Of Numerical and Experimental Resultsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…The advancement in sensor technology has made the fabrication of micro-electro-sensor systems possible, which helps in logging the flight data acquired while performing system identification manoeuvres, even in small UAVs. Equation error methods (EEM), output error methods (OEM) [14][15][16], filter error methods (FEM) [17][18][19] and Artificial Intelligence-(AI) based methods [20][21][22][23][24] are primary aerodynamic parameter estimation methods. The least square cost function-based EEM has been touted as a promising alternative for a rapid parameter estimation technique because of its computational simplicity.…”

Section: Introductionmentioning

confidence: 99%

Parametric model identification of delta wing UAVs using filter error method augmented with particle swarm optimisation

Kumar

Saderla

et al. 2023

Aeronaut. j.

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From arsenal delivery to rescue missions, unmanned aerial vehicles (UAVs) are playing a crucial role in various fields, which brings the need for continuous evolution of system identification techniques to develop sophisticated mathematical models for effective flight control. In this paper, a novel parameter estimation technique based on filter error method (FEM) augmented with particle swarm optimisation (PSO) is developed and implemented to estimate the longitudinal and lateral-directional aerodynamic, stability and control derivatives of fixed-wing UAVs. The FEM used in the estimation technique is based on the steady-state extended Kalman filter, where the maximum likelihood cost function is minimised separately using a randomised solution search algorithm, PSO and the proposed method is termed FEM-PSO. A sufficient number of compatible flight data sets were generated using two cropped delta wing UAVs, namely CDFP and CDRW, which are used to analyse the applicability of the proposed estimation method. A comparison has been made between the parameter estimates obtained using the proposed method and the computationally intensive conventional FEM. It is observed that most of the FEM-PSO estimates are consistent with wind tunnel and conventional FEM estimates. It is also noticed that estimates of crucial aerodynamic derivatives ${C_{{L_\alpha }}},\;{C_{{m_\alpha }}},\;{C_{{Y_\beta }}},\;{C_{{l_\beta }}}$ and ${C_{{n_\beta }}}$ obtained using FEM-PSO are having relative offsets of 2.5%, 1.5%, 6.5%, 3.4% and 7.6% w.r.t. wind tunnel values for CDFP, and 1.4%, 1.9%, 0.1%, 9.6% and 7.5% w.r.t. wind tunnel values for CDRW. Despite having slightly higher Cramer-Rao Lower Bounds of estimated aerodynamic derivatives using the FEM-PSO method, the simulated responses have a relative error of less than 0.10% w.r.t. measured flight data. A proof-of-match exercise is also conducted to ascertain the efficacy of the estimates obtained using the proposed method. The degree of effectiveness of the FEM-PSO method is comparable with conventional FEM.

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“…Williams [11] summarises discretised tether models and introduces the quasi-static model of a discretised tether. Licitra et al [12,13] present a dynamic model of the Ampyx Power AP-3 (12 m 2 wing area) aircraft. Rapp et al [14,15] present the dynamic model and flight control framework underlying also the present work.…”

Section: Introductionmentioning

confidence: 99%

Six-Degrees-Of-Freedom Simulation Model for Future Multi-Megawatt Airborne Wind Energy Systems

Eijkelhof¹,

Schmehl²

2022

SSRN Journal

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Aerodynamic model identification of an autonomous aircraft for airborne wind energy

Cited by 10 publications

References 32 publications

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

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

Parametric model identification of delta wing UAVs using filter error method augmented with particle swarm optimisation

Six-Degrees-Of-Freedom Simulation Model for Future Multi-Megawatt Airborne Wind Energy Systems

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