Leading-edge inflatable (LEI) kites are morphing aerodynamic surfaces that are actuated by the bridle line system. Their design as tensile membrane structures has several implications for aerodynamic performance. Because of the pronounced C shape of the wings, a considerable part of the aerodynamic forces is redirected sideways and used for steering. The inflated tubular frame introduces flow recirculation zones on the pressure side of the wing. In this paper, we present wind tunnel measurements of a 1:6.5 rigid-scale model of the 25 ^m2 TU Delft V3 LEI kite developed specifically for airborne wind energy (AWE) harvesting. Aerodynamic forces and moments were recorded in an open-jet wind tunnel over wide ranges of flow conditions, including angles of attack from -11.6 to 24.5°, sideslip angles from -20 to 20°, and freestream velocities from 5 to 25 m^s-1. The wind tunnel measurements were performed with and without zigzag tape along the model's leading edge to investigate the possible boundary layer tripping effect of the stitching seam connecting the canopy to the inflated tube. At a Reynolds number of 5×¹⁰⁵, the addition of zigzag tape was found to reduce lift and increase drag, indicating a negative impact on aerodynamic performance. The rigid-scale model was manufactured to match the undeformed geometry employed in Reynolds-averaged Navier–Stokes (RANS) simulations from the literature, rather than the unknown in-flight deformed geometry. A representative subset of the measurements was used to benchmark both these RANS and new vortex-step method simulations. Both computational methods successfully reproduced the measured trends under nominal operating conditions. While the post-stall discrepancy persists, excellent agreement was observed for lift, drag, and side force coefficients, with lift deviations remaining within the 10% range.

Different Design Driving Load constraints (DDLs), are explored in this work to determine under which constraints and conditions a winglet can have an added value to the wind turbine blade design. Multi-objective Bayesian optimization is used to maximize the rotor's power production while minimizing the flapwise DDLs. Surrogate models, created using machine learning techniques such as Gaussian Processes and Bayesian Neural Networks, are used in combination with an acquisition function, to determine what designs should be evaluated by the lifting line model AWSM, with the goal to obtain designs that lie on the Pareto front of two or more objectives. The recent Bayesian Neural Networks as surrogate model were able to find the Pareto-front most effectively in this work. Furthermore, the results show that different DDL constraints led to different winglet designs, with noticeable differences between upwind and downwind winglet designs. Winglet designs were found to be able to increase power without increasing the thrust, root flapwise bending moment and flapwise bending moment at radial locations on the blade. A noticeable increase in power was found when introducing sweep to the winglet design.

In this paper, we present the design and computational model of a representative multi-megawatt airborne wind energy (AWE) system, together with a simulation framework that accounts for the flight dynamics of the fixed-wing aircraft and the sagging of the tether, combining this with flight control and optimisation strategies to derive the power curve of the system. The computational model is based on a point mass approximation of the aircraft, a discretisation of the tether by five elastic segments and a rotational degree of freedom of the winch. The aircraft has a wing surface area of 150 m² and is operated in pumping cycles, alternating between crosswind flight manoeuvres during reel out of the tether, and rapid decent towards the ground station during reel in. To maximise the net cycle power, we keep the design parameters of the aircraft constant, while tuning the operational and controller parameters for different wind speeds and given contraints. We find that the presented design can generate a net cycle power of up to 3.8 megawatts.