Renewable energy for Mars habitats is of key interest for future crewed missions. However, the low solar irradiation and low atmospheric pressure on Mars pose serious challenges to exploiting these resources reliably. In this chapter, we investigate the technical feasibility of a soft-kite-based airborne wind energy system and its potential to power a subsurface Mars habitat in combination with photovoltaics and short-term electrical storage. We propose a soft kite for its high surface-to-mass ratio, compact packing volume, adaptability to the available wind resource, and, thus, high capacity factor. First, the siting of the habitat is outlined, and the wind resources are quantified in terms of the wind speed probability distribution at the operational height of the system for different seasonal periods. Then, a performance model for the pumping cycle operation is developed to compute the power curve of the airborne wind energy system. Combining this with the wind statistics, a process for predicting the electricity yield at the habitat location is developed, which is then used to size all components of the hybrid power system to meet the continuous electrical power demand of 10 kW of the envisioned habitat.@en

Although technologically challenging, airborne wind energy systems have several advantages over conventional wind turbines that make them an interesting option for deployment on Mars. However, the environmental conditions on the red planet are quite different from those on Earth. The atmosphere’s density is about 100 times lower, and gravity is about one-third, which affects the tethered flight operation and harvesting performance of an airborne wind energy system. In this chapter, we investigate in how far the physics of tethered flight differs on the two planets, specifically from the perspective of airborne wind energy harvesting. The derived scaling laws provide a means to systematically adapt a specific system concept to operation on Mars using computation. Sensitivity analyses are conducted for two different sites on Mars, drawing general conclusions about the technical feasibility of using kites for harvesting wind power on the red planet.@en

Soft-wing kites for airborne wind-energy harvesting function as flying tensile membrane structures, each of whose shape depends on the aerodynamic load distribution and vice versa. The strong two-way coupling between shape and loading poses a complex fluid-structure interaction problem. Since computational models for such problems do not yet meet the requirements of being accurate and at the same time fast, kite designers usually work on the basis of intuition and experience, combined with extensive iterative flight testing. This paper presents a fast aero-structural model of leading-edge inflatable kites for the design phase of airborne wind-energy systems. The fluid-structure interaction solver couples two fast and modular models: a particle system model to capture the deformation of the wing and bridle-line system and a 3D nonlinear vortex step method coupled with viscous 2D airfoil polars to describe the aerodynamics. The flow solver was validated with several wing geometries and proved to be accurate and computationally inexpensive for pre-stall angles of attack. The coupled aero-structural model was validated using experimental data, showing good agreement in the deformations and aerodynamic forces. Therefore, the speed and accuracy of this model make it an excellent foundation for a kite design tool.@en

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.

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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.

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