Dynamic Model of a Ground-Controlled Ram-Air Kite System for Ship Propulsion

Book (2025)
Author(s)

Bart van de Lint (Student TU Delft)

Uwe Fechner (TU Delft - Aerospace Engineering)

Roland Schmehl (TU Delft - Aerospace Engineering)

Research Group
Wind Energy
DOI related publication
https://doi.org/10.5281/ZENODO.20664773 Final published version
More Info
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Publication Year
2025
Language
English
Research Group
Wind Energy
Event
Wind Energy Science Conference 2025 (2025-06-24 - 2025-06-27), Nantes, France
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Abstract

The European Union has set a goal to become carbon neutral by 2050 through the European Green Deal. Torealize this goal, all sectors of the economy, including shipping, have to become carbon neutral. One way to makeshipping carbon neutral is by directly using wind energy for ship propulsion. Different methods for this are sails,Flettner rotors, and kites. Traditional sails rely on heavy masts and capture low-velocity winds near the oceansurface. Kites operate at higher altitudes where wind speeds are substantially higher and more consistent, usinglightweight materials that induce low vessel bending moments while producing 5 to 25 times more power thanconventional sails [1]. One key advantage of kites is that they can fly cross-wind, achieving much higher apparentwind speed and, thus, thrust. The main disadvantage of kite systems is that they are unstable without active control.To stabilize the system, a controller is needed, and designing such a controller requires an accurate kite model.This work presents a dynamic model of a ground-controlled ram-air kite system for ship propulsion that com-bines a rigid body representation of the kite concerning its dynamic behavior with an aerodynamic model of themain wing and two tether-actuated trailing edges. The key innovation of this work is the accurate modeling of trail-ing edge deformation and twist in a dynamic model capable of real-time. The open-source system model achieves10 times real-time performance on an Intel Core i9-9980HK while maintaining good accuracy in flight simulations.This was achieved using the symbolic modeling framework ModelingToolkit.jl [2] and the Julia programming lan-guage. This modeling framework enables high simulation speed and fast linearization of the model, which willallow the use of modern control design methods, such as model predictive control [3] or machine learning.

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