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Aeroelastically Constrained Optimal Path Planning of a Fixed-Wing Airborne Wind Energy System
arXiv cs.SY0 views
arXiv:2611.13678v1 Announce Type: new Abstract: Multi-megawatt fixed-wing airborne wind energy systems (AWES) use an aircraft with large, slender wings that deform over the pumping cycle. To harvest maximum energy, the cycle is usually designed by means of dedicated reference trajectory optimization routines. It is well known that such routines rely heavily on the system model and that close alignment between the model and the physical system is required for the results to hold in practice. Nevertheless, the wing is usually treated as rigid and structural limits are respected indirectly through upper bounding of states and inputs. In this work, we directly enforce bounds on the elastic deflection and twist of the wing in the power-maximizing periodic optimal control problem. To that end, we model the wing by root-clamped modes, loaded by strip-theory aerodynamics and by the gravitational and inertial loads of the manoeuvring aircraft. To keep the problem tractable, the modal dynamics are replaced by their quasi-static equilibrium, which enters every discretization interval as algebraic constraints. For a multi-megawatt reference aircraft, the elastic twist near the wing tips reaches $1.03^\circ$ along the optimal rigid-wing trajectory. With a twist limit of $0.6^\circ$, the optimal trajectory delivers $31\,\%$ less mean power than the rigid-wing one, mainly through a lower tether tension during reel-out. Rigid-wing trajectory optimization can thus overestimate the power that the wing limits permit. Forward simulations that retain the modal dynamics demonstrate that the quasi-static deflection and twist deviate from the dynamic ones by less than $2\,\%$ of their limits. This work shows that elastic deformation can be accounted for in the trajectory optimization and that, depending on the wing limits, it can strongly reduce the achievable power.
Source:https://arxiv.org/abs/2611.13678PreviousPriority-Aware Firm and Flexible Capacity Quantification for Large-Load InterconnectionNextDecision-Focused Learning via Neural Optimization Layers for Battery-Assisted Grid-Import Smoothing in AI Data Centers
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