Phase-parameterised gaussian process for predicting UAV aerodynamic loads in operational turbine wakes

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ABSTRACT Operational inspection of wind turbines using uncrewed aerial vehicles (UAVs) is an attractive method for reducing offshore operation and maintenance costs, but robust flight in the turbine near-wake remains challenging due to the presence of strong unsteady disturbances that can degrade stability and quality of the inspection. This paper presents a control-oriented modelling framework for predicting UAV aerodynamic loads in operational turbine wakes with faster-than-real-time capability. A free-vortex-wake representation is used to capture the dominant near-wake physics, and a phase-parameterised Gaussian Process Regression (GPR) surrogate model is employed in a wake-aligned coordinate system to exploit the periodic structure of the induced velocity field. The surrogate model is coupled to a nonlinear, physics-informed rotorcraft flight dynamics model to predict wake-induced force perturbations on a quadrotor UAV. The approach is validated against data obtained from an experimental campaign with a scaled three-bladed turbine and a 250 mm-class quadrotor mounted on a load cell. The relevant data and experimental settings are reported as a reference for future research. Across lateral and vertical traverses in the near wake, the predicted streamwise and vertical force components agree with measurements in both magnitude and trend. A frequency-domain error attribution analysis shows the turbine–UAV interaction component, and the results indicate the measured spectral peaks at the turbine rotational and blade-passing harmonics are consistent with the simulations. The present formulation is primarily intended for wake conditions with coherent low-order periodic content, and its prediction fidelity may degrade under strongly turbulent or weakly phase-coherent inflow. Within this scope, the results demonstrate that phase-parameterised GPR surrogates can provide accurate, data-efficient, uncertainty-aware wake disturbance predictions suitable for real-time simulation platforms supporting UAV autonomy and control development for operational turbine inspection.

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