Hybrid Model for Inflow Conditions Inference on Airfoils Under Uncertainty

Abstract

Estimation of inflow conditions, such as wind speed and angle of attack, is vital for assessing aerodynamic performance of a lifting profile. This task is particularly challenging in the field due to the inherent stochasticity of the inflow variables. In practice, the field installation of a measurement system exacerbates the measurement uncertainty. Here, we present a hybrid model to infer the inflow conditions on a wind turbine blade along with a process to quantify the involved uncertainty. The model combines potential flow theory and conformal mapping with pressure measurements from a novel monitoring system, which eliminates the need for external reference pressure measurements. Stagnation point location and wind speed are formulated as outputs of an optimization problem, in which pressure differences along the surface of an airfoil are connected to the potential flow solution through the Bernoulli equation. The proposed scheme is experimentally validated. The hybrid model offers a practical and robust solution for inflow condition estimation, suitable for field deployment on wind turbine or aircraft. The uncertainty quantification process provides valuable insights for improving monitoring system design and quantifying the accuracy of the predictive scheme before actual field installation.