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We present a framework for the robust optimization of the heat flux distribution for an anti-ice electro-thermal ice protection system (AI-ETIPS) and iced airfoil performance analysis under uncertain conditions. The considered uncertainty regards a lack of knowledge concerning the characteristics of the cloud i.e. the liquid water content and the median volume diameter of water droplets, and the accuracy of measuring devices i.e., the static temperature probe, uncertain parameters are modeled as uniform random variables. A forward uncertainty propagation analysis is carried out using a Monte Carlo approach. The optimization framework relies on a gradient-free algorithm (Mesh Adaptive Direct Search) and three different problem formulations are considered in this work. Two bi-objective deterministic optimizations aim to minimize power consumption and either minimize ice formations or the iced airfoil drag coefficient.

Wind turbines in cold climates are likely to suffer from icing events, deteriorating the aerodynamic performances of the blades and decreasing their power output. Continuous ice accretion causes an increase in the ice mass and, consequently, in the centrifugal force to which the ice shape is subjected. This can result in the shedding of chunks of ice, which can jeopardize the aeroelastic properties of the blade and, most importantly, the safety of the surrounding people and of the wind turbine structure itself. In this work, ice shedding analysis is performed on a quasi-3D, multi-step ice geometry accreted on the NREL 5MW reference wind turbine. A preliminary investigation is performed by including the presence of an ice protection system to decrease the adhesion surface of the ice on the blade. A reference test case with a simple geometry is used as verification for the correct implementation of the procedure.