The largest blockage ratio occurring between the model and the flow cross-section was σ max = 5.1% at the highest angle of attack α = 18°. In order to change the angle of attack, the entire set-up, including the force balance, was mounted on a turntable with a rotational accuracy of ☐.25°. Glaze and mixed ice shapes can exhibit complex ice horn features, whereas rime typically has more streamwise ice characteristics. It is characterized by a balanced ratio between instantaneous freezing and surface freezing. Mixed icing is an ice type that is formed in the temperature regime between rime and glaze. Glaze typically appears as transparent ice with a smooth surface. This film will flow downstream (called runback) where it gradually freezes or evaporates. Most droplets form a liquid water film on the surface of the airfoil. It is dominated by a low mass fraction of particles that freeze on impact. Glaze is an ice typology that forms at temperatures close to the freezing point. Due to entrapped air between the frozen droplets, rime appears white in color and displays a rugged, rough surface. At very low temperatures, all droplets freeze on impact and form rime ice.
Typically, three icing typologies can be identified, which are mainly characterized by the temperature during which the icing process occurs. Icing cases are generally defined by the following parameters: free-stream velocity v ∞, duration of icing t icing, airfoil chord length c, angle of attack α, liquid water content LWC, median volume diameter MVD, and ambient temperature T ∞. Additionally, none of these datasets share the coordinates of the ice geometries or the tabularized data of lift and drag.
The existing datasets in the literature lack well-defined experimental ice geometries, have no or limited performance data, offer only one data point for lift and drag for each icing case, or are performed at low or high Reynolds numbers. The table reveals that there is a gap when it comes to datasets that can be used for the validation of predicting aerodynamic icing penalties at low Reynolds numbers. Table 1 gives an overview of available data in the fields of wind energy and UAVs. In the open literature, few experimental studies exist that are suitable for the validation of numerical tools. This includes typical validation data, such as ice shapes from experimental icing wind tunnel (IWT) tests and aerodynamic performance experiments of iced airfoils. One aspect of this question is that there is a lack of data that can be used to validate numerical simulation tools for icing at low Reynolds numbers. The simulation data showed good fidelity for the clean and streamlined icing cases but had limitations for complex ice shapes and stall. Simulations were performed with two turbulence models (Spalart Allmaras and Menter’s k-ω SST).
The experimental data were compared to computational fluid dynamics (CFD) simulations with the RANS solver FENSAP. The results showed that the icing performance penalty correlated to the complexity of the ice geometry. Experimental measurements of lift, drag, and pressure on the clean and iced airfoils have been conducted in the low-speed wind tunnel at the Norwegian University of Science and Technology. Three ice geometries were obtained from icing wind tunnel experiments, and an additional three geometries were generated with LEWICE. This study investigated the aerodynamic performance degradation on an S826 airfoil with 3D-printed ice shapes at Reynolds numbers Re = 2 × 10 5, 4 × 10 5, and 6 × 10 5. Information on icing at low Reynolds numbers, as it is encountered by wind turbines and unmanned aerial vehicles, is less available, and few experimental datasets exist that can be used for validation of numerical tools. Most icing research focuses on the high Reynolds number regime and manned aviation.