Most icing research focuses on the high Reynolds number regime and manned aviation. 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. This study investigated the aerodynamic performance degradation on an S826 airfoil with 3D-printed ice shapes at Reynolds numbers Re = 2 × 105, 4 × 105, and 6 × 105. Three ice geometries were obtained from icing wind tunnel experiments, and an additional three geometries were generated with LEWICE. 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. The results showed that the icing performance penalty correlated to the complexity of the ice geometry. The experimental data were compared to computational fluid dynamics (CFD) simulations with the RANS solver FENSAP. Simulations were performed with two turbulence models (Spalart Allmaras and Menter’s k-ω SST). The simulation data showed good fidelity for the clean and streamlined icing cases but had limitations for complex ice shapes and stall.
With advanced control, estimation and simulation requirements in unmanned aerial systems comes the need for sophisticated aerodynamic models. This paper reviews two common means for establishing such models; numerical design tools and wind tunnel testing, by presenting strengths and potential problems, in a "lessons learned"-manner. As a case study throughout the paper, a six degrees-of-freedom aerodynamic model of the Skywalker X8 fixed-wing unmanned aerial vehicle is presented.
Atmospheric icing is a key challenge to the operational envelope of medium-sized fixed-wing UAVs. Today, several numeric icing codes exist, that all have been developed for general aviation applications. UAVs with wingspans of several meters typically operate at Reynolds numbers an order of magnitude lower than commercial and military aircraft. Therefore, the question arises to what extent the existing codes can be applied for low-Reynolds UAV applications to predict ice accretion. This paper describes an experimental campaign at the Cranfield icing wind tunnel on a RG-15 and a NREL S826 airfoil at low velocities (25-40m/s). Three meteorological icing conditions have been selected to represent the main ice typologies: rime, glaze, and mixed ice. Each case has been run at least twice in order to assess the repeatability of the experiments. Manual ice shape tracings have been taken at three spanwise locations for each icing case. The liquid water content calibration was performed according to ARP5905 using the icing blade method. The tests have initially shown significantly higher water contents than anticipated, which could be traced to dimensional differences of the blade at Cranfield, as well as low flow velocities. This systematic error was resolved by simulating the droplet collection coefficients on the off-specification blade. In addition to manual tracings, photogrammetry and a handheld laser-scanner were used to capture the ice shapes. The results indicate that manual tracings are still the most efficient method, although there is potential in exploring the alternatives further. Additionally, numerical simulations with two icing codes, LEWICE and FENSAP-ICE, were performed on a rime and a glaze case. For rime, the simulations show a good agreement with the experiment, whereas the glaze case exhibits significant differences.
Unmanned Aerial Vehicles (UAVs) have benefited from a tremendous increase in popularity over the past decade, which has inspired their application toward many novel and unique use cases. One of them is the use of UAVs in meteorological research, in particular for wind measurement. Research in this field using quadcopter UAVs has shown promising results. However, most of the results in the literature suffer from three main drawbacks. First, experiments are performed as numerical simulations or in wind tunnels. Such results are limited in their validity in real-life conditions. Second, it is almost always assumed that the drone is stationary, which limits measurements spatially. Third, no attempts at estimating vertical wind are made. Overcoming these limitations offer an opportunity to gain significant value from using UAVs for meteorological measurements. We address these shortcomings by proposing a new dynamic model-based approach, that relies on the assumption that thrust can be measured or estimated, while drag can be related to air speed. Moreover, the proposed method is tested on empirical data gathered on a DJI Phantom 4 drone. During hovering, our method leads to precision and accuracy comparable to existing methods that use tilt to estimate the wind. At the same time, the method is able to estimate wind while the drone is moving. This paves the way for new uses of UAVs, such as the measurement of shear wind profiles, knowledge of which is relevant in Atmospheric Boundary Layer (ABL) meteorology. Additionally, since a commercial off-the-shelf drone is used, the methodology can be replicated by others without any need for custom hardware development or modifications.
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