Akademik Veri Yönetim Sistemi
Wind Energy, cilt.28, sa.3, 2025 (SCI-Expanded, Scopus)
This study presents a data-driven approach to extrapolate and predict Reynolds number effects on wind turbine airfoil polars. For this purpose, a database is created using experimentally obtained aerodynamic coefficients from open literature for airfoils with thickness-to-chord ratio (t/c) values in the range of 15% to 30% in order to be more relevant to wind turbine blade design applications. All available airfoil geometries are parameterized using PARSEC methodology, and response surfaces are generated to predict (Formula presented.) and (Formula presented.) of a given airfoil operating at a given Reynolds number. These predicted values are then utilized in a power-law-based estimation methodology to obtain predictions for full polars, which are then compared with experimental data for selected airfoil test cases. Unlike previously proposed methods in the literature, the new extrapolation method does not rely on predicted trends obtained through numerical simulations. Because the response surfaces used for predictions are based on an entirely experimental database, this brings in an advantage in general in terms of better predicting (Formula presented.), (Formula presented.), and stall angle of attack levels because it is known that numerical simulations generally fail to predict the (Formula presented.), (Formula presented.), and (Formula presented.) levels accurately for wind turbine airfoils. The results show that the proposed data-driven approach and the full polar prediction methodology show better agreement with experimental results compared with those obtained using numerical simulations-based extrapolation schemes. Also, because the method consists of basic polynomial relations, it requires much less computational time compared with numerical simulations-based approaches. For airfoil types that might be underrepresented in the database (such as symmetrical airfoils), the prediction results can be erroneous, especially at very high Reynolds numbers that are not in the constructed database. In the power-law-based polar prediction methodology, utilizing a reference Reynolds number that is closer to the target value provides more accurate predictions for both lift and drag polars.