• Journal of the Korean Society of Visualization
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• v.20 no.3
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• pp.109-116
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• 2022

The optimized design of airfoils is essential to increase the performance and efficiency of wind turbines. The aerodynamic characteristics of airfoils near the stall show large deviation from experiments and numerical simulations. Hence, it is needed to perform repetitive analysis of various shapes near the stall. To overcome this, the artificial intelligence is used and combined with numerical simulations. In this study, three types of airfoils are chosen, which are S809, S822 and SD7062 used in wind turbines. A convolutional neural network model is proposed in the combination of VGG16 and U-Net. Learning data are constructed by extracting pressure fields and aerodynamic characteristics through numerical analysis of 2D shape. Based on these data, the pressure field and lift coefficient of untrained airfoils are predicted. As a result, even in untrained airfoils, the pressure field is accurately predicted with an error of within 0.04%.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.516-520
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• 2009

This study describes aerodynamic characteristics for the HAWT (Horizontal Axis Wind Turbine) rotor blade using general CFD(Computational Fluid Dynamics) code. The boundary conditions for analysis are validated with the experimental result by the NREL (National Renewable Energy Laboratory)/NASA Ames wind tunnel test for S809 airfoil. In the case of wind turbine rotor blade, complex phenomena are appeared such as flow separation and re-attachment. Those are handled by using a commercial flow analysis tool. The 2-equation k-$\omega$ SST turbulence model and transition model appear to be well suited for the prediction. The 3-dimensional phenomena in the HAWT rotor blade is simulated by a commercial 3-D aerodynamic analysis tool. Tip vortex geometry and Radial direction flows along the blade are checked by the analysis.