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Traditional computational fluid dynamics (CFD) methods are usually used to obtain information about the flow field over an airfoil by solving the Navier–Stokes equations for the mesh with boundary conditions. These methods are usually costly and time-consuming. In this study, the pix2pix method, which utilizes conditional generative adversarial networks (cGANs) for image-to-image translation, and a deep neural network (DNN) method were used to predict the airfoil flow field and aerodynamic performance for a wind turbine blade with various shapes, Reynolds numbers, and angles of attack. Pix2pix is a universal solution to the image-to-image translation problem that utilizes cGANs. It was successfully implemented to predict the airfoil flow field using fully implicit high-resolution scheme-based compressible CFD codes with genetic algorithms. The results showed that the vortical flow fields of the thick airfoils could be predicted well using the pix2pix method as a result of deep learning.

The performance and efficiency of a centrifugal compressor are usually affected by the highly complex 3-dimensional flow structures which develop in the flow field of the compressor. Several experiments and research using numerical analysis have been reported, however, there are still many unknown physical phenomena that need to be studied, in order to optimize the design and improve the efficiency of turbomachines, especially those installed on hydrogen-powered fuel cell electric vehicles (FCEVs). In this study, the 3-dimensional vortex structures were analyzed using the critical-point theory and the probabilistic definitions, for an air supply device mounted on the commercial hydrogen FCEVs. The behavior of the complex 3-dimensional vortex structures at the design flow rate and low flow rate were elucidated. A tip leakage vortex was observed to develop at the leading edge of the main blade at all flow rates, which caused interference to the splitter blade. At 60% of the design flow rate, a vortex breakdown occurred at the tip leakage vortex near the leading edge of the main blade, and a reverse flow at 50% chord length of the main blade’s suction surface. The boundary layer which developed at the leading edge of the main blade’s suction surface at all flow rates led to the creation of a hub separation vortex by interfering with the boundary layer developed at the hub surface as a result of the centrifugal force. In addition, the boundary layer developed at the hub and shroud surface created a horseshoe vortex as it moved downstream and interfered with the leading edge of the main blade and splitter blade. It was confirmed that the behavior of the tip leakage, hub separation, and horseshoe vortex structures determined the aerodynamic performance of the centrifugal compressor. The average pressure difference improved by 1.47% of the entire flow rate after optimizing the compressor design.

Thick airfoils are conventionally adopted in the blade root region of a wind turbine to ensure structural safety under extreme conditions, despite the resulting power loss. To prevent this loss, a passive flow control device known as a vortex generator (VG) is installed at the starting point of the stall to control the flow field near the wall of the suction surface. In this study, we used computational fluid dynamics (CFD) to investigate the aerodynamic characteristics induced as a result of the shape and layout of the VG on a multi-MW wind turbine blade. The separated and vortical flow behavior on the suction surface of the wind turbine blade equipped with VGs was captured by the Reynolds-averaged Navier–Stokes (RANS) steady-flow simulation. The parametric sensitivity study of the VG shape parameters such as the chord-wise length, height, and interval of the fair of VGs was conducted using thick DU airfoil on the blade inboard area. Based on these results, the response surface method (RSM) was used to investigate the influence of the design parameters of the VG. Based on the CFD results, the VG design parameters were selected by considering the lift coefficient and vorticity above the trailing edge. The maximum vorticity from the trailing edge of the selected VG and the lift coefficient were 55.7% and 0.42% higher, respectively, than the average. The selected VG design and layout were adopted for a multi-MW wind turbine and reduced stall occurrence in the blade root area, as predicted by the simulation results. The VG improved the aerodynamic performance of the multi-MW wind turbine by 2.8% at the rated wind speed.

The performance of a wind turbine generator (WTG) is highly dependent on the interaction of a rotor blade with complex fluid behaviors, especially the induced vortex structure. In this paper, vortical flows around a blade were first investigated by the unsteady Reynolds averaged Navier–Stokes (RANS) simulation with shear stress transport (SST) turbulence model. It showed that the vortical flows were strongly formed at the blade tip due to the 3D behavior of the boundary layers dominated by pressure gradient. The strong secondary flow was also formed at the near hub due to the Coriolis force and the centrifugal force. At the interacting region of the rotating blade with the tower, the power production was reduced by 22.1% due to the high-pressure fluctuation of the 3P frequency. Based on the close investigation, methods for enhancing the power performance of a WTG were explored, which included the optimization of winglet and ogee design for the blade tip and optimal layout of the nacelle anemometer. The optimized winglet achieved the increase of aerodynamic performance with 0.54%, and the optimal location of the nacelle anemometer was found with a low-turbulence intensity level of 0.003 normalized by the rotor tip speed. The results showed that the traditional anemometer needs to consider the intrinsic flow angle of 11.43° to avoid the loss of aerodynamic performance caused from yaw error.