###### Abstract  
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 pix2 pix 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. Pix2 pix 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 pix2 pix method as a result of deep learning. pix2 pix; image-to-image translation; airfoil; deep learning; GAN; DNN; wind turbine blade; CFD +
Footnote †: journal: _aciences_  
of the inboard rotor blade is made thicker to maximize the sectional moment of inertia of the thin-shelled airfoil structure [4]. Multi-objective optimization should thus be conducted during the design process for an airfoil since it involves several aerodynamic requirements, such as high lift, low drag, and stall characteristics [5; 6]. These aerodynamic requirements can be determined through flow field analysis using computational fluid dynamics (CFD) simulations by solving the Navier-Stokes equations for the mesh with boundary conditions. However, CFD simulations in the airfoil design process require a lot of time and expensive computation [7; 8; 9]. Recently, data-driven approaches, such as machine learning and deep learning, have received considerable attention in the field of fluid dynamics due to the powerful learning capabilities of neural networks [8; 10; 11]. After training, the neural network model can be used to obtain the prediction results for the airfoil flow field in a few seconds or even milliseconds. This provides a faster alternative to CFD simulations as an efficient function-approximation technique in high-dimensional spaces. Deep learning has been used for the prediction of airflow in several studies. [PERSON] et al. [12] proposed an approximation model based on convolution neural networks (CNNs) to predict the velocity and pressure field of new geometries under new flow conditions. Data from the Reynolds-averaged Navier-Stokes (RANS) flow solutions for flow over airfoil shapes were used to train the model. The trained model effectively detected essential features from new geometries with minimal supervision and could effectively estimate the velocity and pressure field much faster, which made it possible to study the impacts of the airfoil shape and operating conditions on the aerodynamic forces and the flow field in near-real time. [PERSON] et al. [7] used a combination of a CNN and a multilayer perceptron (MLP) network to predict the incompressible laminar steady flow field over airfoils. The CNN was employed to extract the geometrical parameters from airfoil shapes and the results were fed as input into the MLP network to obtain an approximate model to predict the flow field. The CNN could efficiently and accurately estimate the entire velocity field two to four orders of magnitude faster than the CFD solver and with a lower error rate [9]. Recently, the data-augmented generative adversarial network (GAN) model has gained attention for its rapid and accurate flow field prediction [13]. The GAN can be adapted to the task with sparse data and can learn losses by attempting to determine whether the output image is real or fake while simultaneously training a generative model to minimize this loss.