Effective access to obtain the complex flow fields around an airfoil is crucial in improving the quality of supercritical wings. In this study, a systematic method based on generative deep learning is developed to extract features for depicting the flow fields and predict the steady flow fields around supercritical airfoils. To begin with, a variational autoencoder (VAE) network is designed to extract representative features of the flow fields. Specifically, the principal component analysis technique is adopted to realize feature reduction, aiming to obtain the optimal dimension of features in VAE. Afterward, the extracted features are incorporated into the dataset, followed by the mapping from the airfoil shapes to features via a multilayer perception (MLP) model. Eventually, a composite network is adopted to connect the MLP and the decoder of VAE for predicting the flow fields given the airfoil. The proposed VAE network achieves compression of high-dimensional flow field data into ten representative features. The statistical results indicate the accurate and generalized performance of the proposed method in reconstructing and predicting flow fields around a supercritical airfoil. Especially, our method obtains accurate prediction results over the shock area, indicating its superiority in conducting turbulent flow under high Reynolds number.

Data-driven turbulence modeling has been considered an effective method for improving the prediction accuracy of Reynolds-averaged Navier–Stokes equations. Related studies aimed to solve the discrepancy of traditional turbulence modeling by acquiring specific patterns from high-fidelity data through machine learning methods, such as artificial neural networks. The present study focuses on the unsmoothness and prediction error problems from the aspect of feature selection and processing. The selection criteria for the input features are summarized, and an effective input set is constructed. The effect of the computation grid on the smoothness is studied. A modified feature decomposition method for the spatial orientation feature of the Reynolds stress is proposed. The improved machine learning framework is then applied to the periodic hill database with notably varying geometries. The results of the modified method show significant enhancement in the prediction accuracy and smoothness, including the shape and size of separation areas and the friction and pressure distributions on the wall, which confirms the validity of the approach.

Field inversion and machine learning are implemented in this study to describe three-dimensional (3D) separation flow around an axisymmetric hill and augment the Spalart–Allmaras (SA) model. The discrete adjoint method is used to solve the field inversion problem, and an artificial neural network is used as the machine learning model. A validation process for field inversion is proposed to adjust the hyperparameters and obtain a physically acceptable solution. The field inversion result shows that the non-equilibrium turbulence effects in the boundary layer upstream of the mean separation line and in the separating shear layer dominate the flow structure in the 3D separating flow, which agrees with prior physical knowledge. However, the effect of turbulence anisotropy on the mean flow appears to be limited. Two approaches are proposed and implemented in the machine learning stage to overcome the problem of sample imbalance while reducing the computational cost during training. The results are all satisfactory, which proves the effectiveness of the proposed approaches.

Numerical investigations were conducted to predict the performance of a transonic axial compressor rotor with circumferential groove casing treatment. The Notre Dame Transonic Axial Compressor (ND-TAC) was simulated at Tsinghua University with an in-house computational fluid dynamics (CFD) code (NSAWET) for this work. Experimental data from the ND-TAC were used to define the geometry, boundary conditions, and data sampling method for the numerical simulation. These efforts, combined with several unique simulation approaches, such as nonmatched grid boundary technology to treat the periodic boundaries and interfaces between groove grids and the passage grid, resulted in good agreement between the numerical and experimental results for overall compressor performance and radial profiles of exit total pressure. Efforts were made to study blade level flow mechanisms to determine how the casing treatment impacts the compressor's stall margin and performance. The flow structures in the passage, the tip gap, and the grooves as well as their mutual interactions were plotted and analyzed. The flow and momentum transport across the tip gap in the smooth wall and the casing treatment configurations were quantitatively compared.

Aerodynamic prediction of glaze-ice accretion on airfoils and wings is studied using the Reynolds-averaged Navier–Stokes method. Two separation fixed turbulence models are developed by considering ...