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Aiming at the problems of a long design period and imperfect surrogate modeling in the field of airfoil design optimization, a convolutional neural network framework for airfoil design and performance prediction (DPCNN) is established based on the deep learning method. The airfoil profile parameterization, physical field prediction, and performance prediction are achieved. The results show that the DPCNN framework can generate substantial perfect airfoil profiles with only three geometric parameters. It has significant advantages such as good robustness, great convergence, fast computation speed, and high prediction accuracy compared with the conventional machine learning method. When the train size is 0.1, the predicted results can be obtained within 5 ms. The prediction absolute errors of physical field of most sample points are lower than 0.002, and the relative errors of aerodynamic performance parameters are lower than 2.5%. Finally, the optimization attempt of operating parameters is completed by gradient descent method, which shows good stability and convergence. Overall, the DPCNN framework in this paper has outstanding advantages in time cost and prediction accuracy. [ABSTRACT FROM AUTHOR]

Aerodynamic design optimization of the blade profile is a critical approach to improve performance of turbomachinery. This paper aims to achieve the performance prediction with deep learning method and realize fast design optimization of a turbine blade. Two parameterization methods based on geometric relationships (PGR) and neural network (PNN) are proposed, which can generate smooth and complete blade profiles. A dual convolutional neural network (DCNN) is constructed to predict the physical fields and aerodynamic performance. The implementations of DCNN are accomplished based on the datasets generated by the two parameterization methods respectively, which are called PGR-DCNN and PNN-DCNN model. Results show that the prediction accuracy increases and then keeps stable as train size increases. The two models can offer the detailed physical field distribution within 3 ms and accurately predict the aerodynamic performance. The prediction errors of performance parameters for 99% samples in validation set are less than 0.5% with PGR-DCNN model, which are significantly better than conventional machine learning methods. Finally, based on the accurate predictive models, the gradient-based design optimization for rotor blade profile is completed in 38 s. The efficiency of the two optimal blades reaches 89.29% and 88.92% respectively, which verifies the feasibility of our method. • Two parameterization methods based on geometric relationships and neural network are proposed. • The DCNN model is constructed to reconstruct physical fields and predict performance. • High prediction accuracy and fast calculation speed are achieved by DCNN model. • The gradient descent method is adopted to conduct the optimization of the turbine blade profile. [ABSTRACT FROM AUTHOR]