A non-parametric high-resolution prediction method for turbine blade profile loss based on deep learning

Lele Li, Weihao Zhang, Ya Li, Ruifeng Zhang, Zongwang Liu, Yufan Wang and Yumo Mu

Energy, 2024, vol. 288, issue C

Abstract: Obtaining the aerodynamic performance of the turbine blade by Computational Fluid Dynamics (CFD) methods is accurate. However, it consumes time and computational resources. This paper proposes an evaluation method based on Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) to obtain the aerodynamic performance of the turbine blade accurately and quickly. Compared with the existing data-driven modeling methods, this method innovatively introduces the Residual Network (ResNet), employs a transfer learning strategy for network design, and realizes the automatic extraction of blade profile features and non-parametric input. In processing boundary conditions, the ANN is utilized to fuse the blade profile features with the boundary conditions to realize the mapping between blade profile and aerodynamic performance under different conditions. In addition, to minimize the prediction deviation caused by the severely uneven distribution of the data set, we combined ensemble learning with transfer learning and proposed a two-step prediction strategy. The numerical simulations results show that the ResNet-ANN model established in this paper has a prediction relative error of 5 % on turbine blade aerodynamic parameters under various working conditions. The error is reduced by more than 90 % under −40°-10° incidence angle of incoming flow compared with the empirical model.

Keywords: Gas turbine; Performance prediction; Non-parametric input; Deep learning; Transfer learning (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:288:y:2024:i:c:s0360544223031134

DOI: 10.1016/j.energy.2023.129719

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