An adaptive performance map generation method through shape feature fusion for the gas turbine compressor

Accurate generation of the compressor performance map (CPM) is necessary for building high-precision physical models of gas turbines. The applicability and generalization ability of the current CPM generation method are relatively poor under off-design operating conditions, and the extrapolability of the generated curves is debatable. This paper proposes an adaptive performance map generation method through shape feature fusion (SFF-AG) for the gas turbine compressor. A framework for the generation and optimization of the CPM curve database is developed. The nonlinear features of the CPM curves are extracted and fused by the variational autoencoder. Different CPM curves conforming to the physical shape are reconstructed to build a high-quality CPM curve database. Correction factors are introduced to adaptively correct the CPM curves by genetic algorithm. The proposed method is verified on an aircraft engine and an in-service industrial gas turbine and compared with other methods. The results show that the method can generate CPM with higher accuracy, better generalization ability and extrapolability, and better capture the nonlinear behavior of different types of gas turbines. With the continuous updating and improvement of the initial CPM curve database with richer curve shape features, the CPM curve database constructed by SFF-AG will be more complete.