Comparison of Proper Orthogonal Decomposition and Auto-Encoder as Reduced-Order Models for Reconstruction and Prediction of Turbine Blade Temperature Field With Sparse Data

Abstract Accurately reconstructing and predicting the global temperature field of turbine blades is of significant importance in the field of aero-engines. The complexities in geometries and operation conditions of the blades further complicate these problems, because temperatures can only be acquired from sparse and noisy measurements. Proper orthogonal decomposition (POD) and deep neural network auto-encoder (AE) are two typical reduced-order models to reconstruct the global temperature fields from sparse data points, and they are further combined with long short-term memory (LSTM) networks for prediction. In contrast with the linear modes of POD, the nonlinear features of AE may lead to advantages in reconstructing and predicting of temperature fields. A systematic comparison between the two methods is seldom studied in existing research, particularly regarding their noise resistance and time-series prediction capabilities. Therefore, a detailed study is conducted in this paper. The two-dimensional cross section of Mark II blades is used as an example; this work compares the performance of POD–LSTM and AE–LSTM in reconstructing and predicting the global temperature field of turbine blades based on sparse and noisy measurement data under transient operating conditions. The results indicate that both reduced-order prediction models achieved low mean absolute percentage errors (MAPEs) and high computational efficiency for reconstruction and prediction. With 12 sparse data points, the reconstruction error of two methods is comparable. Compared to the POD method, reduction coefficients of the AE method are more robust and have a uniform energy distribution, so AE exhibits superior noise resistance and time-series prediction capabilities.