A Fast Reconstruction Method for Temperature Field Based on Principal Component Analysis and Convolutional Autoencoder

Abstract

A fast reconstruction method of temperature field based on principal component analysis (PCA) and convolutional autoencoder is proposed in this paper. The two-dimensional temperature field can be quickly reconstructed by inputting the small amounts of sensor data. Principal component analysis is first used to extract key features from high-dimensional prior dataset, and the extracted results are combined with the sensor measurement points information according to the coefficient optimization method to achieve the approximate reconstruction of the temperature field. Then, the reconstruction results are inputted into the convolutional autoencoder model for iterative learning to further reduce the reconstruction error and achieve accurate reconstruction of the temperature field. The effectiveness proposed method has been verified in the boiler combustion simulation experiment, and the experimental results show that the proposed method can reconstruct the two-dimensional temperature field quickly and accurately, which is of great significance to the research of some combustion systems.