Field reconstruction and off-design performance prediction of turbomachinery in energy systems based on deep learning techniques

Abstract

Obtaining the off-design characteristics of core components such as turbines and compressors is the basis of off-design analysis for energy systems. However, the characteristics are difficult to accurately acquire in the initial design stage of turbomachinery. Based on deep learning techniques, an accurate and rapid field reconstruction and off-design aerodynamic performance prediction method is proposed. First, a Generative Adversarial Network with the added Bezier layer is employed to establish a database of turbine blade profiles. Then, a Dual Convolutional Neural Network (Dual-CNN) is established to reconstruct the pressure and temperature fields as well as predict the off-design performances of different profiles and working conditions. Based on the above two kinds of neural networks, a turbine in a solar-based supercritical carbon dioxide Brayton cycle is taken as an example. The field reconstruction and off-design performance prediction are conducted on the basis of the established rotor blade profile database. The accuracy of field reconstruction is guaranteed. The off-design performance prediction of the established Dual-CNN indicates that the example blade profile is suitable for operation with larger mass flow rate. Compared with the traditional method, Dual-CNN can reduce the off-design analysis time of one blade geometry from 38.4 h to 7.68s.