Off-Design Performance Prediction of a S-CO2 Turbine Based on Field Reconstruction Using Deep-Learning Approach

Abstract

The reliable design of the supercritical carbon dioxide (S-CO2) turbine is the core of the advanced S-CO2 power generation technology. However, the traditional computational fluid dynamics (CFD) method is usually applied in the S-CO2 turbine design-optimization, which is a high computational cost, high memory requirement, and long time-consuming solver. In this research, a flexible end-to-end deep learning approach is presented for the off-design performance prediction of the S-CO2 turbine based on physical fields reconstruction. Our approach consists of three steps: firstly, an optimal design of a 60,000 rpm S-CO2 turbine is established. Secondly, five design variables for off-design analysis are selected to reconstruct the temperature and pressure fields on the blade surface through a deconvolutional neural network. Finally, the power and efficiency of the turbine is predicted by a convolutional neural network according to reconstruction fields. The results show that the prediction approach not only outperforms five classical machine learning models but also focused on the physical mechanism of turbine design. In addition, once the deep model is well-trained, the calculation with graphics processing unit (GPU)-accelerated can quickly predict the physical fields and performance. This prediction approach requires less human intervention and has the advantages of being universal, flexible, and easy to implement.