The research on the supercritical carbon dioxide (S-CO2) Brayton cycle has gradually become a hot spot in recent years. The off-design performance of turbine is an important reference for analyzing the variable operating conditions of the cycle. With the development of deep learning technology, the research of surrogate models based on neural network has received extensive attention. In order to improve the inefficiency in traditional off-design simulation analyses, this research establishes a data-driven deep learning off-design aerodynamic prediction model for a S-CO2 centrifugal turbine, which is based on a deep convolutional neural network. The model can not only reconstruct the pressure and temperature field for multiple profile and operating conditions, but also predict the efficiency and power of each operating condition.In the first section, the general research framework of the off-design aerodynamic prediction convolutional neural network (OAP-CNN) is introduced. The network can rapidly and adaptively provide dynamic aerodynamic performance prediction results for multiple blade profiles and operating conditions. Compared with the traditional surrogate models, OAP-CNN improves the accuracy and the mechanism based on the field reconstruction results can be analyzed for the generated aerodynamic performance.In the second section, the parameterization of the S-CO2 centrifugal turbine and the sampling of the training data are introduced. Meanwhile, the training method of OAP-CNN is presented in detail.In the third section, aerodynamic predictions of multiple profiles and off-design conditions are conducted for the S-CO2 centrifugal turbine. The trans-critical region in the pressure distribution is analyzed. In terms of efficiency and power prediction, the accuracy of the traditional Gaussian Process Regression (GPR) and Artificial Neural Network (ANN) are compared. This prediction network does not need to perform a CFD calculation to quickly and accurately obtain the aerodynamic performance of different blade profiles and operating conditions.In summary, this research provides a precise and fast aerodynamic performance prediction model in the analyses of off-design conditions for S-CO2 turbomachinery and Brayton cycle.

Aerodynamic Prediction on the Off-Design Performance of a S-Co2 Turbine Based on Deep Learning

Paper Proceedings: https://asmedigitalcollection.asme.org/GT/GT2021/volume/84898

Turbo Expo 2021

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