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Appl. Sci. 2020, 10, 4999 3 of 21 CFD solution method. It mainly solves NS equations on computational grids with corresponding boundary conditions. Although this method is accurate, the time and cost of calculation are very high. The performance prediction of related components shows high prediction efficiency and accuracy. However, it cannot capture the details of heat and mass transfer process in turbine. However, in solutions to similar problems, deep learning can overcome the above shortcomings. Therefore, in order to improve the accuracy and efficiency of performance prediction while preserving the physical field information, a performance prediction method of S-CO2 turbines based on CNN is proposed. Our contributions are as follows: 1. The performance of field reconstruction for an end-to-end deep learning method is explored in this research. The most existing machine learning methods only focus on one target variable in engineering design and optimization tasks. The fields predicted by our method can provide more flow mechanism explanations and help designers understand the physical process. 2. The data-based proxy model is established for a physical system. Traditional methods lack accuracy to some extent and require manual intervention. Based on the existing scientific database, this method does not need to rely on human intervention and has the advantages of being universal, flexible, and easy to implement, showing a good promise for real-time control and design optimization of turbines. 3. The method proposed in this research is effective and accurate. The off-design power and efficiency prediction in this method is able to reach performance comparable to a state-of-the-art model and clearly outperforms classical methods. In addition, once the deep model is well-trained, the calculation with GPU-accelerated can quickly predict the physical fields on the blade surface and turbine performance. This flexible and adaptive tool can not only reduce the design cycle of turbine components, but also help to grasp the actual operating conditions in real time, which can be applied to adjust and control the system in time. The rest of this paper is organized as below: Section 2 introduces the overall architecture of this research, the theory and method of CFD analysis and deep convolutional neural network; Section 3 is the results and discussion, including CFD off-design pre-analysis, flow field reconstruction, and performance prediction. Section 4 draws conclusions. 2. Theory and Method 2.1. Overall Architecture In this research, the end-to-end reconstruction deep convolutional neural network implemented by deep learning framework Pytorch [22] was utilized to reconstruct the expand process in the S-CO2 turbine based on main design parameters and then predict the aerodynamic performance of S-CO2 turbine from reconstructed results. As illustrated in Figure 1, the proposed end-to-end framework includes three stages. The stage 0 was applied to obtain real field structures and performance of the designed S-CO2 turbine from numerical results in the off-design analysis. In the next two stages, a deep convolutional neural network was employed to reconstruct interested physical fields and predict turbine performance based on physical fields. At stage 1, the interested physical fields were reconstructed with design variables including geometric variables and environmental condition variables as input. Subsequently, the performance of the S-CO2 turbine was predicted at stage 2. It should be noted that the input of performance prediction model can be the reconstructed fields from stage 1 or the real fields from off-design analysis.PDF Image | Performance Prediction of a S-CO2 Turbine
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