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Appl. Sci. 2020, 10, 4999 18 of 21 In this study, five classic data prediction methods of XGboost, KNN, RF, SVR, and MLP were compared with this model, as shown in Table 4 and Figure 15. The training and verification set of the above models are consistent. The evaluation index is the R2, MAE, and RMSE of the power and efficiency prediction result. The comparison of square values shows that the prediction efficiency of our model is the best. 1. The design and optimization of a 60,000 rpm S-CO2 turbine were completed based on our 2. At stage 1, the field reconstruction was conducted on 1000 off-design cases with varying design At stage 1, the field reconstruction was conducted on 1000 off-design cases with varying design variables. The physical fields were plausibly predicted and all key typical phenomena in turbine variables. The physical fields were plausibly predicted and all key typical phenomena in turbine were captured. The average relative error of the field is less than 1.5%, while the maximum were captured. The average relative error of the field is less than 1.5%, while the maximum relative error is less than 15%. 2. 3. Based on the reconstructed physical field, the off-design performance of the S-CO2 turbine was Based on the reconstructed physical field, the off-design performance of the S-CO2 turbine was predicted accurately at stage 2. The relative error of predicted power and efficiency are between predicted accurately at stage 2. The relative error of predicted power and efficiency are between −5% and +5%. Moreover, the relative error of efficiency is concentrated in the ±1% range. −5% and +5%. Moreover, the relative error of efficiency is concentrated in the ±1% range. 3. 4. Compared with other five classic data prediction methods, XGboost, KNN, RF, SVR, and MLP, 4. Compared with other five classic data prediction methods, XGboost, KNN, RF, SVR, and MLP, 5. In addition, once the deep model is well-trained, the calculation with GPU-accelerated can quickly 5. In addition, once the deep model is well-trained, the calculation with GPU-accelerated can Model XGboost KNN 0.7020 0.0133 0.0288 RF 0.7446 0.0148 0.0267 SVR 0.8447 0.0076 0.0208 MLP 0.9072 0.0066 0.0161 Our Study 0.9851 0.0027 0.0054 Table 4. Comparison with five classic data prediction methods. R2 MAE 0.6784 0.0184 Appl. Sci. 2020, 10, x FOR PEER REVIEW RMSE 0.0297 21 of 24 In this research, we presented a two-stage deep convolutional neural network to predict the off- off-design performance of a S-CO2 turbine based on field reconstruction. The concrete results are listed design performance of a S-CO2 turbine based on field reconstruction. The concrete results are listed as following: 4. Conclusions 4. Conclusions (a) Figure 15. Prediction results under difffferent methods:: (a) power; (b) effifficiency.. In this research, we presented a two-stage deep convolutional neural network to predict the as following: 1. The design and optimization of a 60,000 rpm S-CO2 turbine were completed based on our previous research. The output power of the designed turbine is 1019 kW and the total static efficiency previous research. The output power of the designed turbine is 1019 kW and the total static is 89.44%. efficiency is 89.44%. relative error is less than 15%. the off-design power and efficiency prediction in this method clearly outperforms classical the off-design power and efficiency prediction in this method clearly outperforms classical methods and comparable to a state-of-the-art model. methods and comparable to a state-of-the-art model. predict the physical fields on the blade surface and turbine performance. quickly predict the physical fields on the blade surface and turbine performance. Compared to the conventional off-design analysis methods, our method can provide more Compared to the conventional off-design analysis methods, our method can provide more Author Contributions: Conceptualization, D.S. and Y.X.; investigation, D.S. and L.S.; methodology, D.S. and L.S.; resources, Y.X.; software, D.S. and Y.X.; supervision, Y.X.; validation Y.X.; writing—original draft mechanism explanations for designers due to accurate prediction of physical fields. Our method relies mechanism explanations for designers due to accurate prediction of physical fields. Our method relies on less human intervention and has the advantages of being effective, universal, flexible, and easy to implement, showing a good promise for real-time control and design optimization of turbines. (b)PDF Image | Performance Prediction of a S-CO2 Turbine
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