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Appl. Sci. 2020, 10, 4999 17 of 21 Method CFD solver Our study—CPU Our study—GPU 3.3. Performance Prediction Table 3. Comparison of computation costs with different methods. Physical Memory 730–1730 Mb 1975–2975 Mb 2787–3787 Mb Graphics Memory / / 1785–2385 Mb Train Time / 24 h 4–5 h Evaluation Time 3.5 h 0.24 s 0.04 s Based on the above physical field reconstruction results, the power and efficiency of the S-CO2 turbine were predicted under off-design conditions, as shown in Figure 13. The abscissa in the figure is the actual power and efficiency data calculated by numerical simulation. The ordinate is the power and efficiency data predicted by the model. The blue scattered points are the predicted sample points and the red line indicates that the prediction is completely correct at the ideal situation. The gray area indicates the distribution interval of the prediction error within 5%. The results show that basically all Appl. Sci. 2020, 10, x FOR PEER REVIEW 20 of 24 the prediction results of this model are within the distribution interval of 5%. The scattered points Appl. Sci. 2020, 10, x FOR PEER REVIEW with poor prediction results are mostly in the low efficiency area. (a) (a) 20 of 24 Figure 13. True-pre performance curve: (a) power; (b) efficiency. FigFuirgeur1e31. 3T.rTureu-ep-rpereppeerrffoorrmance currvee: :(a()ap) opwower;e(rb; )(bef)fiecffiienccieyn. cy. The detailed distribution density of power and efficiency in the range of ±5% relative error is The detailed distribution density of power and efficiency in the range of ±5% relative error is The detailed distribution density of power and efficiency in the range of ±5% relative error is shown in Figure 14. The relative error of power and efficiency are basically between −4% and 4%. The showsnhoiwnnFinguFirgeu1re4.14T.hTehererelalattiiveerrorrooffpopwowerearndanedfficeiffienciyenarceybasriecablalysibceatlwlyeebne−t4w%eeand−44%.Tahned4%. prediction of efficiency has a better effect, and the relative errors are concentrated in the ±1% range. Thepprreediictionofefffificienccyyhhaassaabbetettetrerefefeffcetc,ta,nadndthtehrelraetliavteiveerreorrsoarseacroencoentcreantetdraitnedthien±t1h%er±a1n%ger.ange. It can be proved that the model in this research has high prediction accuracy. It can be proved that the model in this research has high prediction accuracy. It can be proved that the model in this research has high prediction accuracy. (b) (b) (a) (b) (a) (b) FiguFrigeu1r4e.1T4.hTehdeidstirstibriubutitoionnddeensityofrellattiviveeererorro:r(:a)(ap)opwoewr;e(br;)(ebff)iceiffienciyency. Figure 14. The distribution density of relative error: (a) power; (b) efficiency In this study, five classic data prediction methods of XGboost, KNN, RF, SVR, and MLP were 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 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 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 efficiency prediction result. The comparison of square values shows that the prediction efficiency of our model is the best.PDF Image | Performance Prediction of a S-CO2 Turbine
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