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Appl. Sci. 2020, 10, 4999 19 of 21 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. 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 preparation, D.S. and L.S.; writing—review and editing, D.S. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest. References 1. Gil, L.; Lacarra, S.O.; Embid, J.M.; Gallardo, M.A.; Blanco, S.T.; Artal, M.; Velasco, I. Experimental setup to measure critical properties of pure and binary mixtures and their densities at different pressures and temperatures: Determination of the precision and uncertainty in the results. J. Supercrit. Fluid 2008, 44, 123–138. [CrossRef] 2. Crespi, F.; Gavagnin, G.; Sanchez, D.; Martinez, G.S. Supercritical carbon dioxide cycles for power generation: A review. Appl. Energy 2017, 195, 152–183. [CrossRef] 3. Xie, Y.H.; Wang, Y.Q.; Zhang, D.; Shi, D.B. Review on Research of Supercritical Carbon Dioxide Brayton Cycle and Turbomachinery. Proc. Chin. Soc. Electr. Eng. 2018, 38, 7276–7286. 4. Li, M.J.; Zhu, H.H.; Guo, J.Q.; Wang, K.; Tao, W.-Q. The development technology and applications of supercritical CO2, power cycle in nuclear energy, solar energy and other energy industries. Appl. Therm. Eng. 2017, 126, 255–275. [CrossRef] 5. Manente, G.; Fortun, F.M. Supercritical CO2 power cycles for waste heat recovery: A systematic comparison between traditional and novel layouts with dual expansion. Energy Convers. Manag. 2019, 197, 111777. [CrossRef] 6. Sun, L.; Wang, Y.; Wang, D.; Xie, Y. Parametrized Analysis and Multi-Objective Optimization of Supercritical CO2(S-CO2) Power Cycles Coupled with Parabolic Trough Collectors. Appl. Sci. 2020, 10, 3123. [CrossRef] 7. Kim, D.Y.; Kim, Y.T. Preliminary design and performance analysis of a radial inflow turbine for ocean thermal energy conversion. Renew. Energy 2017, 106, 255–263. [CrossRef] 8. Wright, S.; Radel, R.; Vernon, M.; Pickard, P. Operation and Analysis of a Supercritical CO2 Brayton Cycle; Sandia National Laboratories: Livermore, CA, USA, 2010. 9. Cho, J.; Choi, M.; Baik, Y.J.; Lee, G.; Ra, H.-S.; Kim, B.; Kim, M. Development of the turbomachinery for the supercritical carbon dioxide power cycle. Int. J. Energy Res. 2016, 40, 587–599. [CrossRef] 10. Zhou, A.; Song, J.; Li, X.; Ren, X.; Gu, C. Aerodynamic design and numerical analysis of a radial inflow turbine for the supercritical carbon dioxide Brayton cycle. Appl. Therm. Eng. 2018, 132, 245–255. [CrossRef] 11. Han, W.; Zhang, Y.; Li, H.; Yao, M.; Wang, Y.; Feng, Z.; Zhou, N.; Dan, G. Aerodynamic design of the high pressure and low pressure axial turbines for the improved coal-fired recompression SCO2 reheated Brayton cycle. Energy 2019, 179, 442–453. [CrossRef] 12. Luo, D.; Liu, Y.; Sun, X.; Huang, D. The design and analysis of supercritical carbon dioxide centrifugal turbine. Appl. Therm. Eng. 2017, 127, 527–535. [CrossRef] 13. Yu, Y.; Chen, L.; Sun, F.; Wu, C. Neural-network based analysis and prediction of a compressor’s characteristic performance map. Appl. Energy 2007, 84, 48–55. [CrossRef] 14. Rossi, M.; Renzi, M. A general methodology for performance prediction of Pumps-as-Turbines using Artificial Neural Networks. Renew. Energy 2018, 128, 265–274. [CrossRef] 15. Palagi, L.; Sciubba, E.; Tocci, L. A neural network approach to the combined multi-objective optimization of the thermodynamic cycle and the radial inflow turbine for Organic Rankine cycle applications. Appl. Energy 2019, 237, 210–226. [CrossRef] 16. Sarafraz, M.M.; Safaei, M.R.; Goodarzi, M.; Arjomandi, M. Experimental investigation and performance optimisation of a catalytic reforming micro-reactor using response surface methodology. Energy Convers. Manag. 2019, 199, 111983. [CrossRef]PDF Image | Performance Prediction of a S-CO2 Turbine
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