Part-load performance prediction model for supercritical CO2 radial inflow turbines
| Autor: | David J. Mee, Sangkyoung Lee, Grant Yaganegi, Zhigiang Guan, Hal Gurgenci |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Renewable Energy
Sustainability and the Environment Internal flow business.industry Computer science 020209 energy Energy Engineering and Power Technology Thermal power station 02 engineering and technology Inflow Computational fluid dynamics Brayton cycle Turbine Automotive engineering Fuel Technology 020401 chemical engineering Nuclear Energy and Engineering 0202 electrical engineering electronic engineering information engineering Performance prediction 0204 chemical engineering business Thermal energy |
| Zdroj: | Energy Conversion and Management. 235:113964 |
| ISSN: | 0196-8904 |
| DOI: | 10.1016/j.enconman.2021.113964 |
| Popis: | There is recent focus on supercritical CO2 (s-CO2) Brayton power cycles as the next-generation thermal energy conversion choice. They are efficient, compact, economic, and environmentally friendly. They are also scalable without efficiency penalties and suitable for small and large sizes. The most critical component is reported to be the turbine. A turbine design should achieve good performance at its design operating conditions and maintain acceptable performance at off-design conditions. Off-design operating conditions are relevant because thermal power plants are increasingly required to meet varying electricity demand in continuously changing environments. This paper presents a novel one-dimensional part-load performance prediction model for s-CO2 turbines. The novelty of the present model comes from the accurate prediction of the part-load s-CO2 turbine performance with less than 10% deviation, validated against reliable three-dimensional computational fluid dynamics simulation. Furthermore, the proposed model uses fundamental fluid dynamic equations and a real gas property library and does not require calibration. A Python code based on the proposed model can generate a full list of essential performance variables and internal flow information. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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