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Development of an empirical model of turbine efficiency using the Taylor expansion and regression analysis

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  • Fang, Xiande
  • Xu, Yu

Abstract

The empirical model of turbine efficiency is necessary for the control- and/or diagnosis-oriented simulation and useful for the simulation and analysis of dynamic performances of the turbine equipment and systems, such as air cycle refrigeration systems, power plants, turbine engines, and turbochargers. Existing empirical models of turbine efficiency are insufficient because there is no suitable form available for air cycle refrigeration turbines. This work performs a critical review of empirical models (called mean value models in some literature) of turbine efficiency and develops an empirical model in the desired form for air cycle refrigeration, the dominant cooling approach in aircraft environmental control systems. The Taylor series and regression analysis are used to build the model, with the Taylor series being used to expand functions with the polytropic exponent and the regression analysis to finalize the model. The measured data of a turbocharger turbine and two air cycle refrigeration turbines are used for the regression analysis. The proposed model is compact and able to present the turbine efficiency map. Its predictions agree with the measured data very well, with the corrected coefficient of determination Rc2≥0.96 and the mean absolute percentage deviation=1.19% for the three turbines.

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  • Fang, Xiande & Xu, Yu, 2011. "Development of an empirical model of turbine efficiency using the Taylor expansion and regression analysis," Energy, Elsevier, vol. 36(5), pages 2937-2942.
    • Handle: RePEc:eee:energy:v:36:y:2011:i:5:p:2937-2942
    DOI: 10.1016/j.energy.2011.02.036
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    1. Fast, M. & Assadi, M. & De, S., 2009. "Development and multi-utility of an ANN model for an industrial gas turbine," Applied Energy, Elsevier, vol. 86(1), pages 9-17, January.
    2. Poullikkas, Andreas, 2005. "An overview of current and future sustainable gas turbine technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 9(5), pages 409-443, October.
    3. Ameri, Mohammad & Behbahaninia, Ali & Tanha, Amir Abbas, 2010. "Thermodynamic analysis of a tri-generation system based on micro-gas turbine with a steam ejector refrigeration system," Energy, Elsevier, vol. 35(5), pages 2203-2209.
    4. Fast, M. & Palmé, T., 2010. "Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant," Energy, Elsevier, vol. 35(2), pages 1114-1120.
    5. Fang, Xiande & Dai, Qiumin & Yin, Yanxin & Xu, Yu, 2010. "A compact and accurate empirical model for turbine mass flow characteristics," Energy, Elsevier, vol. 35(12), pages 4819-4823.
    6. Joly, R. B. & Ogaji, S. O. T. & Singh, R. & Probert, S. D., 2004. "Gas-turbine diagnostics using artificial neural-networks for a high bypass ratio military turbofan engine," Applied Energy, Elsevier, vol. 78(4), pages 397-418, August.
    7. Wang, Jiangfeng & Dai, Yiping & Gao, Lin & Ma, Shaolin, 2009. "A new combined cooling, heating and power system driven by solar energy," Renewable Energy, Elsevier, vol. 34(12), pages 2780-2788.
    8. Habib, Zehra & Parthasarathy, Ramkumar & Gollahalli, Subramanyam, 2010. "Performance and emission characteristics of biofuel in a small-scale gas turbine engine," Applied Energy, Elsevier, vol. 87(5), pages 1701-1709, May.
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    Cited by:

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    5. Zhu, Sipeng & Deng, Kangyao & Liu, Sheng, 2015. "Modeling and extrapolating mass flow characteristics of a radial turbocharger turbine," Energy, Elsevier, vol. 87(C), pages 628-637.
    6. Serrano, José Ramón & Olmeda, Pablo & Tiseira, Andrés & García-Cuevas, Luis Miguel & Lefebvre, Alain, 2013. "Theoretical and experimental study of mechanical losses in automotive turbochargers," Energy, Elsevier, vol. 55(C), pages 888-898.
    7. Salameh, Georges & Chesse, Pascal & Chalet, David, 2019. "Mass flow extrapolation model for automotive turbine and confrontation to experiments," Energy, Elsevier, vol. 167(C), pages 325-336.
    8. Tang, Yuanyuan & Zhang, Jundong & Gan, Huibing & Jia, Baozhu & Xia, Yu, 2017. "Development of a real-time two-stroke marine diesel engine model with in-cylinder pressure prediction capability," Applied Energy, Elsevier, vol. 194(C), pages 55-70.

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