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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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    Cited by:

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    3. Sakellaridis, Nikolaos F. & Raptotasios, Spyridon I. & Antonopoulos, Antonis K. & Mavropoulos, Georgios C. & Hountalas, Dimitrios T., 2015. "Development and validation of a new turbocharger simulation methodology for marine two stroke diesel engine modelling and diagnostic applications," Energy, Elsevier, vol. 91(C), pages 952-966.
    4. Xu, Maojun & Liu, Jinxin & Li, Ming & Geng, Jia & Wu, Yun & Song, Zhiping, 2022. "Improved hybrid modeling method with input and output self-tuning for gas turbine engine," Energy, Elsevier, vol. 238(PA).
    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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