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A new approach to generate turbine map data in the sub-idle operation regime of gas turbines

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  • Kim, Jeong Ho
  • Kim, Tong Seop

Abstract

Gas turbines are likely to face critical situations such as compressor surge and stall in the sub-idle operation region. Thus, it is important to predict the operation characteristics of gas turbines accurately by using simulations in the sub-idle region. However, sufficient information about the component performance maps in the sub-idle region is generally not available, and extrapolation methods are usually used to generate data. However, extrapolation methods tend to generate physically invalid data points in very low-speed regimes, which sometimes cause critical numerical problems in simulations of engine performance. This study proposes a new method to generate performance data in the sub-idle region of turbine performance maps through interpolation. A zero point is introduced where the pressure ratio is one and the mass flow rate is zero. Performance data were generated between the existing performance curve and the zero point using the interpolation. To validate the proposed method, it was applied to publicly available turbine performance maps. It was confirmed that the proposed method improves the numerical matching between the compressor and turbine maps and thus expands the predictable operating regime by as much as 23.3% compared to a conventional extrapolation method.

Suggested Citation

  • Kim, Jeong Ho & Kim, Tong Seop, 2019. "A new approach to generate turbine map data in the sub-idle operation regime of gas turbines," Energy, Elsevier, vol. 173(C), pages 772-784.
    • Handle: RePEc:eee:energy:v:173:y:2019:i:c:p:772-784
    DOI: 10.1016/j.energy.2019.02.110
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    References listed on IDEAS

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    Cited by:

    1. Park, Yeseul & Choi, Minsung & Kim, Kibeom & Li, Xinzhuo & Jung, Chanho & Na, Sangkyung & Choi, Gyungmin, 2020. "Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network," Energy, Elsevier, vol. 213(C).
    2. Liu, Zuming & Karimi, Iftekhar A., 2020. "Gas turbine performance prediction via machine learning," Energy, Elsevier, vol. 192(C).
    3. Seong Won Moon & Tong Seop Kim, 2020. "Advanced Gas Turbine Control Logic Using Black Box Models for Enhancing Operational Flexibility and Stability," Energies, MDPI, vol. 13(21), pages 1-23, October.

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