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Transient system simulation for an aircraft engine using a data-driven model

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  • Kim, Sangjo
  • Kim, Kuisoon
  • Son, Changmin

Abstract

In this paper, a simulation approach using a data-driven model to predict the performance of a gas turbine for aircraft engines during transient operations is proposed. The low bypass ratio and mixed-flow turbofan engine is considered in the simulation. The input parameters for the training of the data-driven model are fuel mass flow rate, altitude, flight Mach number, and the required power due to the moments of inertia of the rotating parts. The output parameters in the data-driven model are engine net thrust, low-pressure shaft rotating speed, pressure and temperature at each station, propulsion efficiency, efficiency of energy conversion and the overall efficiency. The data-driven model is trained using the data set obtained from a validated first principle model. In the simulation, using the data-driven model, the final engine performance is calculated using an iterative calculation for converging the power balance equation that considers the required power due to the moments of inertia at each time step. The transient performance simulation is tested during a throttle frequency sweep maneuver. In a comparison between the first principle model and the proposed simulation approach, the R-squared values of the output parameters are higher than 0.98, except for the efficiency of energy conversion and the overall efficiency, which register 0.9715 and 0.9183, respectively. It has been confirmed that the proposed simulation approach using the data-driven model can be applied to the transient simulation.

Suggested Citation

  • Kim, Sangjo & Kim, Kuisoon & Son, Changmin, 2020. "Transient system simulation for an aircraft engine using a data-driven model," Energy, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:energy:v:196:y:2020:i:c:s0360544220301535
    DOI: 10.1016/j.energy.2020.117046
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    References listed on IDEAS

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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. Kim, Sangjo & Son, Changmin & Kim, Kuisoon, 2017. "Combining effect of optimized axial compressor variable guide vanes and bleed air on the thermodynamic performance of aircraft engine system," Energy, Elsevier, vol. 119(C), pages 199-210.
    3. Orozco, Dimas José Rúa & Venturini, Osvaldo José & Escobar Palacio, José Carlos & del Olmo, Oscar Almazán, 2017. "A new methodology of thermodynamic diagnosis, using the thermoeconomic method together with an artificial neural network (ANN): A case study of an externally fired gas turbine (EFGT)," Energy, Elsevier, vol. 123(C), pages 20-35.
    4. Nikpey, H. & Assadi, M. & Breuhaus, P. & Mørkved, P.T., 2014. "Experimental evaluation and ANN modeling of a recuperative micro gas turbine burning mixtures of natural gas and biogas," Applied Energy, Elsevier, vol. 117(C), pages 30-41.
    5. Rossi, Mosè & Renzi, Massimiliano, 2018. "A general methodology for performance prediction of pumps-as-turbines using Artificial Neural Networks," Renewable Energy, Elsevier, vol. 128(PA), pages 265-274.
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    Cited by:

    1. 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).
    2. Wang, Busheng & Xuan, Yimin, 2023. "An integrated model for energy management of aero engines based on thermodynamic principle of variable mass systems," Energy, Elsevier, vol. 276(C).

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