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A novel, reduced-order optimization method for nonlinear model correction of turboshaft engines

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Abstract

Turboshaft engines are different from each other due to manufacturing and installation tolerances. Hence, it is difficult to draw out the physical model from the average component maps and design points to represent the performance of the individual engine. The available test-bed data is usually less than the number of correction coefficients to update the maps, and it is the underdetermined state optimization issue. In this paper, we propose a novel reduced-order optimization, namely PSO-EKF algorithm, combined with prior state estimation for non-linear model correction of the turboshaft engine. This method combines the advantages of PSO and extended Kalman filter (EKF). PSO-EKF method converts partial parameters optimized by PSO into parameters directly solved by EKF. The order of optimization space is reduced. Using the correction coefficient function, a stability improvement strategy is designed to ensure the stability of the optimization process. Compared with the GA and PSO algorithms, experimental verification shows that the method has a faster convergence speed and smaller model error than the general PSO. After the performance map is updated by this method, the error of outputs of the individual model is within 1.4 %.

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Abbreviations

PSO :

Particle swarm optimization algorithm

EKF :

Extended Kalman filter

GA :

Genetic algorithm

PSO-EKF :

Kelvin temperature scale

Ma :

Match number

H :

Altitude

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Acknowledgments

We are grateful for the financial support of the National Science and Technology Major Project (No. 2017-I-0006-0007).

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Authors and Affiliations

  1. College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Xinhao Han, Jinquan Huang, Xin Zhou, Zelong Zou, Feng Lu & Wenxiang Zhou

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  1. Xinhao Han
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  2. Jinquan Huang
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  3. Xin Zhou
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  4. Zelong Zou
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  5. Feng Lu
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  6. Wenxiang Zhou
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Corresponding author

Correspondence to Xinhao Han.

Additional information

Xinhao Han received the B. Eng. in Aerospace Propulsion Engineering from the Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China, in 2020, where studied as a postgraduate student, from 2020 to 2023. He is currently pursuing the Ph.D. in Power Machinery and Engineering with NUAA. His current research interests are in fields of gas turbine engine modelling and control.

Jinquan Huang received the M.S. and Ph.D. in Aerospace Propulsion System Theory and Engineering from the Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 1987 and 1998, respectively. He has been a Professor with the College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, since 1999. His current research interests include gas turbine engine modeling, control, and health management.

Xin Zhou received the Ph.D. degree in Aeronautical and Astronautical Propulsion Theory and Engineering from Nanjing University of Aeronautics and Astronautics, China in 2021. Her current research interests include aeroengine modeling and intelligent control.

Zelong Zou received the B. Eng. in Aerospace Propulsion Engineering from the Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China, in 2019, where studied as a postgraduate student, from 2021 to 2023. He is currently pursuing the Ph.D. in Power Machinery and Engineering with NUAA. His research interests are in gas turbine engine modelling.

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Han, X., Huang, J., Zhou, X. et al. A novel, reduced-order optimization method for nonlinear model correction of turboshaft engines. J Mech Sci Technol 38, 2103–2122 (2024). https://doi.org/10.1007/s12206-024-0340-5

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