The aero-engine is a large and complex system, and the research and use of simulation technology can greatly reduce the cost and difficulty of learning, training and, experimental research. Based on the flight parameter data and the engine control plan, this paper uses computer simulation technology to establish the steady-state process neural network model of the engine, and adopts the process fitting method to establish the transient state process engine model. Finally, by integrating the transition state model and the steady state model, the development of the aero-engine dynamic model is completed. Since the development of the model is based on real flight parameter data, it has high simulation accuracy and can achieve better simulation results.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Aero-engine dynamic modeling based on flight parameter data and BP neural network


    Contributors:
    Li, Shuaiguo (author) / Peng, Jingbo (author) / Wang, Weixuan (author)

    Conference:

    MEMAT 2022 - 2nd International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology ; 2022 ; Guilin, China


    Published in:

    Publication date :

    2022-01-01


    Size :

    6 pages



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Dynamic Data Driven Modeling of Aero Engine Response

    Krishnan, Manu / Gugercin, Serkan / Sever, Ibrahim et al. | British Library Conference Proceedings | 2020


    Aero engine problems in flight

    Penn, R.J. | Engineering Index Backfile | 1928


    Aero-Engine Control Based on Improved Neural Network

    Shuguang, Lei / Yanying, Guo / Jiayu, Li | IEEE | 2016


    Data Based Modeling of Aero Engine Vibration Responses

    Krishnan, Manu / Jin, Ran / Sever, Ibrahim A. et al. | TIBKAT | 2020


    Data Based Modeling of Aero Engine Vibration Responses

    Krishnan, Manu / Jin, Ran / Sever, Ibrahim A. et al. | Springer Verlag | 2019