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Application of artificial neural network for predicting the dynamic performance of a free piston Stirling engine

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  • Ye, Wenlian
  • Wang, Xiaojun
  • Liu, Yingwen

Abstract

In this study, an artificial neural network model is built to predict the dynamic performance of a beta-type free piston Stirling engine. The influences of six input dynamic parameters on operating frequency, amplitude ratio and phase angle are analyzed. The operating frequency is significantly affected by the spring stiffness and the mass of the pistons. However, the relationships of the dynamic parameters are comprehensive, which are determined by multiple parameters. Then, a number of dynamic output parameters are used as training and testing data. The best results are obtained by 6-6-1, 6-6-1 and 6-10-6-1 network architectures for the operating frequency, amplitude ratio and phase angle respectively. For these network architectures, the back propagation algorithm, namely Levenberg-Marguardt is applied. Stirling engine’s dynamic performance predicted with the network model is compared with the actual values. After training, correlation coefficients (R2) values for training and testing data are close to 1. The mean relative errors of the operating frequency, amplitude ratio and phase angle are 0.85%, 2.78% and 3.19% for the training process. These results show that the artificial neural network model is an acceptable and powerful approach for predicting the dynamic performance of the beta-type free piston Stirling engine.

Suggested Citation

  • Ye, Wenlian & Wang, Xiaojun & Liu, Yingwen, 2020. "Application of artificial neural network for predicting the dynamic performance of a free piston Stirling engine," Energy, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:energy:v:194:y:2020:i:c:s0360544220300190
    DOI: 10.1016/j.energy.2020.116912
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    References listed on IDEAS

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    Cited by:

    1. Ye, Wenlian & Zhang, Ting & Wang, Xiaojun & Liu, Yingwen & Chen, Pengfan, 2020. "Parametric study of gamma-type free piston stirling engine using nonlinear thermodynamic-dynamic coupled model," Energy, Elsevier, vol. 211(C).
    2. Chen, Pengfan & Zhong, Geyu & Niu, Yafeng & Liu, Yingwen, 2022. "Performance optimization of a free piston stirling engine using multi-section regenerators based on the response surface methodology," Energy, Elsevier, vol. 261(PB).
    3. Chang, Depeng & Hu, Jianying & Sun, Yanlei & Zhang, Limin & Chen, Yanyan & Luo, Ercang, 2023. "Numerical investigation on key parameters of a double-acting free piston Stirling generator," Energy, Elsevier, vol. 278(PB).
    4. Chen, Pengfan & Yang, Peng & Liu, Liu & Liu, Yingwen, 2021. "Parametric investigation of the phase characteristics of a beta-type free piston Stirling engine based on a thermodynamic-dynamic coupled model," Energy, Elsevier, vol. 219(C).
    5. Qiu, Hao & Wang, Kai & Yu, Peifeng & Ni, Mingjiang & Xiao, Gang, 2021. "A third-order numerical model and transient characterization of a β-type Stirling engine," Energy, Elsevier, vol. 222(C).
    6. Jiang, Han & Xi, Zhongli & A. Rahman, Anas & Zhang, Xiaoqing, 2020. "Prediction of output power with artificial neural network using extended datasets for Stirling engines," Applied Energy, Elsevier, vol. 271(C).
    7. Rahmati, A. & Varedi-Koulaei, S.M. & Ahmadi, M.H. & Ahmadi, H., 2022. "Dynamic synthesis of the alpha-type stirling engine based on reducing the output velocity fluctuations using Metaheuristic algorithms," Energy, Elsevier, vol. 238(PB).

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