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Prediction of output power with artificial neural network using extended datasets for Stirling engines

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  • Jiang, Han
  • Xi, Zhongli
  • A. Rahman, Anas
  • Zhang, Xiaoqing

Abstract

A Stirling engine is inherently complex in structure and manufacturing process, and its operating mechanism involves thermal-mechanic-electronic (electromagnetic) coupling and complicated nonlinear losses. Therefore, it is difficult to accurately predict the performances by theoretical analysis during the design of a Stirling engine. In the present study, the artificial neural network is used to predict the output power of Stirling engines. Using extended datasets including the isothermal analytical data and the experimental data, two accuracy-improved artificial neural network models that are able to predict the output power for two typical Stirling engine prototypes are developed using Matlab to improve the prediction ability of normal artificial neural network models only based on experimental data. Compared to the normal artificial neural network model, the two improved artificial neural network models achieve maximum improvements of over 50% and 20% in average prediction error for Ford 4-215 engine and General Motors 4L23 engine, respectively. The results also demonstrate that the two improved artificial neural network models have better robustness to the quality of experimental data samples. This research provides an effective approach based on the artificial neural network methodology to predict the performances of Stirling engines.

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  • 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).
    • Handle: RePEc:eee:appene:v:271:y:2020:i:c:s0306261920306358
    DOI: 10.1016/j.apenergy.2020.115123
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    1. Babaelahi, Mojtaba & Sayyaadi, Hoseyn, 2015. "A new thermal model based on polytropic numerical simulation of Stirling engines," Applied Energy, Elsevier, vol. 141(C), pages 143-159.
    2. Toro, Claudia & Lior, Noam, 2017. "Analysis and comparison of solar-heat driven Stirling, Brayton and Rankine cycles for space power generation," Energy, Elsevier, vol. 120(C), pages 549-564.
    3. Hooshang, M. & Askari Moghadam, R. & Alizadeh Nia, S. & Masouleh, M. Tale, 2015. "Optimization of Stirling engine design parameters using neural networks," Renewable Energy, Elsevier, vol. 74(C), pages 855-866.
    4. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
    5. Carrillo Caballero, Gaylord Enrique & Mendoza, Luis Sebastian & Martinez, Arnaldo Martin & Silva, Electo Eduardo & Melian, Vladimir Rafael & Venturini, Osvaldo José & del Olmo, Oscar Almazán, 2017. "Optimization of a Dish Stirling system working with DIR-type receiver using multi-objective techniques," Applied Energy, Elsevier, vol. 204(C), pages 271-286.
    6. Tavakolpour-Saleh, A.R. & Zare, SH. & Bahreman, H., 2017. "A novel active free piston Stirling engine: Modeling, development, and experiment," Applied Energy, Elsevier, vol. 199(C), pages 400-415.
    7. Çinar, Can & Karabulut, Halit, 2005. "Manufacturing and testing of a gamma type Stirling engine," Renewable Energy, Elsevier, vol. 30(1), pages 57-66.
    8. Mohammadi, Mohammad Amin & Jafarian, Ali, 2018. "CFD simulation to investigate hydrodynamics of oscillating flow in a beta-type Stirling engine," Energy, Elsevier, vol. 153(C), pages 287-300.
    9. Li, Ruijie & Grosu, Lavinia & Li, Wei, 2017. "New polytropic model to predict the performance of beta and gamma type Stirling engine," Energy, Elsevier, vol. 128(C), pages 62-76.
    10. 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).
    11. Gairaa, Kacem & Khellaf, Abdallah & Messlem, Youcef & Chellali, Farouk, 2016. "Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 238-249.
    12. Mehra, Roopesh Kumar & Duan, Hao & Luo, Sijie & Rao, Anas & Ma, Fanhua, 2018. "Experimental and artificial neural network (ANN) study of hydrogen enriched compressed natural gas (HCNG) engine under various ignition timings and excess air ratios," Applied Energy, Elsevier, vol. 228(C), pages 736-754.
    13. Ahmadi, Mohammad H. & Ahmadi, Mohammad Ali & Sadatsakkak, Seyed Abbas & Feidt, Michel, 2015. "Connectionist intelligent model estimates output power and torque of stirling engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 871-883.
    14. Yang, Hang-Suin & Cheng, Chin-Hsiang, 2017. "Development of a beta-type Stirling engine with rhombic-drive mechanism using a modified non-ideal adiabatic model," Applied Energy, Elsevier, vol. 200(C), pages 62-72.
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