IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v194y2020ics0360544220300190.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220300190
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.116912?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhu, Shunmin & Yu, Guoyao & Ma, Ying & Cheng, Yangbin & Wang, Yalei & Yu, Shaofei & Wu, Zhanghua & Dai, Wei & Luo, Ercang, 2019. "A free-piston Stirling generator integrated with a parabolic trough collector for thermal-to-electric conversion of solar energy," Applied Energy, Elsevier, vol. 242(C), pages 1248-1258.
    2. Khoshnevisan, Benyamin & Rafiee, Shahin & Omid, Mahmoud & Yousefi, Marziye & Movahedi, Mehran, 2013. "Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks," Energy, Elsevier, vol. 52(C), pages 333-338.
    3. Hung, Nguyen Ba & Lim, Ocktaeck, 2016. "A review of free-piston linear engines," Applied Energy, Elsevier, vol. 178(C), pages 78-97.
    4. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    5. Cheng, Chin-Hsiang & Yang, Hang-Suin, 2013. "Theoretical model for predicting thermodynamic behavior of thermal-lag Stirling engine," Energy, Elsevier, vol. 49(C), pages 218-228.
    6. Vuarnoz, D. & Kitanovski, A. & Gonin, C. & Borgeaud, Y. & Delessert, M. & Meinen, M. & Egolf, P.W., 2012. "Quantitative feasibility study of magnetocaloric energy conversion utilizing industrial waste heat," Applied Energy, Elsevier, vol. 100(C), pages 229-237.
    7. Zare, Sh. & Tavakolpour-Saleh, A.R., 2016. "Frequency-based design of a free piston Stirling engine using genetic algorithm," Energy, Elsevier, vol. 109(C), pages 466-480.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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).
    3. Huang, Mengqi & Peng, Changhong & DU, Zhengyu & Liu, Yu, 2024. "A power regulation strategy for heat pipe cooled reactors based on deep learning and hybrid data-driven optimization algorithm," Energy, Elsevier, vol. 289(C).
    4. 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).
    5. 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).
    6. 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).
    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).
    8. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhu, Shunmin & Yu, Guoyao & Liang, Kun & Dai, Wei & Luo, Ercang, 2021. "A review of Stirling-engine-based combined heat and power technology," Applied Energy, Elsevier, vol. 294(C).
    2. Carmela Perozziello & Lavinia Grosu & Bianca Maria Vaglieco, 2021. "Free-Piston Stirling Engine Technologies and Models: A Review," Energies, MDPI, vol. 14(21), pages 1-22, October.
    3. Sun, Haojie & Yu, Guoyao & Zhao, Dan & Dai, Wei & Luo, Ercang, 2023. "Thermoacoustic hysteresis of a free-piston Stirling electric generator," Energy, Elsevier, vol. 280(C).
    4. Yamashiro, Hirochika & Nonaka, Hirofumi, 2021. "Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem," Operations Research Perspectives, Elsevier, vol. 8(C).
    5. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    6. Wang, Zhenyu & Zhang, Yunpeng & Li, Guorong & Zhang, Jinlong & Zhou, Hai & Wu, Ji, 2024. "A novel solar irradiance forecasting method based on multi-physical process of atmosphere optics and LSTM-BP model," Renewable Energy, Elsevier, vol. 226(C).
    7. Zare, Shahryar & Tavakolpour-Saleh, Alireza & Shourangiz-Haghighi, Alireza & Binazadeh, Tahereh, 2019. "Assessment of damping coefficients ranges in design of a free piston Stirling engine: Simulation and experiment," Energy, Elsevier, vol. 185(C), pages 633-643.
    8. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    9. Chen, Ruihua & Zhao, Ruikai & Deng, Shuai & Zhao, Li & Xu, Weicong, 2021. "A cycle research methodology for thermo-chemical engines: From ideal cycle to case study," Energy, Elsevier, vol. 228(C).
    10. Das, Pronob & Das, Barun K. & Rahman, Mushfiqur & Hassan, Rakibul, 2022. "Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms," Energy, Elsevier, vol. 238(PB).
    11. Liu, Yanfeng & Zhou, Yong & Chen, Yaowen & Wang, Dengjia & Wang, Yingying & Zhu, Ying, 2020. "Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China," Renewable Energy, Elsevier, vol. 146(C), pages 1101-1112.
    12. Hu, Luoke & Peng, Chen & Evans, Steve & Peng, Tao & Liu, Ying & Tang, Renzhong & Tiwari, Ashutosh, 2017. "Minimising the machining energy consumption of a machine tool by sequencing the features of a part," Energy, Elsevier, vol. 121(C), pages 292-305.
    13. Ruan, Zhaohui & Sun, Weiwei & Yuan, Yuan & Tan, Heping, 2023. "Accurately forecasting solar radiation distribution at both spatial and temporal dimensions simultaneously with fully-convolutional deep neural network model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    14. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    15. Mehrenjani, Javad Rezazadeh & Gharehghani, Ayat & Ahmadi, Samareh & Powell, Kody M., 2023. "Dynamic simulation of a triple-mode multi-generation system assisted by heat recovery and solar energy storage modules: Techno-economic optimization using machine learning approaches," Applied Energy, Elsevier, vol. 348(C).
    16. Marzouq, Manal & El Fadili, Hakim & Zenkouar, Khalid & Lakhliai, Zakia & Amouzg, Mohammed, 2020. "Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data," Renewable Energy, Elsevier, vol. 157(C), pages 214-231.
    17. Zhao, Dan & Ji, Chenzhen & Li, Shihuai & Li, Junwei, 2014. "Thermodynamic measurement and analysis of dual-temperature thermoacoustic oscillations for energy harvesting application," Energy, Elsevier, vol. 65(C), pages 517-526.
    18. Mojtaba Alborzi & Faramarz Sarhaddi & Fatemeh Sobhnamayan, 2019. "Optimization of the thermal lag Stirling engine performance," Energy & Environment, , vol. 30(1), pages 156-175, February.
    19. Alireza Taghdisian & Sandra G. F. Bukkens & Mario Giampietro, 2022. "A Societal Metabolism Approach to Effectively Analyze the Water–Energy–Food Nexus in an Agricultural Transboundary River Basin," Sustainability, MDPI, vol. 14(15), pages 1-25, July.
    20. Han, Jee-Hoon & Lee, In-Beum, 2014. "A systematic process integration framework for the optimal design and techno-economic performance analysis of energy supply and CO2 mitigation strategies," Applied Energy, Elsevier, vol. 125(C), pages 136-146.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:194:y:2020:i:c:s0360544220300190. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.