IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i14p2823-d250601.html
   My bibliography  Save this article

Diesel Mean Value Engine Modeling Based on Thermodynamic Cycle Simulation Using Artificial Neural Network

Author

Listed:
  • Eunhee Ko

    (Department of Mechanical Engineering, Graduate School, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea)

  • Jungsoo Park

    (Department of Mechanical Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Korea)

Abstract

This study aims to construct a reduced thermodynamic cycle model with high accuracy and high model execution speed based on artificial neural network training for real-time numerical analysis. This paper proposes a method of constructing a fast average-value model by combining a 1D plant model and exhaust gas recirculation (EGR) control logic. The combustion model of the detailed model uses a direct-injection diesel multi-pulse (DI-pulse) method similar to diesel combustion characteristics. The DI-pulse combustion method divides the volume of the cylinder into three zones, predicting combustion- and emission-related variables, and each combustion step comprises different correction variables. This detailed model is estimated to be within 5% of the reference engine test results. To reduce the analysis time while maintaining the accuracy of engine performance prediction, the cylinder volumetric efficiency and the exhaust gas temperature were predicted using an artificial neural network. Owing to the lack of input variables in the training of artificial neural networks, it was not possible to predict the 0.6–0.7 range for volumetric efficiency and the 1000–1200 K range for exhaust gas temperature. This is because the mean value model changes the fuel injection method from the common rail fuel injection mode to the single injection mode in the model reduction process and changes the in-cylinder combustion according to the injection timing of the fuel amount injected. In addition, the mean value model combined with EGR logic, i.e., the single-input single-output (SISO) coupled mean value model, verifies the accuracy and responsiveness of the EGR control logic model through a step-transient process. By comparing the engine performance results of the SISO coupled mean value model with those of the mean value model, it is observed that the SISO coupled mean value model achieves the desired target EGR rate within 10 s. The EGR rate is predicted to be similar to the response of volumetric efficiency. This process intuitively predicted the main performance parameters of the engine model through artificial neural networks.

Suggested Citation

  • Eunhee Ko & Jungsoo Park, 2019. "Diesel Mean Value Engine Modeling Based on Thermodynamic Cycle Simulation Using Artificial Neural Network," Energies, MDPI, vol. 12(14), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2823-:d:250601
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/14/2823/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/14/2823/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roy, Sumit & Banerjee, Rahul & Bose, Probir Kumar, 2014. "Performance and exhaust emissions prediction of a CRDI assisted single cylinder diesel engine coupled with EGR using artificial neural network," Applied Energy, Elsevier, vol. 119(C), pages 330-340.
    2. Maroteaux, Fadila & Saad, Charbel, 2015. "Combined mean value engine model and crank angle resolved in-cylinder modeling with NOx emissions model for real-time Diesel engine simulations at high engine speed," Energy, Elsevier, vol. 88(C), pages 515-527.
    3. Theotokatos, Gerasimos & Guan, Cong & Chen, Hui & Lazakis, Iraklis, 2018. "Development of an extended mean value engine model for predicting the marine two-stroke engine operation at varying settings," Energy, Elsevier, vol. 143(C), pages 533-545.
    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. Chih-Hong Lin, 2020. "Permanent-Magnet Synchronous Motor Drive System Using Backstepping Control with Three Adaptive Rules and Revised Recurring Sieved Pollaczek Polynomials Neural Network with Reformed Grey Wolf Optimizat," Energies, MDPI, vol. 13(22), pages 1-33, November.
    2. Der-Fa Chen & Yi-Cheng Shih & Shih-Cheng Li & Chin-Tung Chen & Jung-Chu Ting, 2020. "Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO," Energies, MDPI, vol. 13(11), pages 1-25, June.

    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. Wan, Peng & Liu, Bolan & Li, Ben & Liu, Fanshuo & Zhang, Junwei & Fan, Wenhao & Tang, Jingxian, 2023. "Engine modelling architecture study for hybrid electric vehicle diagnosis application," Energy, Elsevier, vol. 282(C).
    2. Gu, Jie & Wang, Yingyuan & Hu, Jiancun & Zhang, Kun & Shi, Lei & Deng, Kangyao, 2024. "Real-time prediction of fuel consumption and emissions based on deep autoencoding support vector regression for cylinder pressure-based feedback control of marine diesel engines," Energy, Elsevier, vol. 300(C).
    3. Jingrui Li & Jietuo Wang & Teng Liu & Jingjin Dong & Bo Liu & Chaohui Wu & Ying Ye & Hu Wang & Haifeng Liu, 2019. "An Investigation of the Influence of Gas Injection Rate Shape on High-Pressure Direct-Injection Natural Gas Marine Engines," Energies, MDPI, vol. 12(13), pages 1-18, July.
    4. Jarosław Ziółkowski & Mateusz Oszczypała & Jerzy Małachowski & Joanna Szkutnik-Rogoż, 2021. "Use of Artificial Neural Networks to Predict Fuel Consumption on the Basis of Technical Parameters of Vehicles," Energies, MDPI, vol. 14(9), pages 1-23, May.
    5. Zhu, Sipeng & Gu, Yuncheng & Yuan, Hao & Ma, Zetai & Deng, Kangyao, 2020. "Thermodynamic analysis of the turbocharged marine two-stroke engine cycle with different scavenging air control technologies," Energy, Elsevier, vol. 191(C).
    6. Di Battista, D. & Cipollone, R., 2016. "Experimental and numerical assessment of methods to reduce warm up time of engine lubricant oil," Applied Energy, Elsevier, vol. 162(C), pages 570-580.
    7. Konstantinos-Marios Tsitsilonis & Gerasimos Theotokatos & Nikolaos Xiros & Malcolm Habens, 2020. "Systematic Investigation of a Large Two-Stroke Engine Crankshaft Dynamics Model," Energies, MDPI, vol. 13(10), pages 1-29, May.
    8. Sasanka Katreddi & Sujan Kasani & Arvind Thiruvengadam, 2022. "A Review of Applications of Artificial Intelligence in Heavy Duty Trucks," Energies, MDPI, vol. 15(20), pages 1-20, October.
    9. Zamboni, Giorgio & Moggia, Simone & Capobianco, Massimo, 2016. "Hybrid EGR and turbocharging systems control for low NOX and fuel consumption in an automotive diesel engine," Applied Energy, Elsevier, vol. 165(C), pages 839-848.
    10. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    11. Deb, Madhujit & Paul, Abhishek & Debroy, Durbadal & Sastry, G.R.K. & Panua, Raj Sekhar & Bose, P.K., 2015. "An experimental investigation of performance-emission trade off characteristics of a CI engine using hydrogen as dual fuel," Energy, Elsevier, vol. 85(C), pages 569-585.
    12. Shahir, V.K. & Jawahar, C.P. & Suresh, P.R., 2015. "Comparative study of diesel and biodiesel on CI engine with emphasis to emissions—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 686-697.
    13. Taghavifar, Hadi & Khalilarya, Shahram & Jafarmadar, Samad, 2014. "Diesel engine spray characteristics prediction with hybridized artificial neural network optimized by genetic algorithm," Energy, Elsevier, vol. 71(C), pages 656-664.
    14. Kayadelen, Hasan Kayhan, 2018. "A multi-featured model for estimation of thermodynamic properties, adiabatic flame temperature and equilibrium combustion products of fuels, fuel blends, surrogates and fuel additives," Energy, Elsevier, vol. 143(C), pages 241-256.
    15. Sun, Ping & Zhang, Jufang & Dong, Wei & Li, Decheng & Yu, Xiumin, 2023. "Prediction of oxygen-enriched combustion and emission performance on a spark ignition engine using artificial neural networks," Applied Energy, Elsevier, vol. 348(C).
    16. Kshirsagar, Charudatta M. & Anand, Ramanathan, 2017. "Artificial neural network applied forecast on a parametric study of Calophyllum inophyllum methyl ester-diesel engine out responses," Applied Energy, Elsevier, vol. 189(C), pages 555-567.
    17. Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
    18. Raptotasios, Spiridon I. & Sakellaridis, Nikolaos F. & Papagiannakis, Roussos G. & Hountalas, Dimitrios T., 2015. "Application of a multi-zone combustion model to investigate the NOx reduction potential of two-stroke marine diesel engines using EGR," Applied Energy, Elsevier, vol. 157(C), pages 814-823.
    19. Taghavifar, Hamid & Mardani, Aref & Karim Maslak, Haleh, 2015. "A comparative study between artificial neural networks and support vector regression for modeling of the dissipated energy through tire-obstacle collision dynamics," Energy, Elsevier, vol. 89(C), pages 358-364.
    20. Roy, Sumit & Ghosh, Ashmita & Das, Ajoy Kumar & Banerjee, Rahul, 2015. "Development and validation of a GEP model to predict the performance and exhaust emission parameters of a CRDI assisted single cylinder diesel engine coupled with EGR," Applied Energy, Elsevier, vol. 140(C), pages 52-64.

    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:gam:jeners:v:12:y:2019:i:14:p:2823-:d:250601. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.