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New Combustion Modelling Approach for Methane-Hydrogen Fueled Engines Using Machine Learning and Engine Virtualization

Author

Listed:
  • Santiago Molina

    (CMT—Motores Térmicos, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, Spain)

  • Ricardo Novella

    (CMT—Motores Térmicos, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, Spain)

  • Josep Gomez-Soriano

    (CMT—Motores Térmicos, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, Spain)

  • Miguel Olcina-Girona

    (CMT—Motores Térmicos, Universitat Politècnica de València, Camino de Vera, 46022 Valencia, Spain)

Abstract

The achievement of a carbon-free emissions economy is one of the main goals to reduce climate change and its negative effects. Scientists and technological improvements have followed this trend, improving efficiency, and reducing carbon and other compounds that foment climate change. Since the main contributor of these emissions is transportation, detaching this sector from fossil fuels is a necessary step towards an environmentally friendly future. Therefore, an evaluation of alternative fuels will be needed to find a suitable replacement for traditional fossil-based fuels. In this scenario, hydrogen appears as a possible solution. However, the existence of the drawbacks associated with the application of H 2 -ICE redirects the solution to dual-fuel strategies, which consist of mixing different fuels, to reduce negative aspects of their separate use while enhancing the benefits. In this work, a new combustion modelling approach based on machine learning (ML) modeling is proposed for predicting the burning rate of different mixtures of methane ( CH 4 ) and hydrogen ( H 2 ). Laminar flame speed calculations have been performed to train the ML model, finding a faster way to obtain good results in comparison with actual models applied to SI engines in the virtual engine model framework.

Suggested Citation

  • Santiago Molina & Ricardo Novella & Josep Gomez-Soriano & Miguel Olcina-Girona, 2021. "New Combustion Modelling Approach for Methane-Hydrogen Fueled Engines Using Machine Learning and Engine Virtualization," Energies, MDPI, vol. 14(20), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6732-:d:657758
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    References listed on IDEAS

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    1. Giglio, Veniero & di Gaeta, Alessandro, 2020. "Novel regression models for wiebe parameters aimed at 0D combustion simulation in spark ignition engines," Energy, Elsevier, vol. 210(C).
    2. Liu, Jinlong & Dumitrescu, Cosmin E., 2019. "Single and double Wiebe function combustion model for a heavy-duty diesel engine retrofitted to natural-gas spark-ignition," Applied Energy, Elsevier, vol. 248(C), pages 95-103.
    3. López, J.J. & Novella, R. & Gomez-Soriano, J. & Martinez-Hernandiz, P.J. & Rampanarivo, F. & Libert, C. & Dabiri, M., 2021. "Advantages of the unscavenged pre-chamber ignition system in turbocharged natural gas engines for automotive applications," Energy, Elsevier, vol. 218(C).
    4. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
    5. Benajes, J. & Novella, R. & Gomez-Soriano, J. & Martinez-Hernandiz, P.J. & Libert, C. & Dabiri, M., 2019. "Evaluation of the passive pre-chamber ignition concept for future high compression ratio turbocharged spark-ignition engines," Applied Energy, Elsevier, vol. 248(C), pages 576-588.
    6. Borges, Cosme P. & Sobczak, Jéssica C. & Silberg, Timothy R. & Uriona-Maldonado, Mauricio & Vaz, Caroline R., 2021. "A systems modeling approach to estimate biogas potential from biomass sources in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    7. Niklas Höhne & Takeshi Kuramochi & Carsten Warnecke & Frauke Röser & Hanna Fekete & Markus Hagemann & Thomas Day & Ritika Tewari & Marie Kurdziel & Sebastian Sterl & Sofia Gonzales, 2017. "The Paris Agreement: resolving the inconsistency between global goals and national contributions," Climate Policy, Taylor & Francis Journals, vol. 17(1), pages 16-32, January.
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    Cited by:

    1. Novella, Ricardo & García, Antonio & Gomez-Soriano, Josep & Fogué-Robles, Álvaro, 2023. "Exploring dilution potential for full load operation of medium duty hydrogen engine for the transport sector," Applied Energy, Elsevier, vol. 349(C).
    2. Ye, Jianan & Xie, Min & Zhang, Shiping & Huang, Ying & Liu, Mingbo & Wang, Qiong, 2023. "Stochastic optimal scheduling of electricity–hydrogen enriched compressed natural gas urban integrated energy system," Renewable Energy, Elsevier, vol. 211(C), pages 1024-1044.
    3. Youcef Sehili & Khaled Loubar & Lyes Tarabet & Mahfoudh Cerdoun & Clément Lacroix, 2023. "Development of Predictive Model for Hydrogen-Natural Gas/Diesel Dual Fuel Engine," Energies, MDPI, vol. 16(19), pages 1-19, October.

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