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

Santiago Molina, Ricardo Novella, Josep Gomez-Soriano and Miguel Olcina-Girona
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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

Energies, 2021, vol. 14, issue 20, 1-21

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.

Keywords: virtual engine modelling; combustion modelling; machine learning; data-driven modelling; ANN; hydrogen; methane (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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