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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 H2-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 (CH4) and hydrogen (H2). 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.

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