In the fight against climate change, one of the key pathways to decarbonizing the transport sector is the use of alternative fuels in internal combustion engines. Hydrogen emerges as a promising solution for transforming internal combustion engines into a greener alternative, paving the way for a fuel-flexible era and enabling a rapid reduction in tailpipe carbon emissions. Coherent with this vision, this numerical study investigates different hydrogen/methane mixtures to evaluate the performance of various combustion models based on different modelling approaches. On one hand, a flamelet-based model (ECFM) is presented, demonstrating its ability to fit experimental data through a calibration process that manages turbulence-chemistry interactions by adjusting model parameters. On the other hand, a detailed chemistry solver (SAGE), which employs a kinetics mechanism and does not require calibration to model the reaction rate, is also analysed in this paper and results are compared against the one coming from the flamelet-based combustion model. The analysis focuses on the model’s performance using a Rapid Compression Expansion Machine (RCEM) with a fully premixed charge and a passive pre-chamber, allowing the evaluation of different mixture compositions under nearly constant initial conditions in terms of turbulence and physical properties. More specifically, eight cases are presented, consisting of four different hydrogen concentrations by volume and two different equivalence ratios, to assess the model’s adaptability in lean combustion conditions. For each case, only one chemical kinetic mechanism is examined to evaluate its fidelity in capturing the effects of varying mixture compositions under engine-like conditions.
Premixed Hydrogen-Methane Combustion Modelling in a Pre-Chamber RCEM with Flamelet-Based and Detailed-Chemistry Approaches / Sola, Riccardo; Baratta, Mirko; Misul, Daniela; D'Elia, Matteo; Ferretti, Luca; Kumar Venkataramanan, Jyothish. - (2025). (Intervento presentato al convegno ICE2025 - 17th International Conference on Engines & Vehicles for Sustainable Transport tenutosi a Capri, Italia nel 14th-17th September 2024) [10.4271/2025-24-0046].
Premixed Hydrogen-Methane Combustion Modelling in a Pre-Chamber RCEM with Flamelet-Based and Detailed-Chemistry Approaches
Riccardo Sola;Mirko Baratta;Daniela Misul;
2025
Abstract
In the fight against climate change, one of the key pathways to decarbonizing the transport sector is the use of alternative fuels in internal combustion engines. Hydrogen emerges as a promising solution for transforming internal combustion engines into a greener alternative, paving the way for a fuel-flexible era and enabling a rapid reduction in tailpipe carbon emissions. Coherent with this vision, this numerical study investigates different hydrogen/methane mixtures to evaluate the performance of various combustion models based on different modelling approaches. On one hand, a flamelet-based model (ECFM) is presented, demonstrating its ability to fit experimental data through a calibration process that manages turbulence-chemistry interactions by adjusting model parameters. On the other hand, a detailed chemistry solver (SAGE), which employs a kinetics mechanism and does not require calibration to model the reaction rate, is also analysed in this paper and results are compared against the one coming from the flamelet-based combustion model. The analysis focuses on the model’s performance using a Rapid Compression Expansion Machine (RCEM) with a fully premixed charge and a passive pre-chamber, allowing the evaluation of different mixture compositions under nearly constant initial conditions in terms of turbulence and physical properties. More specifically, eight cases are presented, consisting of four different hydrogen concentrations by volume and two different equivalence ratios, to assess the model’s adaptability in lean combustion conditions. For each case, only one chemical kinetic mechanism is examined to evaluate its fidelity in capturing the effects of varying mixture compositions under engine-like conditions.Pubblicazioni consigliate
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https://hdl.handle.net/11583/3002956
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