Abstract
Abstract
Ammonia/diesel dual-fuel combustion offers a promising pathway toward low-carbon engine technologies, yet compact and accurate chemical mechanisms suitable for engine simulations remain limited. In this study, a four-component diesel surrogate—n-hexadecane, iso-hexadecane, cyclohexane, and toluene—was formulated to better capture the representative molecular features of diesel fuel. Based on this surrogate, a reduced ammonia/diesel mechanism with 135 species and 697 reactions was developed using a combination of mechanism decoupling, skeletal reduction, and targeted optimization, effectively addressing the compositional and kinetic limitations in previous models. The mechanism was validated using ignition delay times under high-pressure and intermediate-to-high-temperature conditions, together with laminar flame speeds and emission-related species for ammonia and alkanes. Reaction sensitivity and rate-of-production analyses identified several key pathways controlling ammonia/diesel combustion, including the dominant contribution of H 2 O 2 thermal decomposition to the rapid rise of OH radicals, the influence of alkane–NH 2 reactions on ignition delay, and the combined roles of NH 2 –NO x conversion, alkane–NO x interactions, and primary alkane oxidation in shaping NO x and N 2 O formation. Finally, when implemented into a three-dimensional RCCI engine CFD model, the mechanism accurately reproduced the in-cylinder pressure, heat release rate, and emissions across different ammonia energy ratios, demonstrating strong predictive capability under engine-relevant conditions. This study provides a validated and computationally efficient mechanism that supports detailed analysis and simulation of ammonia/diesel dual-fuel combustion.
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@article{Ning2026skeletal,
title = {A skeletal chemical mechanism for high-pressure ammonia/diesel co-combustion in RCCI engines: Validation and analysis of combustion and emissions},
author = {Yilin Ning and Huan Liu and Keshuai Sun and Liangliang Zang and Enzhe Song},
journal = {International Journal of Engine Research},
year = {2026},
doi = {10.1177/14680874261462368},
url = {https://doi.org/10.1177/14680874261462368}
}
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