Machine learning-enhanced combustion modeling for predicting laminar burning velocity of ammonia-hydrogen mixtures using improved reaction mechanisms

Anas Rao, Wei Li, Muhammad Salman Abbasi, Muhammad Ihsan Shahid, Muhammad Farhan, Sana Zulfiqar, Tianhao Chen, Fanhua Ma and Xin Li

Energy, 2025, vol. 320, issue C

Abstract: The ammonia/hydrogen mixture is a promising zero-carbon fuel for internal combustion engines, with optimal efficiency in spark ignition engines at equivalence ratios of 0.6–1 and hydrogen fractions of 0.4–0.6. However, existing reaction mechanisms lose accuracy in this range, limiting combustion modeling. To address this, prediction accuracy is improved using refined reaction kinetics and machine learning algorithms. GRI Mech3.0 is refined by enhancing H/O, N2O, HNO, NH, and NH2 mechanisms, forming Model I for pure ammonia and Model II for lean burn ammonia-hydrogen mixtures. The simulated laminar burning speed of these models is compared with literature and seven other mechanisms. Combustion analysis includes sensitivity analysis of coefficients, nitric oxide emission rates, sub-mechanisms, and reaction sensitivities to assess their impact, with machine learning algorithms to improve accuracy. Model II achieves the highest accuracy under stoichiometric (RMSE = 1.4590 cm/s) and lean burn conditions (RMSE = 1.3701 cm/s). As different mechanisms suit various conditions, machine learning algorithms further enhance prediction accuracy. The support vector machine with particle swarm optimization improves computational accuracy by 2.9745 times over reaction kinetics, demonstrating its effectiveness. This study develops refined reaction mechanisms and machine learning models for practical applications in ammonia-hydrogen fueled SI engines.

Keywords: Ammonia/hydrogen fuel; Laminar burning speed; Reaction kinetics; Machine learning algorithm; PSO-SVM (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225009016
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:320:y:2025:i:c:s0360544225009016

DOI: 10.1016/j.energy.2025.135259

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().