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Prediction of NOx emissions and pathways in premixed ammonia-hydrogen-air combustion using CFD-CRN methodology

Chaturvedi, Shivansh, Santhosh, R., Mashruk, Syed, Yadav, Rajneesh and Valera-Medina, Agustin ORCID: https://orcid.org/0000-0003-1580-7133 2023. Prediction of NOx emissions and pathways in premixed ammonia-hydrogen-air combustion using CFD-CRN methodology. Journal of the Energy Institute 111 , 101406. 10.1016/j.joei.2023.101406
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Abstract

Ammonia-hydrogen blends have gained significance as they are carbon-free energy-dense fuels. However, NOx emissions have been a significant concern. In this study, the emissions from the premixed combustion of 70/30VOL.% NH3/H2 blend is studied using the computational fluid dynamics (CFD)- chemical reactor network (CRN) approach. The velocity, temperature, and species field are first obtained using CFD, based on which, a network consisting of perfectly stirred reactors (PSR) and plug flow reactor (PFR) is constructed. Three mechanisms have been implemented in the CRN to predict the NO and N2O emissions. It is shown that the trend of NOx is correctly predicted by the CRN over a wide range of equivalence ratios (φ) of 0.65–1.2 as compared to the authors’ recently published experimental data. It is demonstrated that a single CRN (based on the CFD for a specific φ) can be run to cover the range of = 0.65 to 1.2 b y scaling the temperature input to each reactor of the CRN. To contrast the NO pathways at different φ, quantitative reaction pathway diagrams (QRPD) are constructed, and dominant production and consumption pathways of NO for lean and rich combustion are established. The shifts in reaction pathways with φ are noted and found to be governed by OH, O, and H radicals. Next, the effect of stoichiometry on these radicals is established. Finally, the experimental trend of high NO close to stoichiometric combustion and high N2O in very lean combustion along with their respective pathways are explained.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Advanced Research Computing @ Cardiff (ARCCA)
Engineering
Publisher: Elsevier
ISSN: 1743-9671
Date of First Compliant Deposit: 8 November 2023
Date of Acceptance: 21 September 2023
Last Modified: 10 Jun 2024 13:13
URI: https://orca.cardiff.ac.uk/id/eprint/163178

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