A virtual chemical mechanism for prediction of NO emissions from flames

Abstract

A reduced order kinetic model for NO (nitric oxide) prediction, based on the virtual chemistry methodology [M. Cailler, N. Darabiha, and B. Fiorina, Development of a virtual optimized chemistry method. Application to hydrocarbon/air combustion, Combust. Flame 211 (2020), pp. 281–302], is developed and applied. Virtual chemistry aims to optimise thermochemical properties and kinetic rate parameters of a network of virtual species and reactions. A virtual main chemical mechanism is dedicated to temperature and heat release prediction and is coupled with the flow governing equations, whereas satellite sub-mechanisms are designed to predict pollutants formation. Two virtual chemistry mechanisms are here employed: a main mechanism for calculating the temperature and heat release rate and a second mechanism dedicated to NO prediction. To recover the chemical structure of multi-mode combustion, both premixed and non-premixed flamelets are included in the learning database used to optimise the virtual NO mechanism. A multi-zone optimisation procedure is developed to accurately capture both fast and slow NO chemistry that include prompt, thermal and reburning pathways. The proposed NO sub-mechanism and optimisation methodology are applied to CH${}_4$/air combustion. Laminar 1-D premixed and non-premixed flamelet configurations are first tested. The approach is then further assessed in 2-D CFD laminar flame simulations, by providing a direct comparison against detailed chemistry. 2-D premixed, non-premixed and partially premixed flame configurations are numerically investigated. For all cases, the virtual mechanism fairly captures temperature and ${\mathrm{N}\mathrm{O}}_x$ chemistry with only 12 virtual species and 8 virtual reactions with a drastic CPU time reduction compared to detailed chemistry.

Keywords:

• nitric oxide
• virtual chemistry
• laminar flames
• reduced chemistry

Acknowledgments

The authors warmly acknowledge Prof. Nasser Darabiha for the useful discussions about the model and the numerical simulations. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 643134. This work was granted access to the HPC resources under the allocations $A0032B10253$ made available by GENCI (Grand Equipement National de Calcul Intensif). This work was performed using HPC resources from the ‘Mésocentre’ computing centre of CentraleSupélec and École Normale Supérieure Paris-Saclay supported by CNRS and Région Île-de-France (http://mesocentre.centralesupelec.fr/).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the MSCA-ITN-2014-EJD - Marie Skłodowska-Curie Innovative Training Networks (ITN-EJD), grant agreement No 643134. This work was granted access to the HPC resources under the allocations A0032B10253 made available by GENCI (Grand Equipement National de Calcul Intensif). This work was performed using HPC resources from the ‘Mésocentre’ computing center of CentraleSupélec and Ecole Normale Supérieure Paris-Saclay supported by CNRS and Région Ile-de-France (http://mesocentre.centralesupelec.fr/).