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Characterisation of soot particle size distribution through population balance approach and soot diagnostic techniques for a buoyant non-premixed flame

Cheng Wang,A. Yuen,Q. Chan,T. Chen,Wei Yang,S. Cheung,G. Yeoh

Published 2020 in Journal of The Energy Institute

ABSTRACT

Abstract Combustion-generated soot particles is a subject of great interest due to its existing and future use in research and industrial sectors. An in-house Direct Quadrature Method of Moments (DQMOM) based population balance model is constructed to investigate the evolution of soot particulate. Most numerical models assume the particle size to be monodispersed, while the proposed model enables the evaluation of real-time soot particle size distribution which further enhances prediction accuracies. The proposed model is fully coupled with all essential fire sub-modelling components and is specifically constructed for low-speed buoyant flames. Additionally, to better describe the combustion process of parental fuel, ethylene, the strained laminar flamelet model considering detailed chemical reaction mechanisms is adopted. Numerical simulation with the implementation of the proposed population balance soot model has been validated against an in-house co-flow burner experimental study and been compared with other numerical studies. The results demonstrated that the improved DQMOM soot model has significant improvement to the accuracy of simulation when compared to Moss-Brookes soot model. It was also discovered that by employing Moss nucleation law, modified NSC oxidation law and adopting fractal dimension value of 2.0 , the DQMOM model produces the results with the best agreement against experimental data.

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Journal of The Energy Institute

  • Publication date

    2020-02-01

  • Fields of study

    Materials Science, Chemistry, Engineering, Environmental Science

  • Identifiers
    DOI 10.1016/J.JOEI.2019.04.004
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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