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Sensitivity Analysis of Key Parameters for Population Balance Based Soot Model for Low-Speed Diffusion Flames

1
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
2
Department of Chemical and Materials Engineering, Hefei University, Hefei 230601, Anhui, China
3
School of Mechanical and Automotive Engineering, RMIT University, Melbourne, VIC 3000, Australia
4
Australian Nuclear Science and Technology Organisation (ANSTO), Locked Bag 2001, Kirrawee DC, NSW 2232, Australia
*
Author to whom correspondence should be addressed.
Energies 2019, 12(5), 910; https://doi.org/10.3390/en12050910
Received: 13 January 2019 / Revised: 23 February 2019 / Accepted: 4 March 2019 / Published: 8 March 2019
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Abstract

In this article, the evolution of in-flame soot species in a slow speed, buoyancy-driven diffusion flame is thoroughly studied with the implementation of the population balance approach in association with computational fluid dynamics (CFD) techniques. This model incorporates interactive fire phenomena, including combustion, radiation, turbulent mixing, and all key chemical and physical formation and destruction processes, such as particle inception, surface growth, oxidation, and aggregation. The in-house length-based Direct Quadrature Method of Moments (DQMOM) soot model is fully coupled with all essential fire sub-modelling components and it is specifically constructed for low-speed flames. Additionally, to better describe the combustion process of the parental fuel, ethylene, the strained laminar flamelet model, which considers detailed chemical reaction mechanisms, is adopted. Numerical simulation is validated against a self-conducted co-flow slot burner experimental measurement. A comprehensive assessment of the effect of adopting different nucleation laws, oxidation laws, and various fractal dimension and diffusivity values is performed. The results suggest the model employing Moss law of nucleation, modified NSC law of oxidation, and adopting a fractal dimension value of 2.0 and Schmidt number of 0.9 yields the simulation result that best agreed with experimental data. View Full-Text
Keywords: Population balance approach; computational fluid dynamics; soot diagnostics; combustion modelling; detailed chemistry Population balance approach; computational fluid dynamics; soot diagnostics; combustion modelling; detailed chemistry
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, C.; Yuen, A.C.Y.; Chan, Q.N.; Chen, T.B.Y.; Yang, W.; Cheung, S. .-P.; Yeoh, G.H. Sensitivity Analysis of Key Parameters for Population Balance Based Soot Model for Low-Speed Diffusion Flames. Energies 2019, 12, 910.

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