The scope of this study is the application of the recently developed univariate moment method Extended Quadrature Method of Moments (EQMOM) (Yuan et al., 2012) to model soot formation in flames. Furthermore, it is combined with another advanced moment approach, called the Conditional Quadrature Method of Moments (CQMOM) (Yuan and Fox, 2011), and this extension leads to a bivariate model. Retaining the efficiency of a moment method, EQMOM enables the reconstruction of the number density function. CQMOM is a numerically robust multivariate moment method which allows a bivariate soot particle description in terms of particle volume and surface to take into account aggregation. The joint Extended Conditional Quadrature Method of Moments (ECQMOM) model combines the advantages of the two methods to arrive at a numerically efficient bivariate moment method which captures both the particle size distribution and the formation of aggregates. Both the EQMOM and the ECQMOM model are validated against experimental results for premixed burner-stabilized ethylene flames. Thereby, the gas phase is modeled using a modified version of a very detailed, well-established kinetic scheme, which is adapted to be consistent with the moment methods introduced. The results demonstrate the suitability of the applied models to describe both soot precursors and soot evolution in flames. Furthermore, the ability of the moment approaches to represent the statistical soot model accurately is evaluated comparing EQMOM and ECQMOM to other numerical approaches, which are based on the Monte Carlo method, the standard Gaussian Quadrature Method of Moments and the Gaussian-Radau Quadrature Method of Moments, respectively.

Modeling soot formation in premixed flames using an Extended Conditional Quadrature Method of Moments

SALENBAUCH, STEFFEN;CUOCI, ALBERTO;FRASSOLDATI, ALESSIO;SAGGESE, CHIARA;FARAVELLI, TIZIANO;
2015-01-01

Abstract

The scope of this study is the application of the recently developed univariate moment method Extended Quadrature Method of Moments (EQMOM) (Yuan et al., 2012) to model soot formation in flames. Furthermore, it is combined with another advanced moment approach, called the Conditional Quadrature Method of Moments (CQMOM) (Yuan and Fox, 2011), and this extension leads to a bivariate model. Retaining the efficiency of a moment method, EQMOM enables the reconstruction of the number density function. CQMOM is a numerically robust multivariate moment method which allows a bivariate soot particle description in terms of particle volume and surface to take into account aggregation. The joint Extended Conditional Quadrature Method of Moments (ECQMOM) model combines the advantages of the two methods to arrive at a numerically efficient bivariate moment method which captures both the particle size distribution and the formation of aggregates. Both the EQMOM and the ECQMOM model are validated against experimental results for premixed burner-stabilized ethylene flames. Thereby, the gas phase is modeled using a modified version of a very detailed, well-established kinetic scheme, which is adapted to be consistent with the moment methods introduced. The results demonstrate the suitability of the applied models to describe both soot precursors and soot evolution in flames. Furthermore, the ability of the moment approaches to represent the statistical soot model accurately is evaluated comparing EQMOM and ECQMOM to other numerical approaches, which are based on the Monte Carlo method, the standard Gaussian Quadrature Method of Moments and the Gaussian-Radau Quadrature Method of Moments, respectively.
2015
Soot modeling, Population balance equation, Premixed flames, Method of moments, Particle size distribution
File in questo prodotto:
File Dimensione Formato  
Salenbauch_et_al_CF2015.pdf

Accesso riservato

Descrizione: Articolo principale
: Publisher’s version
Dimensione 747.31 kB
Formato Adobe PDF
747.31 kB Adobe PDF   Visualizza/Apri
Modeling soot formation in premixed flames_11311-970704_Frassoldati.pdf

accesso aperto

: Post-Print (DRAFT o Author’s Accepted Manuscript-AAM)
Dimensione 806.08 kB
Formato Adobe PDF
806.08 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/970704
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 64
  • ???jsp.display-item.citation.isi??? 56
social impact