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On the spatial structure and intermittency of soot in a lab-scale gas turbine combustor: Insights from large-eddy simulations

Leonardo Pachano, Daniel Mira, Abhijit Kalbhor, Jeroen van Oijen

Abstract

This work presents a numerical investigation of soot formation in the Cambridge lab-scale gas turbine combustor. Large-eddy simulations (LES) of a swirl-stabilized ethylene flame are performed using the flamelet generated manifold method coupled with a discrete sectional model to account for soot formation, growth, and oxidation. The study aims to elucidate the mechanism governing the spatial structure and intermittency of soot, supported by comparisons with experimental data. The predicted soot distribution agrees well with measurements, with peak concentrations near the bluff body. Flow recirculation is identified as the key mechanism driving soot accumulation in fuel-rich regions, where surface reactions dominate soot mass growth. Soot intermittency arises from fluctuations in the flow field driven by interactions between the flame front and the recirculation vortex. Two soot modeling approaches are evaluated, differing in their treatment of soot model quantities: the first approach employs on-the-fly computation of source terms (FGM-C), while the second uses fully pre-tabulated source terms (FGM-T). Their predictive performance and computational cost are compared in the context of unsteady, sooting flames in swirl-stabilized combustors.

On the spatial structure and intermittency of soot in a lab-scale gas turbine combustor: Insights from large-eddy simulations

Abstract

This work presents a numerical investigation of soot formation in the Cambridge lab-scale gas turbine combustor. Large-eddy simulations (LES) of a swirl-stabilized ethylene flame are performed using the flamelet generated manifold method coupled with a discrete sectional model to account for soot formation, growth, and oxidation. The study aims to elucidate the mechanism governing the spatial structure and intermittency of soot, supported by comparisons with experimental data. The predicted soot distribution agrees well with measurements, with peak concentrations near the bluff body. Flow recirculation is identified as the key mechanism driving soot accumulation in fuel-rich regions, where surface reactions dominate soot mass growth. Soot intermittency arises from fluctuations in the flow field driven by interactions between the flame front and the recirculation vortex. Two soot modeling approaches are evaluated, differing in their treatment of soot model quantities: the first approach employs on-the-fly computation of source terms (FGM-C), while the second uses fully pre-tabulated source terms (FGM-T). Their predictive performance and computational cost are compared in the context of unsteady, sooting flames in swirl-stabilized combustors.
Paper Structure (15 sections, 9 equations, 16 figures)

This paper contains 15 sections, 9 equations, 16 figures.

Figures (16)

  • Figure 1: Computational domain including a slice placed at the center of the combustor colored by instantaneous temperature. $Z_{st}$ isocontour in white.
  • Figure 2: Experimental LII soot volume fraction defalco2021 (a). FGM-C predictions of mean soot volume fraction (b), soot number density (c), and A4 mass fraction (d). The stoichiometric mixture-fraction isocontour is shown in gray, and the zero-axial-velocity isocontour in white. Red points and circles indicate the locations of extinction and in-situ PSD measurements, respectively.
  • Figure 3: Predicted mean mixture-fraction–temperature scatter colored by mean soot volume fraction (top) and soot mass-fraction source term (bottom). Gray vertical lines indicate $Z_{st}$ (dashed) and $6 \times Z_{st}$ (dotted). Results correspond to the FGM-C approach.
  • Figure 4: Predicted mean soot source terms from nucleation (a), condensation (b), and surface reactions (c). Gray lines indicate $Z_{st}$ (solid) and $6 \times Z_{st}$ (dashed) isocontours. Blue isocontour drawn at 80% of peak value. Results correspond to the FGM-C approach.
  • Figure 5: Predicted mean normalized soot volume fraction radial profiles from FGM-C (red) and FGM-T (blue) approaches. A dotted gray line indicates the location of the ISL. Normalized experimental LII data from defalco2021.
  • ...and 11 more figures