https://orcid.org/0000-0001-5958-7166
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ABSTRACT
A comprehensive CFD model is developed to predict soot for a turbulent kerosene-air diffusion flame. The method of moments (MOM) soot model has better prediction capability with meaningful soot behavioral predictions than semi-empirical models. The coupled model is primarily validated with experimental measurements from the literature in the form of major species concentration, flame temperature, and soot volume fraction. A surrogate mix comprising 80% n-decane and 20% toluene on a liquid volume basis constitutes kerosene fuel. The 20% aromatic content is able to reproduce the sooting behavior with considerable accuracy. The model can replicate CO, CO2, CH4, C2H2, C2H4, and C6H6 concentrations, flame temperature, soot volume fraction, and soot aggregation parameters for a laminar JetA-1 flame at atmospheric pressure. The reproduction of flame temperature and major species concentrations for a laminar kerosene flame validates the applicability of the gas-phase kinetic mechanism and PAH formation pathway for the reacting flow system. The model also performs appreciably for measurements of a turbulent kerosene flame at higher operating pressures up to 6.44 bar. The peak soot volume fraction matches significantly well with the measurements for all the flames. The predicted peak soot volume fractions are 8.8, 27.3, 68, and 82 ppm compared to measured values of 9.3, 28.3, 63, and 67 ppm at pressures 1, 2.7, 4.81, and 6.44 bar, respectively. However, the location of the peak soot has a slight discrepancy at low pressures till 2.7 bar, showing the predicted peak earlier compared to a later appearance in the measurements. The simulated peak flame temperatures are 1377, 1393, 1412, and 1477 K as operating pressure increases from 1 to 2.7, 4.81, and 6.44 bar. The thermal absorbance from soot and species radiation plays a vital role in predicting the flame temperature. Soot radiation reduces flame temperature by ~ 500 K compared to gaseous radiation (CO2, H2O, CO, CH4, etc.), reducing approximately 100 K. The predictive soot number density, mean diameter, surface area, primary particle count in soot aggregates, and primary particle diameter carry a substantial dependency on the operating pressure.
Nomenclature
| C1ε, C2ε, C3ε, Cµ | = | Model constants for turbulence equations |
| dP | = | diameter of precursor species |
| Gb | = | generation of turbulence kinetic energy due to buoyancy |
| Gk | = | generation of turbulence kinetic energy due to the mean velocity gradients |
| Gr | = | coagulation source term |
| k | = | Turbulent kinetic energy |
| kB | = | Boltzmann constant |
| M | = | moment of the particle size distribution function |
| m0 | = | mass of bulk species molecule comprising the particle core |
| mc | = | mass of carbon atom (12 amu) |
| N | = | particle number density denotes the number of soot particles per unit volume |
| NA | = | Avogadro number |
| NC,P | = | number of carbon atoms in a precursor molecule |
| Rr | = | nucleation source term |
| Sk Sε | = | user-defined source terms for k and ε |
| Sr | = | source term in the moment transport equation |
| u | = | velocity component, m s−1 |
| Wr | = | source term due to surface reactions, including surface growth and oxidation |
| x | = | spatial coordinate |
| YM | = | contribution of the fluctuation dilation in compressible turbulence to the overall dissipation rate |
| Greek letters | = | |
| β | = | collision coefficient |
| ε | = | turbulence dissipation rate; Van der Waals enhancement factor = 2.2 |
| γ | = | sticking coefficient |
| η | = | power exponent |
| ρ | = | Density of the fluid, kg m−3 |
| ρb | = | bulk density of particle core (soot density) |
| µ | = | molecular viscosity, kg m−1 s−1 |
| µt | = | turbulent viscosity, kg m−1 s−1 |
| µeff | = | effictive diffusion coefficient |
| ν | = | stoichiometric coefficients |
| σt | = | turbulent Prandtl number for the moment transport equation |
| σk, σε | = | turbulent Prandtl numbers for k and ε |
| Subscripts & superscripts | = | |
| i, j | = | identifier of velocity components for turbulence equations; size class for moment equations |
| B | = | temperature exponent |
Acknowledgements
The authors are pleased to acknowledge funding support from the UAY project (project code I UAY/Proj. 155/18-19) under ICSR, IIT Madras. The authors also sincerely acknowledge NCCRD, IIT Madras and GE Aviation, Bengaluru, for technical discussions and improvements in the quality of research.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Funding
Notes on contributors
Shyamal Bhunia
Shyamal Bhunia obtained his B.Tech in Chemical Engineering from Haldia Institute of Technology in 2010. He also received his master’s degree in the same area from the National Institute of Technology Warangal in 2012. The author was awarded a PhD in Chemical Engineering from the National Institute of Technology Durgapur in 2019. His area of expertise is in CFD modelling of solid, liquid and gaseous fuel combustion and multiphase flow. He was associated with Tata Steel R&D, Jamshedpur, from 2018-2019. The author conducted post-doctoral research at the IIT Madras (2019-2021) and IIT Bombay (2021-2023). The author has published several articles in journals of international repute.
Preeti Aghalayam
Preeti Aghalayam is the Dean of the School of Engineering & Science and Director-in-Charge of the Indian Institute of Technology Madras, Zanzibar Campus. She obtained a B.Tech in Chemical Engineering from IIT Madras in 1991 and an M.S. from the University of Rochester, USA in 1995. She received her PhD in Chemical Engineering in 2000 from the University of Massachusetts, Amherst, USA. Post-PhD, she worked at the Massachusetts Institute of Technology as a Post-Doctoral researcher. After completing her post-doctoral study, she worked as a Professor in Chemical Engineering at IIT Madras (2010-2023) and IIT Bombay (2002-2010).