Numerical simulation of soot formation in pulverized coal combustion with detailed chemical reaction mechanism

Muto, Masaya; Yuasa, Kohei; Kurose, Ryoichi

doi:10.1016/j.apt.2018.02.002

Cited by 33 publications

(12 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1998); they validated the simulation using our measured data (Hayashi J. et al, 2013). Muto M. et al (2018) developed a soot-formation model for the DNS of coal-combustion fields. They used the detailed chemistry for soot formation; their simulation results are yet to be validated.…”

Section: Numerical Simulation For Soot Formation In Pulverized-coal Combustion Fieldsmentioning

confidence: 75%

Coal Particle Devolatilization and Soot Formation in Pulverized Coal Combustion Fields

Hashimoto

Hayashi

2021

KONA

View full text Add to dashboard Cite

In this paper, recent developments of the devolatilization model and soot-formation model for the numerical simulations of pulverized-coal combustion fields, and the technology used to measure soot particles in pulverizedcoal combustion fields are reviewed. For the development of new models, the validation of the developed models using measurement is necessary to check the accuracy of the models because new models without validation have a possibility to make large errors in simulations. We have developed the tabulated devolatilization process model (TDP model) that can take into account the effect of particle heating rate on the volatile matter amount and the devolatilization-rate parameters. The accuracy of the developed TDP model was validated by using the laser Doppler velocimetry data for the bench-scale coal combustion test furnace. The soot-formation model combined with TDP model for the large eddy simulation (LES) has been also developed. The spatial distributions of both the soot-volume fraction and the polycyclic aromatic hydrocarbons were measured by virtue of laser-induced incandescence (LII) and laser-induced chemiluminescence (PAHs-LIF). The accuracy of the developed sootformation model was validated by using the measured data.

show abstract

Section: Numerical Simulation For Soot Formation In Pulverized-coal Combustion Fieldsmentioning

confidence: 75%

Coal Particle Devolatilization and Soot Formation in Pulverized Coal Combustion Fields

Hashimoto

Hayashi

2021

KONA

View full text Add to dashboard Cite

show abstract

“…Turbulence variance intensity, laminar flame velocity, laminar flame thickness and integral length scale are decided to become a thin reaction zones regime for each equivalence ratio. In this study, inhouse-code FK 3 is used [20][21][22][23]. The spatial derivative of convective term in the momentum equation is approximated with a fourth-order central difference scheme, and a WENO scheme [24] is used to evaluate the scalar gradients.…”

Section: Fig 3 Diagram Of Premixed Turbulent Combustion Regimesmentioning

confidence: 99%

Numerical Simulation of CO Concentration on Flame Propagation in the Vicinity of the Wall -Validity of Non-Adiabatic FGM Approach-

Yunoki

Kai

Inoue

et al. 2020

JGPP

Self Cite

View full text Add to dashboard Cite

To design a gas turbine combustor for low emissions, carbon monoxide (CO) generated near the cooled wall is one of the important indexes. However, the measuring of CO concentration is difficult in experiments in actual conditions of high pressure and temperature. In this study, in order to take the heat loss effect on the cooled wall into account, a non-adiabatic flamelet generated manifolds (NA-FGM) approach, which can account for the change of gas composition due to the heat loss, is applied to two-dimensional numerical simulations of premixed flame near the cooled wall and the effect of equivalence ratio variation on the CO concentration is investigated. The results show that the CO concentrations predicted for the equivalence ratio of 1.0 using the NA-FGM approach are in good agreements with those using the detailed reaction approach, and that the NA-FGM approach can adequately catch the tendency of CO generation in the vicinity of the wall with heat loss. NOMENCLATURE Progress variable Isopiestic specific heat ℎ Thermal diffusivity Diffusion coefficient � = ⁄ � ℎ Enthalpy Pressure ̇ Source term of heat loss Gas constant Laminar flame velocity Temperature Velocity vector Diffusion velocity of chemical species Molecular weight Mass fraction of chemical species Z Mixture fraction Greeks Adjustment parameter δ Laminar flame thickness λ Thermal conductivity Shear stress tensor φ Equivalence ratio Density ̇ Generation rate of progress variable ̇ Chemical reaction rate of chemical species Subscripts k Chemical species

show abstract

“…Unfortunately, fires regularly occur in solid-fuel systems and the early stages of soot formation vary greatly in these systems from gaseous fuel systems [18]. With regard to solid fuel systems, there are very few models developed, only a few intended for coal systems [19,20] and even fewer for biomass systems [21] where we'd expect most fires to be occurring. All the above cited soot models require a high resolution (millimeters to centimeters) to implement as the models require local values of temperature and chemical species concentrations resolved on a combustion scale.…”

Section: Introductionmentioning

confidence: 99%

Zonal-Based Emission Source Term Model for Predicting Particulate Emission Factors in Wildfire Simulations

et al. 2020

View full text Add to dashboard Cite

A physics/chemistry-based numerical model for predicting the emission of fine particles from wildfires is proposed. This model implements the fundamental mechanisms of soot formation in a combustion environment: soot nucleation, surface growth, agglomeration, oxidation, and particle fragmentation. These mechanisms occur on a scale too fine for the discretization of most wildfire models, which need to simulate landscape-scale dynamics. As a result this model implements a zonal approach, where the computed soot particle distribution is partitioned into process zones within a single resolved grid cell. These process zones include: an inception zone (for nucleation), a heating zone (for coagulation, surface growth, and fragmentation), a reaction zone (for oxidation), and a quenched zone (for atmospheric processes). Governing mechanisms are applied to the appropriate zones to predict total particle growth and emission. The proposed model is implemented into HIGRAD/ FIRETEC, a physics-based wildfire simulation code which couples interactions between fire, fuels, atmosphere, and topography on a landscape scale. Fire simulations among grasslands and conifer forests are performed and compared against experimental data for emission factors.

show abstract

Numerical simulation of soot formation in pulverized coal combustion with detailed chemical reaction mechanism

Cited by 33 publications

References 42 publications

Coal Particle Devolatilization and Soot Formation in Pulverized Coal Combustion Fields

Coal Particle Devolatilization and Soot Formation in Pulverized Coal Combustion Fields

Numerical Simulation of CO Concentration on Flame Propagation in the Vicinity of the Wall -Validity of Non-Adiabatic FGM Approach-

Zonal-Based Emission Source Term Model for Predicting Particulate Emission Factors in Wildfire Simulations

Contact Info

Product

Resources

About