Abstract
Numerical simulations of turbulent reactive sprays are challenging owing to the presence of multiple timescales and multiphysics phenomena involving complex turbulence spray and turbulence-chemistry interactions. In turbulent spray flames, several physical phenomena such as primary and secondary atomization, droplet dispersion, interparticle collisions, evaporation, mixing, and combustion occur simultaneously, and hence it becomes a formidable task to model these complex interactions. To gain fundamental knowledge and advance current modeling capabilities, it may be appropriate to aim for progress in individual modeling of breakup, dispersion, mixing and combustion, which however cannot be viewed in complete isolation. A brief review of the development of state-of-the-art turbulent combustion models applicable to the dilute spray regime is presented. Therefore, complexities associated with the dense regime, including interparticle collisions as well as primary and secondary atomization, are not covered. Further, we restrict ourselves to a brief discussion on large eddy simulation, which has found applications in both laboratory and industrial applications of turbulent combustion without a change in phase. The gas phase-based turbulent combustion models such as flamelet, conditional moment closure and transported filtered density function methods have been developed and extensively used for combustion without a phase change. However, careful adaptation and extension of these models are necessary toward modeling of turbulent combustion with phase change. This article presents a review of recent advances and directions of future research on modeling of turbulent combustion for dilute sprays.
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Abbreviations
- \(B\) :
-
Spalding transfer number
- \(C_{\text{p}}\) :
-
Specific heat at constant pressure
- \(D\) :
-
Diameter
- \(E_{\text{a}}\) :
-
Activation energy
- \(g\) :
-
Acceleration due to gravity
- \(L_{\text{v}}\) :
-
Latent heat of vaporization
- \(Nu\) :
-
Nusselt number
- \({\mathcal{R}}_{\text{u}}\) :
-
Universal gas constant
- \(P\) :
-
Probability density function
- \(q\) :
-
Subgrid flux
- \(Q\) :
-
Conditional expectation
- \({\text{S}}_{ij}\) :
-
Strain rate tensor
- \(T\) :
-
Temperature
- \(v\) :
-
Liquid phase velocity
- \(W\) :
-
Molecular weight
- \(Y\) :
-
Mass fraction
- \(C\) :
-
Reaction progress variable
- \(C_{\text{sgs}}\) :
-
Smagorinsky constant
- \({\mathcal{D}}\) :
-
Diffusivity
- \(G\) :
-
LES filter function
- \(h\) :
-
Enthalpy
- \(m\) :
-
Mass
- \(Re\) :
-
Reynolds number
- \(p\) :
-
Gas phase pressure
- \(Pr\) :
-
Prandtl number
- \(\dot{S}\) :
-
Interphase transfer term due to droplet evaporation
- \(Sc\) :
-
Schmidt number
- \(Sh\) :
-
Sherwood number
- \(u\) :
-
Gas phase velocity
- \(w\) :
-
Weight of a notional particle
- \(x\) :
-
Spatial location
- \(Z\) :
-
Mixture fraction
- \(\alpha\) :
-
Total reactive scalars (including enthalpy)
- \(\Delta\) :
-
LES filter width
- \(\delta\) :
-
Dirac delta function
- \(\mu\) :
-
Viscosity
- \(\tau\) :
-
Timescale
- \(\dot{\omega }\) :
-
Reaction rate
- \(\beta\) :
-
Reactive scalars (without enthalpy)
- \(\Delta_{\text{v}}\) :
-
Filter volume
- \(\lambda\) :
-
Thermal conductivity
- \(\rho\) :
-
Density
- \(\tau_{ij}\) :
-
Stress tensor
- \({\text{d}}\) :
-
Droplet
- \({\text{g}}\) :
-
Gas phase
- \({\text{p}}\) :
-
Notional particle
- \({\text{sgs}}\) :
-
Subgrid scale
- \({\text{F}}\) :
-
Fuel
- \({\text{l}}\) :
-
Liquid phase
- \({\text{sf}}\) :
-
Stochastic fields
- CMC:
-
Conditional moment closure
- ESF:
-
Eulerian stochastic field
- FPV:
-
Flamelet progress variable
- FSSF:
-
Fully stochastic separated flow
- MMC:
-
Multiple mapping conditioning
- PDF:
-
Probability density function
- SDE:
-
Stochastic differential equation
- DNS:
-
Direct numerical simulation
- FDF:
-
Filtered density function
- FGM:
-
Flamelet-generated manifolds
- LES:
-
Large eddy simulation
- MC:
-
Monte Carlo
- SGS:
-
Subgrid scale
- SPM:
-
Stochastic particle method
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Sharma, E., De, S. Large Eddy Simulation-Based Turbulent Combustion Models for Reactive Sprays: Recent Advances and Future Challenges. J Indian Inst Sci 99, 25–41 (2019). https://doi.org/10.1007/s41745-019-0109-5
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DOI: https://doi.org/10.1007/s41745-019-0109-5