High fidelity simulations for polydispersed sprays in the Eulerian-Lagrangian framework need to incorporate subgrid-scale effects in the particle evolution equations. Although the quasi-linear evaporation rate formu- lation captures evaporating droplet statistics, further improvement is required when sub-grid scale velocity effects become essential. The subgrid dispersion model strongly affects droplets spatial distribution, and subsequently net evaporation rate, depending on how rapidly they are dispersed into the dry air region. This paper aims to provide physical insights on these aspects by considering four dispersion models: (i) discrete random walk, (ii) approximate deconvolution method, (iii) stochastic model based on the Langevin equa- tion, and (iv) combined approximate deconvolution method with the Langevin equation. Mass and enthalpy transfer source terms together with droplet diameters and particle distributions were compared against corre- sponding direct numerical and large eddy simulations without a model as reference cases. Numerical results at low Stokes and moderate Reynolds numbers indicate that the dispersion model choice does not affect Eulerian field averages or fluctuations. However, proper dispersion models are essential to capture droplet distributions in the far-field region after jet breakup for Stokes number smaller than unity. The unclosed Lagrangian momentum equation without any dispersion model most accurately reproduces direct numerical simulation in the near field.
Parametric uncertainty is propagated through Reynolds-averaged Navier-Stokes (RANS) computations of a prototypical acetone/air aerosol stream flowing in a dry air environment. Two parameters are considered as uncertain: the inflow velocity dissipation and a coefficient that blends the discrete random walk and the gradient-based dispersion models. A Bayesian setting is employed to represent the degree of belief about the parameters of interest in terms of probability theory, such that uncertainty is described with probability density functions. Random variables are represented by means of polynomial chaos expansions. The sensitivity of mean axial velocity and mean vapor mass fraction to the uncertain parameters is discussed.
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