| The main objective of this work is to develop and validate advanced stochastic models for non-isothermal flows laden with evaporating droplets, while accounting for turbulence anisotropy, temporal/spatial correlations, and cross-correlations among various components of the velocity, temperature, and vapor mass fraction fluctuations. The primary focus of this thesis is on the dispersed phase, and the modeling issues pertaining to the carrier phase flow are only addressed as available in the existing literature.; To achieve the above objective, a systematic approach is adopted in this thesis. First, a detailed investigation of the existing stochastic models is conducted by implementing these models for simulation of particle-laden swirling jet flows. The carrier phase is simulated with two different turbulence models, namely, k-ϵ model and Reynolds stress model. In the particle phase calculation, three different existing stochastic models are compared, namely, a one-time-scale eddy-interaction (EI) model which is the Fluent™ built-in model, a two-time-scale EI model and a model with explicit correlation functions. Next, a more general stochastic model is proposed. This new model is constructed in Lagrangian framework and is capable of predicting the temperature fluctuation as well as the velocity fluctuation. The model is then extended to include the turbulence spatial correlation, and it is shown that the inclusion of this correlation is imperative for capturing the gravity effects. Finally, the new stochastic model is extended for simulation of evaporating droplets, which involves calculation of the vapor mass fraction statistics. To test the model performance in predicting the evaporating droplets behavior, a low-Mach-number homogeneous shear two-phase flow is simulated. This model can easily be extended to incorporate mass fraction fluctuations of other species in a reacting system.; A great deal of efforts has been devoted in this work to assess and validate the stochastic models in various flows using the data from experiments and direct numerical simulations of particle/droplet-laden turbulent flows. The comparisons between the new model prediction and the existing data, indicate very encouraging agreements and clearly demonstrate the necessity of inclusion of turbulence correlations in stochastic models. |
