In the present work, a new simulation of nanofluid/vapor two-phase flow inside the 2-D rectangular boiling chamber was numerically investigated. The Eulerian–Eulerian approach used to predict the boiling curve and the interaction between two phases. The surface modification during pool boiling of silica nanofluid represented by surface roughness and wettability is put into the account in this simulation. New closure correlations regarding the nucleation sites density and bubble departure diameter during boiling of silica nanofluid were inserted to extend the boiling model in this work. Besides, the bubble waiting time coefficient which involved in quenching heat flux under heat flux partitioning HFP model was corrected to improve the results of this study. The numerical results validated with experimental works in the literature, and they revealed good agreements for both pure water and nanofluids. The results found that when improving the heat flux partitioning model HFP by considering the surface modification of nucleate pool boiling parameters, it will give more mechanistic sights compared to the classical model, which is used for predicting of boiling heat transfer of pure liquid.
In most cases, the stationary fluidized beds are composed of two different particle classes (inert and active particles), and the concentration profile of these binary beds along the vertical axis is crucial regarding the effectiveness of the reactor. The present study introduces a semi-empirical 1D mathematical model for predicting the vertical concentration profile of binary fluidized beds. The proposed model is a developed and applicable version of the so-called Gibilaro and Rowe two-phase model, in which the differential equations describing the jetsam movement in the bulk and wake phases were solved numerically. The main work was to determine the parameters of the basic model, which was carried out by means of an advanced multi-step parameter fitting procedure. A more general form was established, which is based on direct linkage with the operating parameters that can be directly set and measured on the system. Comparisons with very diverse measured data sets available in the literature prove the accuracy of this model. Additional comparisons pointed out that the realization of this model is numerically inexpensive as it is several orders of magnitude faster than the available 2D and 3D models.
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