Author :
Release : 1992
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Kind : eBook
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Book Synopsis Predictive Modeling of Particle-laden, Turbulent Flows. Quarterly Progress Report No. 1, September 1--December 1, 1992 by :
Download or read book Predictive Modeling of Particle-laden, Turbulent Flows. Quarterly Progress Report No. 1, September 1--December 1, 1992 written by . This book was released on 1992. Available in PDF, EPUB and Kindle. Book excerpt: The successful prediction of particle-laden, turbulent flows relies heavily on the representation of turbulence in the gas phase. Several types of turbulence models for single-phase gas flow have been developed which compare reasonably well with experimental data. In the present work, a l̀̀ow-Reynolds̀̀ k-?, closure model is chosen to describe the Reynolds stresses associated with gas-phase turbulence. This closure scheme, which involves transport equations for the turbulent kinetic energy and its dissipation rate, is valid in the turbulent core as well as the viscous sublayer. Several versions of the low-Reynolds k-? closure are documented in the literature. However, even those models which are similar in theory often differ considerably in their quantitative and qualitative predictions, making the selection of such a model a difficult task. The purpose of this progress report is to document our findings on the performance of ten different versions of the low-Reynolds k-? model on predicting fully developed pipe flow. The predictions are compared with the experimental data of Schildknecht, et al. (1979). With the exception of the model put forth by Hoffman (1975), the predictions of all the closures show reasonable agreement for the mean velocity profile. However, important quantitative differences exist for the turbulent kinetic energy profile. In addition, the predicted eddy viscosity profile and the wall-region profile of the turbulent kinetic energy dissipation rate exhibit both quantitative and qualitative differences. An effort to extend the present comparisons to include experimental measurements of other researchers is recommended in order to further evaluate the performance of the models.
