Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Mechanical Engineering

First Advisor's Name

George S. Dulikravich

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Norman Munroe

Second Advisor's Committee Title

Committee Co-Chair

Third Advisor's Name

Cheng-Xian Lin

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Bilal El-Zahab

Fourth Advisor's Committee Title

Committee member

Fifth Advisor's Name

Leonel Lagos

Fifth Advisor's Committee Title

Committee member

Sixth Advisor's Name

Shankar Subramaniam

Sixth Advisor's Committee Title

Committee member

Keywords

multiphase flow, Computational Fluid Dynamics, solid-gas, Drag model, Two-fluid model, Fluidization, Multiphase Flow with Interphase Exchanges (MFIX), Void fraction, cohesive, particle, van der Waals forces, Laminar, Experiment, pressure measurement

Date of Defense

11-13-2015

Abstract

This dissertation focused on development and utilization of numerical and experimental approaches to improve the CFD modeling of fluidization flow of cohesive micron size particles. The specific objectives of this research were: (1) Developing a cluster prediction mechanism applicable to Two-Fluid Modeling (TFM) of gas-solid systems (2) Developing more accurate drag models for Two-Fluid Modeling (TFM) of gas-solid fluidization flow with the presence of cohesive interparticle forces (3) using the developed model to explore the improvement of accuracy of TFM in simulation of fluidization flow of cohesive powders (4) Understanding the causes and influential factor which led to improvements and quantification of improvements (5) Gathering data from a fast fluidization flow and use these data for benchmark validations. Simulation results with two developed cluster-aware drag models showed that cluster prediction could effectively influence the results in both the first and second cluster-aware models. It was proven that improvement of accuracy of TFM modeling using three versions of the first hybrid model was significant and the best improvements were obtained by using the smallest values of the switch parameter which led to capturing the smallest chances of cluster prediction. In the case of the second hybrid model, dependence of critical model parameter on only Reynolds number led to the fact that improvement of accuracy was significant only in dense section of the fluidized bed. This finding may suggest that a more sophisticated particle resolved DNS model, which can span wide range of solid volume fraction, can be used in the formulation of the cluster-aware drag model. The results of experiment suing high speed imaging indicated the presence of particle clusters in the fluidization flow of FCC inside the riser of FIU-CFB facility. In addition, pressure data was successfully captured along the fluidization column of the facility and used as benchmark validation data for the second hybrid model developed in the present dissertation. It was shown the second hybrid model could predict the pressure data in the dense section of the fluidization column with better accuracy.

Identifier

FIDC000235

ORCID

orcid.org/0000-0001-6786-8857

Comments

The following article was published based on the results of this dissertation:

Ahmadreza Abbasi Baharanchi, Seckin Gokaltun, George Dulikravich, Performance improvement of existing drag models in two-fluid modeling of gas–solid flows using a PR-DNS based drag model, Powder Technology, Volume 286, December 2015, Pages 257-268, ISSN 0032-5910, http://dx.doi.org/10.1016/j.powtec.2015.07.001.

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