This investigation is of mass, momentum and heat transfer applications of the idealised rotor-stator cavities using Computational Fluid Dynamics (CFD). This approach is based on previous literature that provides a fundamental view of the subject. However, this research is more focused on the development and simulation of high-fidelity computational models to refine the understanding of rotor-stator flow problems in engineering. An open source CFD toolbox, OpenFOAM, is used to solve Navier-Stokes equations and turbulence is modelled using Large eddy simulation (LES) approaches. The rotor boundary layer roughness is modelled by the parametric force approach, which is an ideal method to represent real-world roughness. Different types of rough wall conditions are imposed on the rotor. The roughness of the rotor wall affected the mean velocity profiles and turbulence intensity at the rotor. Increasing the roughness height transmits these effects to the stator wall. The outer wall of the rotor-stator cavity provides a passage to transport the roughness induced disturbances to the stator side, which tends to an unsteady flow even at minor roughness levels. The nanofluid heat transfer in the rotor-stator cavities is investigated using single-phase and two-phase transport models. Both models result in enhanced heat transfer rate by using different volume fractions of nanoparticles. The two-phase models provide additional information on the relative slip in the nanoparticle phase due to the Brownian and thermophoresis effects. Near the hot stator, particles are displaced away from the surface, which results in a mild reduction of heat transfer rates. The final section studies the Lagrangian particle dynamics and deposition in a Rotating Disk Chemical Vapour Deposition (RCVD) chamber. Here, the rotating effects of the disk highly agitate the particle phase, which enhances the deposition efficiencies on the rotor. Apart from that, carrier phase turbulence and thermophoretic forces are important factors in particle dynamics.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:762650 |
| Date | January 2018 |
| Creators | Fernando, Bothalage D. R. |
| Contributors | Gao, Shian ; Garrett, Stephen |
| Publisher | University of Leicester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/2381/43057 |
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