Graduation Year
2006
Document Type
Thesis
Degree
M.S.Cp.E.
Degree Granting Department
Computer Science and Engineering
Major Professor
Dmitry B. Goldgof, Ph.D.
Committee Member
Rangachar Kasturi, Ph.D.
Committee Member
Sudeep Sarkar, Ph.D.
Keywords
inclination angle, mathematical modeling, spiral wave, velocity components, wavelength
Abstract
The flow of a liquid film over a rapidly rotating horizontal disk has many applications inmedical, industrial, and engineering fields. A specific example is the heat and mass transfer processes between expanded liquid and surrounded dense gas. Diferent wave regimes of a liquid film depend on a flow conditions such as the properties of a liquid, its initial speed,parameters of environment, etc. Therefore, experimental investigation of the film flow over a spinning disk is needed to both validate theoretical predictions and establish methods for fluid flow monitoring.This thesis presents novel video-based algorithms for detection and tracking wave structural data of the liquid film flowing over a spinning disk reactor. The algorithms are based on the spiral model of wave and the quasi-optimal method for estimation of a wave velocity as ill-posed problem. Their performance is compared with results predicted by the fluid dynamics based on the Navier-Stokes equations in the case of thin film.Using experimental video data, the developed models and algorithms allow investigators to estimate the characteristics of wave regimes such as wavelengths, inclination angles, and the radial and azimuthal velocity components of the fluid. The accuracy of estimated characteristics was analyzed. It was shown that average distance between consecutive two waves,their spiral shapes, and the radial velocities of waves confirm the theoretical results and predictions. In particular, computed wavelength is within 1% and a change of the inclination angles is within 2% of the predicted values.
Scholar Commons Citation
Korzhova, Valentina N., "Tracking Fluid Flow in a Spinning Disk Reactor" (2006). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/3813