In this research, a tribocharging model based on the prominent condenser model was used in combination with an Eulerian-Lagrangian CFD model to simulate particle tribocharging in particle-laden flows. The influence of different parameters on particle-wall interactions during particle transport in a particle-laden pipe flow was elucidated. An artificial neural network was developed for predicting particle-wall collision numbers based on a database obtained through CFD simulations. The particle-wall collision number from the CFD model was validated against experimental data in the literature. The tribocharging and CFD models were coupled with the experimental tribocharging data to estimate the contact potential difference of powders, which is a function of contact surfaces' work functions and depends on the physicochemical properties of materials. While the contact potential difference between the particles and wall is an essential parameter in the tribocharging models, the accurate measurement of the property is a complex process requiring a highly controlled environment and special equipment. The results from this research also confirm that particle tribocharging is very much dependant on the particle-wall collision number influenced by various parameters, such as particle size and density, air velocity, and pipe dimensions. Plotting the experimentally measured charge-to-mass ratios against the calculated contact potential differences for samples with different protein contents uncovered a linear trend, which opens a novel approach for protein quantification of powders for a given particle size. Therefore, an algorithm is proposed for rapid quantification of protein content and particle size determination of samples during transport in particle-laden flows based on the triboelectric charge measurement. The algorithm requires a CFD-based artificial neural network to estimate the particle-wall interactions based on the hydrodynamic characteristics of the particles and flow systems. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/27053 |
| Date | January 2021 |
| Creators | Mehrtash, Hadi |
| Contributors | Rajabzadeh, Amin, Srinivasan, Seshasai, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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