Effects of buoyancy source composition on multiphase plume behavior in stratification
| Autor: | Chow, Aaron C. (Aaron Chunghin), 1978 |
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| Jazyk: | angličtina |
| Rok vydání: | 2004 |
| Předmět: | |
| Druh dokumentu: | Diplomová práce |
| Popis: | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004. Includes bibliographical references (p. 173-179). This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Experiments are performed where a dense multiphase plume is released vertically in a salinity stratified ambient. The constituent phase composition of the initial buoyancy flux can be dense brine, particles, or a mixture of the two in a prescribed ratio. The resulting trapping heights and peeling depths are recorded by visual acquisition and from dye fluorescence measurements. Also, the radial concentration distribution of the dispersed phase after the first peeling event is obtained by collecting the settled particles from the bottom of the tank. Analytical models assuming plug flow and well-mixed particle distributions within the intrusion layer are used to predict the spread of the particle distribution based on initial buoyancy flux, momentum flux, stratification parameter and particle fall velocity. The effects of initial momentum and volume flux on peel and trap depths were studied by comparing the predictions from these models. Finally the observed results are compared to a single-phase plume numerical prediction (CORMIX) and a multiphase numerical plume model. Observed peeling depths were not sensitive to buoyancy composition, while observed trap depths decreased slightly with high particle fractions, possibly from the 'lift-off' phenomenon where particle fallout decreases the bulk buoyancy of the intrusion layer. The observed radial distribution was Gaussian, consistent with particles being vertially well mixed in the intrusion layer, and the standard deviation agreed well with predictions. by Aaron C. Chow. S.M. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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