Turbulence and interphase mass diffusion assumptions on the performance of secondary settling tanks
© 2018 Water Environment Federation.
| Veröffentlicht in: | Water environment research : a research publication of the Water Environment Federation. - 1998. - 91(2019), 2 vom: 01. Feb., Seite 101-110 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2019
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| Zugriff auf das übergeordnete Werk: | Water environment research : a research publication of the Water Environment Federation |
| Schlagworte: | Journal Article Realizable k-ε model buoyancy effects computational fluid dynamics modeling secondary settling tank turbulent Schmidt number Sewage |
| Zusammenfassung: | © 2018 Water Environment Federation. Secondary settling tanks (SSTs), also known as secondary sedimentation tanks or secondary clarifiers, are a basic yet complicated process in a biological water resource recovery facility. In order to understand and improve SST performance, computational fluid dynamics methods have been employed over the last 30 years. In the present investigation, a Fluent-based two-dimensional axisymmetric numerical model is applied to understand the effects of the buoyancy term (Gb ) in the turbulent kinetic energy (TKE) equation and two model parameters (the coefficient of buoyancy term (C3 ) in the turbulent dissipation rate equation and the turbulent Schmidt number (σc ) in the sludge transport equation) on the performance of an SST. The results show that the hydrodynamics can only be correctly predicted by buoyancy-coupled TKE equation, unless the mixed liquor suspended solids is low and sludge settling velocity is extremely high. When the field observations show the SST is operating well, the buoyancy-decoupled TKE equation predicts the correct result, but the buoyancy-decoupled TKE equation may predict failure. Care is required in selecting the correct modeling technique for various conditions. This study provides guidance on how to avoid modeling problems and increase rates of convergence. PRACTITIONER POINTS: C3 can be set to zero to improve rate of convergence and reduce computing time. σc can be used to adjust SBH, when ESS and RAS concentrations are well calibrated to the field data, but the SBH does not fit field observation |
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| Beschreibung: | Date Completed 24.06.2019 Date Revised 24.06.2019 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1554-7531 |
| DOI: | 10.1002/wer.1003 |