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Case study comparisons of computational fluid dynamics (CFD) modeling versus tracer testing for determining clearwell residence times in drinking water treatment

Authors: M R Templeton; R Hofmann; R C Andrews;

Case study comparisons of computational fluid dynamics (CFD) modeling versus tracer testing for determining clearwell residence times in drinking water treatment

Abstract

Computational fluid dynamics (CFD) modeling and full-scale tracer tests (using barium or fluoride) were used to determine the baffle factors of clearwells at three Canadian water treatment facilities (two in Ottawa, Ontario, and one in Peterborough, Ontario). A variety of clearwell baffling configurations and a range of flow rates (35 to 257 MLD) were considered. Two-dimensional CFD modeling (no depth dimension) was conducted using commercially available software (Fluent 6.0®). Virtual particle tracking allowed simulation of the residence time distribution for each clearwell configuration and flow rate condition. The baffle factors (t10/θ) derived from the CFD modeling closely matched the values obtained from full-scale tracer testing (<10% difference in most cases). The results of the study suggest that CFD modeling can be a reliable alternative to tracer testing for determining clearwell residence times and can thereby provide improved estimates of chemical disinfection performance and disinfection by-product formation. Key words: computational fluid dynamics, tracer, clearwell, baffle factor, barium, fluoride, disinfection, drinking water.

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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
28
Average
Top 10%
Average
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