Case study comparisons of computational fluid dynamics (CFD) modeling versus tracer testing for determining clearwell residence times in drinking water treatment
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".