Computational fluid dynamics application in modeling and improving the performance of a storage reservoir used as a contact chamber for microorganism inactivation
Bibliographic record
Abstract
This study explored the use of computational fluid dynamics (CFD) modeling approach to simulate two tracer studies conducted, in scaled-down physical models of an existing storage tank, to investigate the effect of tank configuration on the effective contact time (t10). One of the scaled-down physical models, of the storage tank, was equipped with one baffle wall at the middle length and the other was equipped with nine baffle walls distributed evenly along the reservoir length. A comparison between the experimental and modeled tracer concentration profiles showed an excellent agreement. The developed CFD model was then applied to different reservoir configurations for further investigation towards achieving t10 improvement. The use of seven small inlets and nine baffle walls resulted in extending the t10 from about 8 min to about 30 min (for a theoretical detention time, τ , of 32 min). Furthermore, using fewer baffle walls with different inlet arrangements enhanced the t10. Key words: microorganism inactivation, disinfection, computational fluid dynamics (CFD), modeling, effective contact time, storage reservoir hydraulics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".