Nouvelle approche pour déterminer la distribution des temps de séjour dans les réservoirs d'eau potable en régime non permanent
Bibliographic record
Abstract
Drinking water storage tanks are used to respond to fluctuating demand and to maintain water pressure in the distribution network. In drinking water plants, they are also used for disinfection. In both cases, a good knowledge of the residence time distribution (RTD) is important. An easy way to obtain the water RTD in a drinking water tank is to perform a tracer study. Nevertheless, "classical" tracing techniques can only be used under steady flow conditions. To cope with that, this study proposes a new approach to extend the application of tracer tests to unsteady flow conditions. This new approach is based on signal processing. It has been tested numerically and on a pilot scale model. The numerical tests have shown that this technique can be used for determining the RTD twice more rapidly than the "classical" methods. Thus, this technique allows to approach the knowledge of the RTD under unsteady flow conditions. Pilot scale tests have shown the need for other improvements to bring the quality of the results obtained to the same level of goodness as those obtained from a theorical example.Key words: drinking water, storage tanks, water quality, tracer study, residence time distribution, signal processing, unsteady flow.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".