Improving incomplete mixing modeling for junctions of water distribution networks
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Most of the existing water quality models for water distribution networks assume complete mixing at junctions. Albeit few models offer the possibility to consider incomplete mixing (IM) at junctions, most of them were developed under laboratory conditions and for equal pipe size junctions. In real-world distribution networks, however, cross junctions of 150 × 100 × 150 × 100 mm or 100 × 150 × 150 × 150 mm are common, yet no model has been developed for these configurations. This paper presents a new equation to compute concentrations in cross junction outlets while considering IM for six cross junction configurations, including unequal pipe sizes and 150 mm pipes. For each cross junction configuration, mixing was studied under 25 flow scenarios in the laboratory and 40 simulated flow scenarios using OpenFOAM software. Two new flow rate ratios were selected as independent variables to compute different outlet concentrations. For two specific cross junctions with equal pipe sizes, the root-mean-squared error between the observed and simulated concentrations of the newly developed model was 0.02, while it was 0.05 and 0.07, respectively, for the AZRED IM model and the Shao et al. IM model.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it