Model fast deposition of fine iron oxide particles on PVC pipe mains during the passage of a suspended particle plume in a full-scale laboratory system
Bibliographic record
Abstract
This paper aims to investigate the interplay of suspended sediment concentration (SSC) and the fluid velocity that are both responsible for the rapid formation of sediments deposits in a full-scale laboratory system with PVC pipes. The specific objectives of the paper were to: (1) estimate the average rate of iron oxide particle deposition on PVC pipes immediately after the rapid release, and during the passage of, a highly-concentrated plume of iron oxide particles that typifies discolouration events in real systems; (2) determine whether previously deposited particles on the pipe wall from the passage of previous iron oxide particle plumes affect the average rate of iron oxide deposition and attachment to the pipe wall. Experiments were realized in a test facility that mimics the operation of drinking water systems using a special design method to accurately inoculate and detect the passage of iron oxide particles through the pipes under steady flow conditions commonly found in water networks. The experiments consisted of the passage of 3 sequential waves of particles, tested for three different SSC and three different flow velocities. The results showed that increasing the inlet water quality conditions and the concentration of the particulate plume tended to increase the total deposited load while fluid velocity had a negligeable impact on the total deposited load. The results also showed that both an increase of concentration and fluid velocity produced an increase in the average deposition rate, and that this was mainly caused by the shorter conditioning period of the experiments at higher velocities. Further, the experimental results did not show any change in average particle deposition rate and attachment across the three plumes. This suggests that the presence of particle deposits from previous plumes had a negligible impact on particle deposition and attachment.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".