The interplay of suspended sediment concentration, particle size and fluid velocity on the rapid deposition of suspended iron oxide particles in PVC drinking water pipes
Bibliographic record
Abstract
The inner walls of drinking water pipes are often expected to be clean and controlled surfaces to assure safe water access to the public. However, these surfaces are typically contaminated with particulate materials and biofilms that eventually degrade water quality. While water utilities place significant efforts in identifying and flushing material deposits from compromised pipes, the development of effective preventive strategies is still limited by the lack of knowledge about material accumulation processes. The aim of this paper is to examine the interplay between suspended sediment concentration, particle size and fluid velocity and the attachment of suspended iron oxide particles in PVC drinking water pipes. For that purpose, a series of short experiments were completed, whereby water amended with a known concentration of selected and stable iron oxide particles was introduced in a full-scale pipe loop laboratory over a short period of time and both turbidity and suspended sediment concentration (SSC) were measured at the inlet and outlet of the pipe loop. Results showed that a selected fraction of the injected particles with sizes above a specific threshold in the range of 4.6 to 6.8 µm had not reached the downstream section of the pipe loop, but instead remained attached to the pipe walls. In addition, exponential decay of the SSC was observed to occur along the pipes and to cause most of the sediments to accumulate in the upstream section of the pipe loop. The research improved our understanding of the physical processes of particulate material accumulation in DWDSs, and it will help water utilities forecast and monitor material accumulation and discolouration potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".