Examining the Role of Layer Growth Duration on Layer Strength and Turbidity Response in a Full-Scale Laboratory Drinking Water Distribution System at Queen’s University:
Bibliographic record
Abstract
Drinking water typically leaves a treatment facility with a high degree of quality for domestic, commercial, and industrial use. However, the overall aesthetic quality of water has been shown to deteriorate as it travels through the drinking water distribution system (DWDS). While most of these changes go unnoticed to the human eye, in rare occurrences significant discoloured water events still arise that result in customer complaints. Often these discolouration events arise from hydraulic shocks in a DWDS such as watermain breaks and sudden valve setting changes.A new laboratory facility has been built at Queen’s University in Kingston Ontario to examine the growth and erosion of cohesive layers in drinking water distribution systems. The laboratory consists of two full-scale pipe loops comprised of 100 mm diameter PVC pipes each with a length of 198 m. Both loops are located in a climate-controlled environmental chamber to reproduce seasonal water temperature variation in the cold climate of Canada. Each loop has drinking water supplied by its own 3800 L tank which is fed directly by the Kingston distribution system. High-flow and low-flow pumps configured in parallel draw from these large tanks that allows for the variation of flows and pressures experienced in a typical DWDS. The lab is equipped with online sensors to measure turbidity, pH, chlorine residual, temperature, flow rate, and pressure in a real-time manner.The objective of the paper is to examine the role of layer growth duration on layer strength and the magnitude of turbidity response during unidirectional flushing (UDF). Three layer-growth experiments lasting 40 days, 80 days, and 120 days were run at a water temperature of 8°C to reflect the seasonal average temperature in Canada. For each growth experiment, the two pipe loops were conditioned separately at low and high steady-state flow rates of 0.48 L/s (0.01 N/m2) and 1.44 L/s (0.08 N/m2). Both conditioning flows corresponded to fully turbulent flow conditions in the pipe. At the conclusion of each layer-growth experiment, the pipe loops were flushed in 15-minute intervals with applied shear stresses of 1.2 N/m2, 3.2 N/m2, and 5.2 N/m2 to erode the layers.This research will help municipalities better understand the dynamics of layer growth and mobilization to mitigate the risk of future discolouration events. This will help Canadian municipalities with decision making with respect to the replacement of legacy water main assets.
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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 | 0.001 | 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".