Hazen-Williams C-factor assessment in an operational irrigation pipeline
Bibliographic record
Abstract
In the spring of 1998, a closed end gravity flow pipeline was installed in the Lethbridge Northern Irrigation District. The pipes ranged from 900 mm to 300 mm in diameter. Manholes were placed at several locations along the pipeline and were used to install velocity meters and pressure transducers, which recorded data every second. Pressures and velocities during periods of steady state were used to calculate the head loss, Re and the Hazen-Williams friction factor, "C", along lengths of constant diameter. The results were compared to the industry design standard of C = 150 (used for the design of rigid PVC pipe installations) and to the theoretical hydraulically smooth line developed from the Moody diagram and the Darcy-Weisbach equation. Since the maximum and minimum calculated values of C came in the 750 mm and 900 mm diameter pipes, respectively, and all other C values, including those from the 300 mm and 650 mm diameters, fell between these, pipe diameter was assumed not to be a variable. Therefore all the field data was averaged. The average value of Re for the range studied was 9.73 x 105 . The corresponding best-fit C value is 147.7, which is 6.0% lower than the derived theoretical maximum. Since the derived theoretical maximum is the ideal condition and the recorded data was slightly lower and therefore determined be a good representation of what can be expected in true field conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".