Experimental Investigation Into the Relationship Between the Roughness Height in Use With Nikuradse or Colebrook Roughness Functions and the Internal Wall Roughness Profile for Commercial Steel Pipes
Bibliographic record
Abstract
The relationships between the sand grain roughness height (ks) in use with Nikuradse or Colebrook correlations for the roughness function (RF) and the internal pipe wall roughness element described by the root-mean-square (RMS) of the roughness profile (Rq) for turbulent flow in pipes are experimentally examined. Flow tests were conducted on a total of 13 commercial steel pipes of two sizes: 168.3 mm and 114.3 mm outer diameter (OD). The aim was to provide further insight into relationship between ks and Rq, for use with either RF correlations. The tests were conducted on high-pressure pipeline quality natural gas in the range of Reynolds number (based on pipe internal diameter) of 9 × 106–16 × 106. For commercial carbon steel pipes, the relationship between ks and Rq was found in the form ks=1.306 Rq+0.078 Rq2 and ks=2.294 Rq (both ks and Rq in μm), for use with Colebrook and Nikuradse RF correlations, respectively. These correlations cover a wide range of Rq from 2.7 μm to 12.5 μm which is typically found in commercial carbon steel pipes. For stainless steel (SS) pipes, preliminary results indicate that other surface roughness profile parameters need to be employed to better define the values of ks for these types of commercial steel pipes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".