Measurements and Evaluation of Internal Wall Surface Roughness of Small Diameter Pipes for High Pressure Natural Gas Systems
Bibliographic record
Abstract
The default roughness parameter values used in industry to determine the pressure loss through small diameter pipeline systems are much higher than the values employed in typical large diameter gas transmission and lateral systems. It is uncertain whether these higher roughness values are due to higher topological roughness of the internal wall of the small diameter pipes or if they are a result of other factors. Measurements were taken on 17 small diameter pipe samples in order to evaluate the pipe-wall roughness parameter. A model to calculate the effective roughness parameter, which takes into account pressure losses due to the measured roughness as well as internal welds and scaling, has been developed. The effective roughness parameter of these samples is found to range from 20.4μm to 62.9μm, an increase of 11.0μm to 23.3μm over the measured pipe-wall roughness parameter. This range of effective roughness parameters agrees well with the default range of 35μm to 65μm used in industry, as well as the literature quoted range for clean pipe of 40μm to 100μm. The measured roughness parameter on average increases with increasing nominal pipe size, a result that may be a characteristic of the extrusion or hot-rolling processes used to manufacture small diameter pipes. Additionally, there is a large variation in the measured roughness parameters of pipe samples of the same nominal pipe size, indicating that surface roughness can vary depending on the manufacturing source of these pipes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".