Characterizing Pressure Fluctuations on Buried Pipelines in Terms Relevant to Stress Corrosion Cracking
Bibliographic record
Abstract
Pressure records for high pressure hydrocarbon pipelines that have previously shown to have occurrences of trans-granular stress corrosion cracking (tSCC) were supplied by ten Canadian Energy Pipeline Association (CEPA) member companies. These pressure records were analyzed by performing six methods of pressure cycle counting described in the ASTM standard E1049-85. A comparison was made of the magnitude and frequency of these counted pressure cycles at compressor/pump discharge and suction locations as well as intermediate locations along the pipeline. An attempt to relate the derived pressure cycles to tSCC growth was made by applying previously published “superposition” type cumulative damage models in calculating tSCC growth rates. Information about the relative importance of fatigue growth in the total tSCC lifecycle as well as the ability to distinguish tSCC susceptible areas on a pipeline based on differences in pressure cycles was gained. Characteristic pressure cycle spectra were developed from the supplied pressure data which describes the different modes of pipeline operation including liquid or gas hydrocarbon transportation as well as mainline or lateral operation which may be of use in further research efforts in this area.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".