Comparison of Pressure Decay Models for Liquid Pipelines
Bibliographic record
Abstract
Abstract This paper presents a comparative study of various models in characterizing the reduction of pressure severity along the length of a given liquid pipeline section. Four models were considered in this study, namely the API RP1176 model, CEPA model, SSI Linear Interpolation model and Pressure Range Linear interpolation model. A total of 132 sets of pressure spectra from TC Energy’s liquid pipelines were collected. Each set includes three pressure spectra, namely one pressure spectrum from a given intermediate valve station and other two pressure spectra from the discharge end of its immediate upstream pump station and the suction end of its immediate downstream pump station. The SSI is used to characterize the severity of a given pressure spectrum. To quantify the uncertainties of the model error associated with the four pressure decay models, the calculated SSI based on the spectrum from the intermediate valve station is compared with the model-predicted SSI at the location of the intermediate valve station based on the SSIs of the upstream discharge and downstream suction pressure spectra. The comparative analyses indicate that the CEPA model is the most accurate and precise model in evaluating the pressure decay along the length of a given pipeline section, and in a descending order followed by Pressure Range Linear Interpolation model, API RP1176 model, and SSI Linear Interpolation model. A fitness-for-service assessment program for liquid pipeline can incorporate the probabilistic characteristics of the model error associated with each of the four models derived based on the 132 sets of pressure data.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".