Mechanistic Leak-Detection Modeling for Single Gas-Phase Pipelines: Lessons Learned from Fit to Field-Scale Experimental Data
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Bibliographic record
Abstract
The use of pipelines is one of the most popular ways of delivering gas phases as shown by numerous examples in hydrocarbon transportation systems in Arctic regions, oil and gas production facilities in onshore and offshore wells, and municipal gas distribution systems in urban areas. A gas leak from pipelines can cause serious problems not only because of the financial losses associated but also its social and environmental impacts. Therefore, establishing an early leak detection model is vital to safe and secure operations of such pipeline systems. A leak detection model for a single gas phase is presented in this study by using material balance and pressure traverse calculations. The comparison between two steady states, with and without leak, makes it possible to quantify the magnitude of disturbance in two leak detection indicators such as the change in inlet pressure (ΔP in ) and the change in outlet flow rate (Δq out ) in a broad range of leak locations (x leak ) and leak opening sizes (d leak ). The results from the fit to large-scale experimental data of Scott and Yi (1998) show that the value of leak coefficient (C D ), which is shown to be the single-most important but largely unknown parameter, ranges from 0.55 to 4.11, and should be a function of Reynolds number (N Re ) which is related to leak characteristics such as leak location (x leak ), leak opening size (d leak ), leak rate (q leak ) and system pressure. Further investigations show that between the two leak detection indicators, the change in outlet flow rate (Δq out ) is superior to the change in inlet pressure (ΔP in ) because of larger disturbance, if the pressure drop along the pipeline is relatively small compared to the outlet pressure; otherwise, the change in inlet pressure (ΔP in ) is superior to the change in outlet flow rate (Δq out ). Key words : Leak; Leak detection modeling; Pipeline; Leak coefficient; Gas flow in pipe
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it