Liquid Pipeline Location Specific Cyclic Pressure Determination
Bibliographic record
Abstract
In the majority of liquid pipelines, the pump station discharge pressure ranges are much greater than the pressure ranges experienced at the suction end of the downstream pump station. Consequently, the cyclic pressure induced fatigue damage accumulation rate is greater at the discharge end than at the suction end of a given pipeline segment. In completing an integrity assessment of a fatigue susceptible feature, assuming that the pump station discharge cyclic pressure profile applies to all features in the line segment is conservative. This conservative assumption can lead to un-necessary repairs, unintentional damage from over-prescribed maintenance, or inefficient decisions regarding maintenance action prioritization. The following paper presents the results of a Canadian Energy Pipeline Association (CEPA) initiative to develop a simple approach to define the cyclic pressure history at any point in a liquid pipeline segment based on the bounding discharge and suction pump station Supervisory Control and Data Acquisition (SCADA) pressure time history data. The approach was developed based on collected operating pipeline SCADA pressure time history data for line segments with intermediate measurement points which could be used to validate the developed model. The pressure time histories for all the locations were analyzed using a Rainflow cycle counting technique to develop pressure range spectra (i.e. histograms of pressure range events) and the cyclic pressure severity of each of the time histories was characterized by the Spectrum Severity Indicator (SSI). The SSI represents the number of annual 90MPa hoop stress cycles required to accumulate the same fatigue damage as the actual pressure spectrums. The technique presented in this paper illustrates how to infer the pressure range spectra or SSI at intermediate locations. The technique is shown to be a significant improvement (i.e. higher location specific accuracy) than either applying the discharge pressure spectrum or applying a linear interpolation between discharge and suction conditions in fatigue life assessments. The liquid pipeline cyclic pressure characterization technique presented in this paper will permit integrity assessment or severity ranking of features along a pipeline to be based on an accurate local pressure profile rather than an upper bound. This understanding will help to improve the accuracy of defect loading, one of the three main pillars in integrity assessment (i.e., loading, geometry, materials) for defects susceptible to cyclic loading (e.g., cracking, mechanical damage).
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".