Developing a Capacity-Demand Assessment Methodology for Pipeline Components Using Elastic-Plastic Load Factors
Bibliographic record
Abstract
Pipeline design approaches in current Canadian and U.S. code provisions advocate the use of standard linear-elastic analyses to determine stresses for comparison with acceptance criteria. However, complex components such as elbows, tees, and bulkheads may produce combined stresses that exceed maximum allowable limits while the main line pipe passes the acceptance criteria. In these cases, designers have the option to specify a code break between the line pipe and component. But, as discussed by previous authors, this methodology can result in an overly-conservative design for the specified component. To avoid such over-conservatism, the use of elastic-plastic stress analysis approach to satisfy protection against plastic collapse and protection against collapse from buckling criteria in ASME BPVC Section VIII Division 2 has been advocated. The appeal of the elastic-plastic stress analysis approach lies in its more accurate assessment of the plastic collapse design margin of a component as compared to the elastic stress and limit load analysis methods, since the actual structural behavior of the component is more closely approximated (e.g., nonlinear material behavior and stresses due to cross-section geometry discontinuities and pipe ovalization). One drawback of this code use is that it would be limited to new designs and is less appropriate for assessments of pipelines already in use. Secondly, the load factors specified in ASME BPVC Section VIII Division 2 can be significantly higher than the design margin of the code to which the pipeline was originally designed. Particularly for pipelines already in use, it would be more appropriate to use similar assessment methods outlined in the API 579-1/ASME FFS-1 fitness for service document as a means of evaluating the “structural integrity of an in-service component.” This nuance is especially important since API 579-1/ASME FFS-1 provides specific guidance on load factors that should be applied to loads for the protection against plastic collapse and protection against collapse from buckling assessments which are applicable to the original design code. This paper investigates the proper use of API 579-1/ASME FFS-1 for pipeline assessments and provides alternate methodology to develop elastic-plastic capacity curves as criteria to screen load demands estimated from linear-elastic analyses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".