Parametric Study of Sleeve Repair on Wrinkled Energy Pipelines
Bibliographic record
Abstract
Field experience showed that repairing wrinkles developed on energy pipelines using steel sleeves is an efficient and cost effective method. Based on the previous successful numerical simulations of a field wrinkle sleeve repair work, a parametric study was conducted by using Finite Element (FE) method to further investigate the effectiveness of the sleeve repair technique. The FE package ABAQUS 6.4 was utilized in conducting the parametric study. The parameters studied include the length, the thickness, and the material properties of the sleeve, and the thickness of the collar, which is used to fit between the wrinkled pipe and the repairing sleeve. The range of the parameters studied covers the most commonly used typical values in the pipeline industry. Two phases were used in carrying out the parametric study. In Phase I, the parameter that plays the most important role in determining the behavior of the wrinkle sleeve repair system (WSRS) was studied. It is found this parameter is the length of the repairing sleeve. Brief discussion was given regarding the way this parameter affects the behavior of the pipe using the WSRS. In Phase II, based on the results from the Phase I study, the effects of other parameters were investigated through a series of FE analyses. Conclusions were drawn and recommendations for future wrinkle sleeve repair work were given based on the results of the parametric study.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".