Strain Capacity of X100 High-Strain Linepipe for Strain-Based Design Application
Bibliographic record
Abstract
In recent years, several natural gas pipeline projects have been planned for permafrost regions. Pipelines laid in such areas are subjected to large plastic deformation as a result of ground movement due to repeated thawing and freezing of the frozen ground. Likewise, in pipeline design methods, research on application of strain-based design as an alternative to the conventional stress-based design method has begun. Much effort has been devoted to the application of strain-based design to high strength linepipe materials. In order to verify the applicability of high-strain X100 linepipe to long distance transmission, a large-scale X100 pipeline was constructed using linepipe with an OD of 42″ and wall thickness of 14.3mm. This paper presents the results of experiments and Finite Element Analysis (FEA) focusing on the strain capacity of high-strain X100 linepipes. The critical compressive strain of X100 high-strain linepipes is discussed based on the results of FEA taking into account geometric imperfections. The critical tensile strain for high-strain X100 pipelines is obtained based on a curved wide plate (CWP) tensile test using specimens taken from girth welded joints. Specifically, the effect of external coating treatment on the strain capacity of X100 high-strain linepipe is investigated. The strain capacity of the 42″ X100 pipeline is considered by comparing the tensile strain limit obtained from girth weld fracture and critical compressive strain which occurs in local buckling under pure bending deformation.
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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 | 0.000 | 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) | 0.003 | 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".