Analysis of Surface Strains and Leakage Behavior in Composite Pipes and Vessels Using Digital Image Correlation Technique
Bibliographic record
Abstract
Pipe and vessel structures made from fiber-reinforced polymer composites are know to commonly outperform metallic structures in terms of corrosion resistance and strength-to-weight ratio. However, composite pressure piping and vessels without internal lining are prone to leakage failure caused by matrix cracking. Microscopic fractures in the often brittle matrix phase grow and coalesce under loading, forming a network of matrix cracks that facilitates fluid to permeate the pipe or vessel wall. Hence, liners are often incorporated into composite pressure containment structures. Leakage failures usually occur considerably below pressures causing rupture of composite pipes and vessels. Hence, more efficient designs may be obtained if liners could be avoided altogether. To achieve this goal a thorough understanding of the damage mechanisms leading to leakage failure is required. Composite pressure piping and vessels are generally manufactured using filament winding or similar techniques. Resulting interwoven fiber architectures are generally considered to influence strain patterns and leakage behavior. Classical experimental methods are usually unable to verify this hypothesis, and therefore modeling techniques have largely been employed. In the present study, the effect of fiber architecture on surface strain patterns and the initiation of leakage were investigated experimentally using digital image correlation technique. Surface strain maps were produced for tubular filament-wound composite specimens subjected to combined internal pressure and axial traction. The findings of this study indicate that no distinct correlation exists between surface strain patterns and leakage initiation points.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".