Evaluation of Glass and Basalt Fiber Reinforcements for Polymer Composite Pressure Piping
Bibliographic record
Abstract
Pressure piping made from fiber-reinforced polymer composites is becoming increasingly popular. This development is driven by the need for lighter and more corrosion resistant components. Compared with traditional metallic structures, composites may satisfy these requirements without compromising strength or cost-effectiveness. The field of composite materials engineering is evolving rapidly, and new analysis and processing methods, as well as material systems, are continually emerging. The present contribution focuses on fiber reinforcements and their performance in pressurized tubular structures. Recently, basalt fiber has gained in popularity and in many cases has been considered an alternative to conventional fiber materials such as E- and S-glasses for composite piping. An investigation was conducted on the performance of basalt, E-glass, and S-glass reinforcements employing uniaxial tensile test rods and tubular samples. Specimens were produced by wet filament winding using a common thermoset epoxy polymer. In addition to rod sample rupture strength, the failure behavior and strength of tube specimens were assessed for leakage and bursting under different biaxial loading conditions. Two different methodologies for the assessment of leakage failures were described and discussed. Based on the experimental findings the performance of the various fiber reinforcements was evaluated.
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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 | 0.000 | 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 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".