Creep-Relaxation Modeling of HDPE and Polyvinyl Chloride Bolted Flange Joints
Bibliographic record
Abstract
Abstract Similar to many polymer materials, high-density polyethylene (HDPE) and polyvinyl chloride (PVC) show a clear creep behavior, the rate of which is influenced by temperature, load, and time. Most bolted flange joints undergo relaxation under compression, which is caused by the creep of the material. However, the creep property of the two polymers is different under tension and compression loading. Since the sealing capacity of a flanged gasketed joint is impacted by the amount of relaxation that takes place, it is important to properly address and predict the relaxation behavior due to flange creep under compression and thereby reducing the chances of leakage failure of HDPE and PVC bolted flange joints. The main objective of this study is to analyze the compressive creep behavior of HDPE and PVC flanges under normal operating conditions. This is achieved by developing a respective creep model for the two materials, based on their short-term experimental creep test data. Both numerical and experimental simulations of the polymeric flange relaxation behavior are conducted on an NPS 3 class 150 bolted flange joint of dissimilar materials, where one of the flanges is made of HDPE or PVC material and the other one is made of steel SA105. The study also provides a clear picture on how the compression creep data of ring specimen may be utilized for predicating the flange bolt load relaxation over time at the operating temperatures.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".