An Analytical Anisotropic Model to Analyze Fiber Reinforced Polymer Bolted Flange Joints
Bibliographic record
Abstract
Abstract Fiber Reinforce Plastic composite flanges have recently experienced a spectacular development in the area of pressure vessels and piping. The current procedures used for the design of these flanges are a major concern because of their inappropriateness to address the anisotropic behavior of composite materials. The current ASME code design procedure of composite flanges of section X uses the same analytical method as the one of section VIII division 2 which treat the flanges as isotropic materials such as metallic flanges. This study deals with FRP bolted flange joints integrity and bolt tightness. A new developed analytical FRP model that treats anisotropic flanges with and without a hub is presented. The model is based on the anisotropy and a flexibility analysis of all joint elements including the gasket, bolts and flanges. It is supported experimentally with tests conducted on a real NPS 3 class 150 FRP bolted flange. Furthermore, three different numerical models based on 3D anisotropic layered shell and solid element models were conducted to further compare and verify the results obtained from the new developed analytical approach. The results show that the new model has potential to be used as an alternative tool to FEM if an accurate method to analyses the stresses and deformation of problematic FRP bolted joint applications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".