Failure phenomena in metallic and metal-lined polymer composite pressure vessels for aerospace applications
Bibliographic record
Abstract
Metallic and metal-lined polymer composite pressure vessels are extensively used in industries, including the aerospace industry. In the absence of unique failure criteria for the structural elements, phenomenological or empirical methodologies are used by researchers. This paper discusses comprehensive methodologies for predicting burst pressure of metallic and metal-lined polymer composite pressure vessels for aerospace applications. Metallic pressure vessels were analysed using Ansys software, considering the elastic-plastic nature of the materials. The progressive analysis was performed in metal-lined composite pressure vessels in an explicit mode using Ansys software. The problem of solution convergence is discussed in detail. The extent of degradation in static analysis is suggested after multiple analysis trials. In the unit pressure extrapolation technique, the stress components were evaluated using Ansys software and transformed into a local coordinate system, and hence, the failure pressure of the first-ply was identified by the maximum stress criterion. The analysis then continues with the degradation of the failed layers using Ansys software, and successive failures of layers were identified in steps. The results of burst pressure, evaluated through the present analyses, showed good agreement with the published test results. The procedures described in the paper would be of interest to the designers of pressure vessels.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".