Fitness-for-Service Evaluation of Thermal Hot Spots and Corrosion Damage in Cylindrical Pressure Components
Bibliographic record
Abstract
Thermal hot spots and corrosion damage are typical of damages occurring in pressure vessels and piping. Structural integrity of such components needs to be evaluated periodically to determine “fitness-for-service” of the components. In the present paper, three alternative methods for Level 2 FFS assessments (as defined by API 579) are proposed. They are based on variational principles in plasticity, the limit load multiplier m-alpha method, reference volume and the concept of decay lengths in shells. Decay lengths in the axial and circumferential directions for cylindrical shells are derived based on elastic shell theories. They are used to specify the reference volume participating in plastic action and the limit of what can be called “local” damage. Interaction between longitudinal and circumferential effects is investigated. A linear interaction curve is shown to give good estimations of “remaining strength factor” for damages of practical aspect ratios. The stretching and bulging effects due to the damage are also studied. The limit defining the threshold to dominance of stretching action is proposed by using an approximate equilibrium calculation based on yield-line analysis. The effectiveness of the proposed assessments are demonstrated through an example and verified by Level 3 inelastic finite element analysis.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".