Improvements in Adapting mα-Tangent Method for Fitness for Service Evaluation of Local Thin Areas in Storage Tanks
Bibliographic record
Abstract
Damage due to corrosion in the form of local thin area (LTA) is a widespread problem in hydrocarbon storage tanks. Fitness for service (FFS) methods are quantitative engineering evaluations that are performed to demonstrate the structural integrity of an in-service tank containing damage like LTAs and make run, repair or replace decisions. The mα-tangent method is a simplified limit load procedure that can be used for FFS evaluations. The method uses a kinematically active reference volume for evaluating the limit load multiplier. This paper reports preliminary results on the use of a modified reference volume approach by considering the reference volume inside the LTA, formed by the overlapping decay lengths from the LTA boundaries. The results from this approach are compared with the existing method which considers a much broader volume outside the damage as the reference volume and with nonlinear FEA. The Remaining Strength Factor (RSF) calculated from the modified reference volume also compares reasonably well with the results from American Petroleum Institute (API)/American Society of Mechanical Engineers (ASME) FFS procedure widely referred as API 579-1/ASME FFS-1. The study also finds that for large cylinders like tanks with very high R/t ratio, the circumferential decay lengths will be smaller than those previously reported (2.5Rt rather than 6.3Rt).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".