Local Post Weld Heat Treatment of a Pressure Vessel: A Postmortem Evaluation
Bibliographic record
Abstract
Abstract This study is a postmortem evaluation of a process tower that suffered significant distortion after a local post weld heat treatment (PWHT). After installation of new nozzles near the base of the pressure vessel, heat treatment was performed to alleviate welding residual stresses in-situ, using a bulls-eye heating coil configuration. Currently, there are established industry guidelines on the placement of heating coils and insulation, but no standard analytical technique or engineering protocol or acceptance criteria that to assess a PWHT. As such, in addition to the postmortem analysis of the excessive deformation, several analytical techniques are also tested in the current study using Finite Element Analysis (FEA) to determine which method best predicts the deformation experienced by the pressure vessel in question. In these case studies, the analytical techniques include lower-bound estimate of collapse loads, buckling evaluation using linear elastic material properties, and an elastic check using a criterion provided in WRC 452. Creep elongation prediction during the short-term post weld heat treatment and buckling evaluation methods recommended by API 579-1/ASME FFS-1 are also included. The paper summarizes the findings based on the aforementioned methods, and an additional suggestion is provided related to the potential optimization of PWHT design/execution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".