On the Effect of Rising Liquid on Coke Drum Skirt Fatigue Life
Bibliographic record
Abstract
Abstract Due to the severe thermal gradients that develop on coke drums as they are filled with hot oil during coking and water during quenching, accurate modeling of the heat transfer from the fluid to the vessel is critical in the analysis of coke drums. To this end, transient thermo-mechanical simulations typically utilize point measurements of thermal profiles extracted from coke drums in operation, but oftentimes use these point measurements as uniform thermal inputs across the entire inner surface of the drum. In reality, non-uniform thermal fields develop along the height of the vessel depending on the fluid fill rates, the effects of which have also been considered in a number of studies to date. The current study focuses on comparing these two temperature methodologies by estimating fatigue life at the skirt-to-vessel attachment weld (a prevalent location of fatigue damage) for two common skirt designs, to assess the feasibility of the former, simpler approach. Although the results indicate similarities in the overall trends, differences in fatigue-life estimates of up to 20% are calculated between the two approaches. Given these differences, the methodology presented in this study should be considered when a higher level of accuracy is required in estimating fatigue life. However, the alternative, simpler methodology provides conservative estimates of the fatigue life when directional insight or expediency is paramount.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".