CFD modeling of LPG vessels under fire exposure conditions
Bibliographic record
Abstract
Fire exposure of tanks used for the storage and transportation of liquefied gases under pressure may cause complex heat‐ and mass‐transfer phenomena that may contribute to compromise the integrity of the vessels in accident scenarios. Heat transfer through vessel lading results in the heat‐up of the internal fluid and the increase of vessel internal pressure. However, local temperature gradients in the liquid phase cause liquid stratification phenomena that result in a more rapid vaporization and pressure build‐up in the liquid phase. These fundamental phenomena were analyzed by a computational fluid dynamic model. The model was specifically focused on the early steps of vessel heat‐up, when liquid stratification plays a relevant role in determining the vessel internal pressure. A two‐dimensional transient simulation was set up using ANSYS FLUENT in order to predict the evolution of the liquid and vapor phases during the tank heat up. The model was validated against large scale experimental data available for liquefied petroleum gas vessels exposed to hydrocarbon fires, and was applied to case studies derived from recent accidental events in order to assess the expected time of pressure build‐up in different fire scenarios. © 2014 American Institute of Chemical Engineers AIChE J 60: 4292–4305, 2014
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".