Numerical Analysis of Turbulent Natural Convection in LNG Storage
Bibliographic record
Abstract
Nowadays, the heat infiltration through the walls of the liquefied naturel gas (LNG) storage tanks is considered a principal source of security problems throughout the supply and distribution chain.In effect, these infiltrations cause evaporation of LNG coupled with stratification phenomenon, which begat a substantial loss in the quantity and quality of the product and may affect the safe storage.the recent work has been published on the effect of heat infiltration on the storage of cryogenic liquids, with used a parietal heat flux of 50 W/m 2 , and the case when them while used up to 330 W/m 2 .In our case, our objective is to make a study of the natural convection turbulent in a LNG storage, description of temperature and velocity profiles particularly at the boundary layer and generates contours by a movement of fluid in the tank.The software used to observe the evolution different parameters.We took into account two types of storage tank 37 000 m 3 and 160 000m 3 with the number of Ra varies between 10 7 and 10 15 , with two turbulence model Kω SST for the boundary layer away to walls and model kε for tank to study significant variations with a minimum constant heat flux of 50 W/m 2 and we increase up to 500 W/m 2 .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".