Flow coupling analysis and evaluation of different working conditions for crude oil storage tanks under dynamic heating
Bibliographic record
Abstract
Abstract During periods of sharp demand increase, there is a need for significant expansion in the fundamental requirements for crude oil storage. As demand rapidly escalates, the prerequisites for crude oil storage necessitate substantial expansion. To scientifically and effectively reduce energy waste associated with heating oil, it is essential to study the heating methods and efficiency of crude oil storage tanks. Considering environmental temperature, solar radiation, and the physical properties of the oil, we have proposed a new heating model to elucidate the dynamic heating process of large floating roof storage tanks equipped with coils. Computational fluid dynamics (CFD) software was used to analyze the impact of the dynamic heating model on the temperature and velocity fields within the floating roof tank, taking into account different initial oil temperatures, oil levels, wind speeds, and types of crude oil during the winter heating process. Additionally, the research suggests the optimal maintenance heating temperature and duration for crude oil storage tanks during the winter season, introducing heating efficiency and inhomogeneity of temperature field as two evaluation metrics to compare the advantages of the dynamic heating method over traditional heating methods. This study provides fresh insights into the coil heating domain in crude oil storage tanks.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".