Modeling the Static Cooling of Wax–Solvent Mixtures in a Cylindrical Vessel
Bibliographic record
Abstract
Under subsea conditions, the transportation of ‘waxy’ crude oil through pipelines is accompanied by the precipitation and deposition of higher paraffinic compounds as solids (waxes) onto the cooler surfaces of the pipeline. Wax deposition is more pronounced during shut down of a pipeline since the fluid is held at static conditions. In this study, the static cooling of wax–solvent mixtures in a cylindrical vessel was modeled as a moving boundary formulation involving liquid–solid phase transformation. The deposition process during the transient cooling was treated as a partial freezing/solidification process. Also, the effect of the mixture composition and the cooling rate on the Wax Precipitation Temperature (WPT) or the solubility curve of the wax–solvent mixture was taken into consideration when the bulk liquid phase temperature was lowered below the WAT of the initial mixture composition. The predictions for the transient temperature profiles in the liquid and the deposit region, and the location of the liquid–deposit interface were validated with recently reported experimental results [19]. The predictions were also compared with the predictions for the gelling behavior of wax–solvent mixtures under static cooling reported by Bidmus [19]. The predictions for the temperature profile at seven thermocouple locations and the location of the liquid–deposit interface were in agreement with the experimental results and signified the important role of the solubility curve. The mathematical model presented was based on heat transfer considerations and regarded the deposition process to be thermally driven.
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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 | 0.000 | 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".