Modeling of Solids Deposition from “Waxy” Mixtures in “Hot Flow” and “Cold Flow” Regimes in a Pipeline Operating under Turbulent Flow
Bibliographic record
Abstract
Solids deposition from “waxy” mixtures under turbulent flow in a pipeline was modeled as a moving boundary problem involving liquid–solid phase transformation. The developed model is applicable for the “hot flow” regime (i.e., with the mixture temperature above its wax appearance temperature, WAT) and the “cold flow” regime (i.e., with the mixture temperature below its WAT, resulting in solid particles suspended in the liquid phase). A recently proposed correlation for the wax precipitation temperature (WPT) as a function of the wax concentration and the cooling rate was used to predict the transition from the “hot flow” regime to the “cold flow” regime. Predictions obtained for both radial and axial deposit growth in the pipeline with time in the “hot flow” and “cold flow” regimes were found to be in agreement with the trends observed in the laboratory deposition results reported in the literature. The predicted deposit thickness in the axial direction increased under the “hot flow” regime, reached a maximum as the liquid temperature approached the WAT of the wax–solvent mixture, and decreased subsequently under the “cold flow” regime. The axial location for the transition from the “hot flow” regime to the “cold flow” regime was predicted to shift with changes in the inlet mixture temperature, pipe wall temperature, and Reynolds number. The predicted maximum deposit thickness was also impacted by these variables. The predictions in this study indicate that solids deposition in pipelines carrying “waxy” mixtures could be decreased by maintaining the flow under the “cold flow” regime. This study shows that solids deposition from “waxy” mixtures can be modeled satisfactorily as a thermally driven process involving partial solidification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".