Investigation on the Safety Temperature Boundary After the Shutdown of Cold Transport Pipelines: Based on the XDLVO Theory and Force Balance
Bibliographic record
Abstract
Abstract During the period when the cold transportation pipeline is stopped, the gelled crude oil in the pipeline is prone to adhere and accumulate at the inclined pipe section, adversely impacting operational efficiency. Based on the extended Derjaguin–Landau–Verwey–Overbeek (XDLVO) theory and force balance, this article conducts a study on the safety boundary of cold transportation pipelines after shutdown during the period of extremely high water content. The results indicated that the total interaction of gelatinous oil increased with the drop of temperature at low temperatures. The gelatinous oil particles adhering to the wall surface were subjected to the combined action of adhesion force (Fa), net buoyancy (Fg), and yield force (Fy). Moreover, under low-temperature conditions, four kinds of gelled oil particles all displayed a propensity for adhering to the wall. With the increase of temperature, the adhesion state, adhesion-slip state, and flow-slip state of gelatinous oil particles appeared successively on the wall surface. Subsequently, we developed a predictive model for the minimum adhesion-slip temperature of gelled oil particles, achieving an accuracy within 2 °C. The study is of great significance for determining the safety boundary of cold transport pipeline when it is shutdown.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".