Simulations of Axial Mixing of Liquids in a Long Horizontal Pipe for Industrial Applications
Bibliographic record
Abstract
Various industrial applications require the use of common pipelines or tubing to simultaneously or sequentially deliver multiple types of liquids. Dependent upon the application, long pipelines or tubing can range from several meters to several kilometers in length, composed of significant horizontal and vertical sections. Axial mixing is an important aspect of such flows of liquids in succession from a safety and reliability point of view. It is anticipated that mixing is due to turbulence and buoyancy, with the latter as a result of density differences of the mixing fluids. This paper sets out a numerical simulation model based on computational fluid dynamics (CFD) to fundamentally understand the mixing behavior of two miscible fluids under actual industrial-project-specific conditions. To benchmark its accuracy, the simulation model is first verified with respect to its numerical parameters using a short, 10 m pipe. Subsequently, a 100 m horizontal pipe is modeled, and we show that these results can be used to extrapolate longer length pipes. Finally, the sensitivity of mixing with respect to the Reynolds and Richardson numbers (characterizing buoyancy) has been investigated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".