An experimental study on the effect of gas injection configuration on flow characteristics in high viscosity oil columns
Bibliographic record
Abstract
Abstract Gas‐viscous liquid bubbly and slug flow are very common in petroleum, chemical, bioengineering, polymer, and food processing. However, there is a major knowledge gap in two‐phase flow research in the design of gas injectors/distributers in very high viscosity oil systems. The present study investigates the effect of gas injection methods in columns containing very high viscosity oils (i.e., realistic liquids), and more specifically using 360 Pa · s viscosity oil in a 240‐mm diameter column. The effects that the radial positioning, number of gas nozzles, and their distance from each other have on the structure of the flow in viscous liquids are presented in detail. Electrical capacitance tomography (ECT) is used to extract experimental data. Void fraction, bubble velocity, frequency, liquid film thickness, and bubble length were measured and analyzed at different radial gas injection positions. It has been observed that bubble length increases significantly by 0.3 m when the injection nozzle is located next to the wall of the pipe. Bubble velocity and length also increase by 0.217 m/s and 3.6 m, respectively, with increasing gas flowrate when multiple injection points are used. Increasing the distance between the gas injection points increased bubbles' length by 1.2 m. Bubbles' velocity and frequency (at higher gas flow rate) were also increased.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".