A study of axial and radial flows for annular channels with roughened walls
Bibliographic record
Abstract
The accurate prediction of pressure drop in production wells is very important to the petroleum industry. To decide if a reservoir is economically feasible, the underground reservoir's naturally occurring pressure should be properly determined. -- In the initial stages of production, most oil is produced by natural lift production methods. In older reservoirs, unless injection methods are employed, the underground pressure eventually declines and oil will no longer naturally flow to the surface. Artificial lift techniques must then be used to extract the oil from the reservoir. Flow through an annulus, or casing flow, is used with artificial lift techniques. Due to the importance of casing flow, predicting pressure drop in these circumstances has become quite important. -- Experiments were carried out using the multiphase flow loop at Memorial University of Newfoundland. Pressure differentials through varying sized annular channels with varying axial and radial flow rates were measured. For each of the three test sections incorporated into the design of the annulus, independent variables such as flow rate and pipe roughness were altered to study the effect each of these parameters would have on the pressure drop and hence the associated friction factors. -- The data was collected and experimental friction factors were calculated. These values were plotted as a function of Reynolds numbers and compared to friction factors found theoretically.
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 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 | 0.002 |
| 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 | 0.000 |
| 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".