Effect of Polymers on Transient Reynolds Number Change in Pipe Flow and Reduction of their Coefficient of Friction
Bibliographic record
Abstract
There are many ways to reduce the coefficient of friction as a result of pressure drop in internal flows and thrust force in external flows. For example, film suction, injection of gas bubbles in the boundary layer, use of magnetic fluid, etc., which are mostly intermediate fluids. Polymers are among the materials that can be used as intermediate fluids. Due to their ring structure and chain structure, polymer particles stretch and elongate when they are inside the stream. This stretching first absorbs energy from the fluid and does not allow this energy to be used to produce vortices. Second, stretching the polymer chain like a wall prevents the growth of vortices. The higher the molecular mass of the polymer, the greater the drop loss and the lower the critical concentration due to the heavier the polymer. As the concentration of polymer in water increases, the drop curve in terms of discharge towards the horizontal axis of Shifa and gets closer to it. In other words, the friction drops decreases. Percentage drop for 100gr per cubic meter of water is 4.54%, 200gr per cubic meter is 12.78%, 300gr per cubic meter is 27%, 400gr per cubic meter is 30.7% and 500gr per cubic meter is 39.4%, the maximum amount of reduction is.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".