Predicting Drag Reduction in Turbulent Pipe Flow With Relaxation Time of Polymer Additives
Bibliographic record
Abstract
This paper presents an experimental study on drag reduction induced by PEO (Polyethylene oxide) in a fully turbulent pipe flow. The objective of this work is to develop a correlation to predict drag reduction using the relaxation time of the polymer additives under dilute solution conditions, i.e., the polymer concentration is less than the overlap concertation. This paper discusses the meaning of relaxation time of polymers, and why the Weissenberg number, a dimensionless number that is related to the relaxation time and shear rate, is independent on the concentration in the dilute solution. Experimental data of drag reduction in a pipe flow are obtained from measurements using a flow loop. A correlation to predict drag reduction with the Weissenberg number and polymer concentration is established and a good agreement is shown between the predicted values and experimental data. The new correlation using the Weissenberg number and polymer concentration is shown to cost less to develop than one using the Reynolds number, in which larger pipes or higher flow rates are required.
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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".