New equation of turbulent fibre suspensions and its solution and application to the pipe flow
Bibliographic record
Abstract
The mean motion equation of turbulent fibre suspensions and the equation of probability distribution function for mean fibre orientation are derived. The successive iteration for calculating the mean orientation distribution of fibres and the mean and fluctuation-correlated quantities of suspensions is presented. The equations and their solutions are applied to a turbulent pipe flow of fibre suspensions and a corresponding experiment is performed. It is found that the theoretical and experimental results are in good agreement with each other. The obtained results for turbulent pipe flow of fibre suspensions show that the flow rate of fibre suspensions is large under the same pressure drop in comparison with the rate of Newtonian flow in the absence of fibre suspensions. Fibres play an important role in reducing the flow drag. The amount of reduction in drag augments with the increase of the concentration of the fibre mass. The relative turbulent intensity and Reynolds stress in the fibre suspensions are smaller than those in the Newtonian flow under the same condition, which illustrates that the fibres have an influence on suppressing the turbulence. The amount of suppression is directly proportional to the concentration of the fibre mass.
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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".