Fibre orientation distribution in turbulent fibre suspensions flowing through an axisymmetric contraction
Bibliographic record
Abstract
Abstract The Reynolds averaged Navier–Stokes equation was solved numerically with the Reynolds stress model to get the mean fluid velocity and the turbulent kinetic energy in turbulent fibre suspensions flowing through an axisymmetric contraction. The fluctuating fluid velocity was represented as a Fourier series with random coefficients. Then the slender‐body theory was used to predict the fibre orientation distribution and orientation tensor. Some numerical results are compared with the experimental ones in the turbulent fibre suspensions flowing through a contraction with a rectangular cross‐section. The results show that the fibres with high aspect ratio tend to align its principal axis with the flow direction much easier. High contraction ratio makes the fibre alignment with the flow direction much easier. The contraction ratio has a strong effect on the fibre orientation distribution. Only a small part of the fibre is aligned with the flow direction in the inlet region, while most fibres are aligned with the flow direction when they approach to exit. The fibres are aligned with the flow direction rapidly in the inlet region, after that the fibre orientations change little in the most of the downstream region. The fibres with high aspect ratio are aligned with the flow direction faster when they enter the contraction. The randomising effect of the turbulence becomes significant in the downstream region because of the high turbulent intensity.
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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 | 0.000 |
| 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) | 0.001 | 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".