Bibliographic record
Abstract
A linear stability analysis of the mixing layer in the presence of fibre additives is presented. Using a formulation based on moments of the probability distribution function to determine the particle orientation, we extend the classical linear stability theory and derive a modified Orr–Sommerfeld equation. It is found that, for large Reynolds numbers, the flow instability is governed by two parameters: a dimensionless group H , analogous to a reciprocal Reynolds number representing the importance of inertial forces versus viscous forces associated with the anisotropic elongational viscosity of the suspension; and a coefficient C I that accounts for inter-particle hydrodynamic interactions. A parametric study reveals that both parameters can induce an important attenuation of the flow instability. Furthermore, we show that the stabilizing effects arise from the orientation diffusion due to hydrodynamic interactions, and not from the anisotropy induced by the presence of fibres in the flow, as speculated before. The examination of profile contours of different perturbation terms and the analysis of the rate of production of enstrophy show clearly that the main factor behind the reduction of the flow instability is associated with the fibre shear stress disturbance. This disturbance acts as a dissipative term as the fibres, due to the orientational diffusivity arising from hydrodynamic interactions, deviate from the fully aligned anisotropic orientation. On the other hand, fibre normal stresses act as a destabilizing factor and are important only in the absence of hydrodynamic interactions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".